wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We then use an inductive "in-fill and out-expansion" algorithm to establish our main results, and consider extensions to more general settings that allow nonseparable dependence between homophily and degree heterogeneity  .
Revised:  We then use an inductive "in-fill and out-expansion" algorithm to establish our main results, and consider extensions to more general settings that allow nonseparable dependence between homophily and degree heterogeneity , as well as certain extents of network sparsity and weaker assumptions on the support of unobserved heterogeneity. As a byproduct, we also propose a concept called "modeling equivalence" as a refinement of "observational equivalence", and use it to provide a formal discussion about normalization, identification and their interplay with counterfactuals .
Edit intent: meaning-changed
Original: Within the framework of Kahneman and Tversky's cumulative prospective theory  , this paper considers a continuous-time behavioral portfolio selection model, which includes both running and terminal terms in the objective functional.
Revised: Within the framework of Kahneman and Tversky's cumulative prospective theory of Kahneman and Tversky , this paper considers a continuous-time behavioral portfolio selection model, which includes both running and terminal terms in the objective functional.
Edit intent: meaning-changed
Original:  In this paper we apply for the first time these techniques to the family of polynomial processes, by exploiting , whenever possible, their peculiar properties. We derive theoretical results to assess the  approximation errors, and we describe in numerical examples practical tools for fast  exotic option pricing.
Revised:  In this paper we apply for the first time these techniques to the family of polynomial processes, by exploiting their peculiar nature. We focus our analysis on the stochastic volatility Jacobi process, by presenting two alternative quantization procedures: the first is a new discretization technique, whose foundation lies on the polynomial structure of the underlying process and which is suitable for vanilla option pricing, the second is based on recursive marginal quantization and it allows for pricing of (vanilla and) exotic derivatives. We prove theoretical results to assess the approximation errors, and we describe in numerical examples practical tools for fast exotic option pricing.
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Original: We introduce an ASEP-like model to describe the effects of RNA polymerase (RNAP) traffic on DNAreplication .
Revised: We introduce an ASEP-like model to describe the effects of RNA polymerase (RNAP) motor traffic, engaged in the transcription of a segment of DNA, concomitant with the progress of two DNA replication forks on the same DNA segment. One of the species of particles (P) represents RNAP motors while the other (R) represents replication forks. Motivated by the biological phenomena that this model is intended to capture, a maximum of only two R particles are allowed to enter the lattice from two opposite ends whereas the unrestricted number of P particles constitute a totally asymmetric simple exclusion process (TASEP) in a segment in the middle of the lattice. Consequently, the lattice consists of three segments; the encounters of the P particles with the R particles are confined within the middle segment (segment 2) whereas only the R particles can occupy the sites in the segments 1 and 3. The model captures three distinct pathways for resolving the co-directional as well as head-collision between the P and R particles .
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Original:  Using Monte Carlo simulation and an heuristic analytical approach based on exact results for the ASEP we make predictions for the RNAP traffic that might be tested in future experiments .
Revised:  Using Monte Carlo simulation and an heuristic analytical approach based on exact results for the TASEP with mean-field approximations, we predict the possible outcomes of the conflict between the traffic of RNAP motors (P particles engaged in transcription) and the replication forks (R particles). The outcomes, of course, depend on the dynamical phase of the TASEP of P particles. In principle, the model can be adapted to the experimental conditions to account for the data quantitatively .
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Original:  An alternative approach to solving the problem is to develop techniques for computing the exact stretch factor of "easier" types of Delaunay triangulations, in particular those defined using regular-polygons instead of a circle.
Revised:  An alternative approach to solving the problem is to develop techniques for computing the exact stretch factor of ``easier'' types of Delaunay triangulations, in particular those defined using regular-polygons instead of a circle.
Edit intent: fluency
Original:  Achieving this running time requires proving several new structural properties of the MAP. Our algorithm actually computes all the locally maximal area parallelograms (LMAPs). In addition to the algorithm , we prove that the LMAPs interleave each other, thus  the number of LMAPs is bounded by O(n).
Revised:  Achieving this running time requires proving several new structural properties of the LMAPs. As an additional result , we prove that the LMAPs interleave each other, thus the number of LMAPs is bounded by O(n).
Edit intent: coherence
Original:  We discuss applications of our result to, among others, the problem of computing the maximum area centrally-symmetric convex body inside a convex polygon, and the simplest case of the Heilbronn Triangle Problem .
Revised:  We discuss applications of our result to, among others, the problem of computing the maximum area centrally-symmetric convex body inside a convex polygon, and the simplest case of the Heilbronn triangle problem .
Edit intent: fluency
Original:  In this paper, we consider the variant of finding the maximum area parallelograms (MAPs) and all the locally maximal area parallelograms (LMAPs) inside a given convex polygon.
Revised:  In this paper, we consider the variant of computing the maximum area parallelograms (MAPs) and all the locally maximal area parallelograms (LMAPs) inside a given convex polygon.
Edit intent: clarity
Original:  In this paper, we consider the variant of finding the maximum area parallelograms (MAPs) and all the locally maximal area parallelograms (LMAPs) inside a given convex polygon.
Revised:  In this paper, we consider the variant of finding the maximum area parallelograms (MAPs) and all the locally maximal area parallelograms (LMAPs) in a given convex polygon.
Edit intent: clarity
Original:  In the partially linear model setting popular in causal inference, we use Stein's lemma to show that we can construct second-order orthogonal moments if and only if the treatment residual is not normally distributed  .
Revised:  In the partially linear regression setting popular in causal inference, we use Stein's lemma to show that we can construct second-order orthogonal moments if and only if the treatment residual is not normally distributed .
Edit intent: clarity
Original:  In the partially linear model setting popular in causal inference, we use Stein's lemma to show that we can construct second-order orthogonal moments if and only if the treatment residual is not normally distributed  .
Revised:  In the partially linear model setting popular in causal inference, we use Stein's lemma to show that we can construct second-order orthogonal moments if and only if the treatment residual is not normally distributed . Our proof relies on Stein's lemma and may be of independent interest .
Edit intent: meaning-changed
Original: It has been recently established that the volatility of financial assets is rough. This means that the behavior of the log-volatility process is similar to that of a fractional Brownian motion with Hurst parameter around 0.1.
Revised: Rough volatility models are becoming increasingly popular in quantitative finance. In this framework, one considers that the behavior of the log-volatility process is similar to that of a fractional Brownian motion with Hurst parameter around 0.1.
Edit intent: clarity
Original:  Furthermore, the approximant is shown to be smooth and of approximation order of O (h^{m+1}) for non-noisy data, where h is the mesh size with respect to the manifold domain, and m is the degree of a local polynomial approximation utilized in our algorithm.
Revised:  Furthermore, the approximant is shown to be smooth and of approximation order of \mathcal{O (h^{m+1}) for non-noisy data, where h is the mesh size with respect to the manifold domain, and m is the degree of a local polynomial approximation utilized in our algorithm.
Edit intent: meaning-changed
Original:  Thus, in case of extremely large ambient space dimension, we are able to avoid the curse of dimensionality without having to perform non-linear dimension reduction, which inevitably introduces distortions to the manifold data.
Revised:  Thus, in case of extremely large ambient space dimension, we are able to avoid the curse of dimensionality without having to perform non-linear dimension reduction, which introduces distortions to the manifold data.
Edit intent: clarity
Original:  The approximation scheme is based upon the Manifold Moving Least-Squares (MMLS) . The proposed algorithm is resistant to noise in both the domain and function values. Furthermore, the approximant is shown to be smooth and of approximation order of O(h^{m+1}) for non-noisy data , where h is the mesh size with respect to the manifold domain, and m is the degree of a local polynomial approximation utilized in our algorithm.
Revised:  The approximation scheme is based upon the Manifold Moving Least-Squares approach of (Sober and Levin 2016) to reconstruct the atlas of charts and the approximation is built on-top of those charts. The resulting approximant is shown to be smooth and of approximation order of O(h^{m+1}) for non-noisy data , where h is the mesh size with respect to the manifold domain, and m is the degree of a local polynomial approximation utilized in our algorithm.
Edit intent: meaning-changed
Original:  Furthermore, the approximant is shown to be smooth and of approximation order of O(h^{m+1}) for non-noisy data , where h is the mesh size with respect to the manifold domain, and m is the degree of a local polynomial approximation utilized in our algorithm.
Revised:  Furthermore, the approximant is shown to be a function defined over a neighborhood of a manifold, approximating the originally sampled manifold. In other words, given a new point, located near the manifold, the approximation can be evaluated directly on that point. We prove that our construction yields a smooth function, and in case of noiseless samples the approximation order is O(h^{m+1}) for non-noisy data , where h is the mesh size with respect to the manifold domain, and m is the degree of a local polynomial approximation utilized in our algorithm.
Edit intent: meaning-changed
Original:  Furthermore, the approximant is shown to be smooth and of approximation order of O(h^{m+1}) for non-noisy data , where h is the mesh size with respect to the manifold domain, and m is the degree of a local polynomial approximation utilized in our algorithm.
Revised:  Furthermore, the approximant is shown to be smooth and of approximation order of O(h^{m+1}) , where h is the mesh size with respect to the manifold domain, and m is the degree of a local polynomial approximation utilized in our algorithm.
Edit intent: clarity
Original:  Furthermore, the approximant is shown to be smooth and of approximation order of O(h^{m+1}) for non-noisy data , where h is the mesh size with respect to the manifold domain, and m is the degree of a local polynomial approximation utilized in our algorithm.
Revised:  Furthermore, the approximant is shown to be smooth and of approximation order of O(h^{m+1}) for non-noisy data , where h is a local density of sample parameter (i.e., the fill distance) and m is the degree of a local polynomial approximation utilized in our algorithm.
Edit intent: meaning-changed
Original:  Furthermore, the approximant is shown to be smooth and of approximation order of O(h^{m+1}) for non-noisy data , where h is the mesh size with respect to the manifold domain, and m is the degree of a local polynomial approximation utilized in our algorithm.
Revised:  Furthermore, the approximant is shown to be smooth and of approximation order of O(h^{m+1}) for non-noisy data , where h is the mesh size with respect to the manifold domain, and m is the degree of a local polynomial approximation , used in our algorithm.
Edit intent: clarity
Original:  Thus, in case of extremely large ambient space dimension, we are able to avoid the curse of dimensionality without having to perform non-linear dimension reduction, which  introduces distortions to the manifold data.
Revised:  Thus, in case of extremely large ambient space dimension, we are able to avoid the computational complexity, commonly encountered in high dimensional approximations, without having to perform non-linear dimension reduction, which introduces distortions to the manifold data.
Edit intent: meaning-changed
Original:  Thus, in case of extremely large ambient space dimension, we are able to avoid the curse of dimensionality without having to perform non-linear dimension reduction, which  introduces distortions to the manifold data.
Revised:  Thus, in case of extremely large ambient space dimension, we are able to avoid the curse of dimensionality without having to perform non-linear dimension reduction, which inevitably introduces distortions to the manifold data.
Edit intent: clarity
Original:  Thus, in case of extremely large ambient space dimension, we are able to avoid the curse of dimensionality without having to perform non-linear dimension reduction, which  introduces distortions to the manifold data. Using numerical experiments, we compare the presented method to state-of-the-art algorithms for regression over manifolds and show its potential.
Revised:  Thus, in case of extremely large ambient space dimension, we are able to avoid the curse of dimensionality without having to perform non-linear dimension reduction, which introduces distortions to the geometry of the data. Additionaly, we show numerical experiments that the proposed approach compares favorably to statistical approaches for regression over manifolds and show its potential.
Edit intent: clarity
Original: This paper studies identification of program effects in settings with latent choice sets. Here, by latent choice sets, I mean the unobserved heterogeneity that arises when the choice set from which the agent selects treatment is heterogeneous and unobserved by the researcher . The analysis is developed in the context of the Head Start Impact Study, a social experiment designed to evaluate preschools as part of Head Start, the largest early childhood education program in the United States .
Revised: In a common experimental format, individuals are randomly assigned to either a treatment group with program access or a control group without access. In such experiments, analyzing the average effects of the treatment of program access may be hindered by the largest early childhood education program in the United States .
Edit intent: clarity
Original:  The analysis is developed in the context of the Head Start Impact Study, a social experiment designed to evaluate preschools as part of Head Start, the largest early childhood education program in the United States .
Revised:  The analysis is developed in the context of the Head Start Impact Study, a social experiment designed to evaluate preschools as part of Head Start, the problem that some control individuals do not comply with their assigned status and receive program access from outside the experiment. Available methods to account for such a problem typically require the researcher to observe the receipt of program access for every individual. However, in many experiments, this is not the case as data is not collected on where any individual received access .
Edit intent: meaning-changed
Original:  In this setting, resource constraints limit preschool slots to only a few eligible children through an assignment mechanism that is not observed in the data, which in turn introduces unobserved heterogeneity in the child's choice set of care settings .
Revised:  In this paper, I develop a framework to show how data on only each individual's treatment assignment status, program participation decision and outcome can be exploited to learn about the average effects of program access .
Edit intent: clarity
Original:  I propose a nonparametric model that explicitly accounts for latent choice sets in the care setting enrollment decision . In this model, I study various parameters that evaluate Head Start in terms of policies that mandate enrollment and also those that allow voluntary enrollment into Head Start. I show that the identified set for these parameters given the information provided by the study and by various institutional details of the setting can be constructed using a linear programming method.
Revised:  I propose a nonparametric model that explicitly accounts for latent choice sets to relate where access was received to the treatment assignment status, participation decision and outcome, and a linear programming procedure to compute the identified set for these parameters given the information provided by the study and by various institutional details of the setting can be constructed using a linear programming method.
Edit intent: meaning-changed
Original:  I show that the identified set for these parameters given the information provided by the study and by various institutional details of the setting can be constructed using a linear programming method. Applying the developed analysis, I find that a significant proportion of children voluntarily enroll into Head Start if provided access and that Head Start is effective in terms of improving short-term test scoresacross multiple policy dimensions .
Revised:  I show that the identified set for parameters evaluating the average effects of program access in this model. I illustrate the framework by analyzing the average effects of Head Start preschool access using the Head Start Impact Study. I find that a significant proportion of children voluntarily enroll into Head Start if provided access and that Head Start is effective in terms of improving short-term test scoresacross multiple policy dimensions .
Edit intent: meaning-changed
Original:  Applying the developed analysis, I find that a significant proportion of children voluntarily enroll into Head Start if provided access and that Head Start is effective in terms of improving short-term test scoresacross multiple policy dimensions .
Revised:  Applying the developed analysis, 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 .
Edit intent: meaning-changed
Original: In a common experimental format, individuals are randomly assigned to either a treatment group with access to a program or a control group without access. In such experiments, analyzing the average effects of the treatment of program access may be hindered by the problem that some control individuals do not comply with their assigned status and receive program access from outside the experiment.
Revised: In a common experimental format, individuals are randomly assigned to either a treatment group with access to a program or a control group without such access. I study what we can learn about the average effects of the treatment of program access may be hindered by the problem that some control individuals do not comply with their assigned status and receive program access from outside the experiment.
Edit intent: clarity
Original:  In such experiments, analyzing the average effects of the treatment of program access may be hindered by the problem that some control individuals do not comply with their assigned status and receive program access from outside the experiment. Available tools to account for such a problem typically require the researcher to observe the receipt of program access for every individual.
Revised:  In such experiments, analyzing the average effects of the treatment of program access may be hindered by the problem that some control individuals do not comply with their assigned status and when the data may only provide partial information on the receipt of program access for every individual.
Edit intent: clarity
Original:  Available tools to account for such a problem typically require the researcher to observe the receipt of program access for every individual. However, in many experiments, this is not the case as data is not collected on where any individual received access. In this paper, I develop a framework to show how data on only each individual's treatment assignment status, program participation decision and outcome can be exploited to learn about the average effects of program access .
Revised:  Available tools to account for such a problem typically require the researcher to observe the receipt of program access across individuals. I propose a new nonparametric multiple treatment selection model that provides a general setup to define a range of parameters evaluating the effects of program access .
Edit intent: clarity
Original:  In this paper, I develop a framework to show how data on only each individual's treatment assignment status, program participation decision and outcome can be exploited to learn about the average effects of program access . I propose a nonparametric selection model with latent choice sets to relate where access was received to the treatment assignment status, participation decision and outcome, and a linear programming procedure to compute the identified set for parameters evaluating the average effects of program access in this model.
Revised:  In this paper, I develop a framework to show how data on only each individual's treatment assignment status, program participation decision and outcome can be exploited to learn about the average effects of program access and to exploit the partial information the data may provide on where access was received to the treatment assignment status, participation decision and outcome, and a linear programming procedure to compute the identified set for parameters evaluating the average effects of program access in this model.
Edit intent: meaning-changed
Original:  I propose a nonparametric selection model with latent choice sets to relate where access was received to the treatment assignment status, participation decision and outcome, and a linear programming procedure to compute the identified set for parameters evaluating the average effects of program access in this model.
Revised:  I propose a nonparametric selection model with latent choice sets to relate where access was received . I illustrate how a computational procedure can be used to learn about these parameters in this model.
Edit intent: clarity
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 can negatively depend on whether access to an alternative preschool .
Edit intent: meaning-changed