wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We consider a robust versionof this revenue-maximization pricing problem, where the seller knows the buyer's marginal distributions of values for each item  , but not the joint distribution, and wishes to maximize worst-case revenue over all possible correlation structures.
Revised:  We consider a robust versionof this revenue-maximization pricing problem, where the seller knows the buyer's marginal value distribution for each item , but not the joint distribution, and wishes to maximize worst-case revenue over all possible correlation structures.
Edit intent: clarity
Original:  We consider a robust versionof this revenue-maximization pricing problem, where the seller knows the buyer's marginal distributions of values for each item  , but not the joint distribution, and wishes to maximize worst-case revenue over all possible correlation structures.
Revised:  We consider a robust versionof this revenue-maximization pricing problem, where the seller knows the buyer's marginal distributions of values for each item separately , but not the joint distribution, and wishes to maximize worst-case revenue over all possible correlation structures.
Edit intent: meaning-changed
Original:  We consider a robust versionof this revenue-maximization pricing problem, where the seller knows the buyer's marginal distributions of values for each item  , but not the joint distribution, and wishes to maximize worst-case revenue over all possible correlation structures.
Revised:  We consider a robust versionof this revenue-maximization pricing problem, where the seller knows the buyer's marginal distributions of values for each item , but not the joint distribution, and prices the items to maximize revenue in the worst case over all compatible correlation structures.
Edit intent: clarity
Original:  I  show that intention-to-treat parameters aggregate a large number of direct and spillover effects for different compliance types, and hence they may not have a clear causal interpretation.
Revised:  I characterize the population compliance types in a setting in which spillovers can occur on both treatment take-up and outcomes, and provide conditions for identification of the marginal distribution of these compliance types. I show that intention-to-treat parameters aggregate a large number of direct and spillover effects for different compliance types, and hence they may not have a clear causal interpretation.
Edit intent: meaning-changed
Original:  I  show that intention-to-treat parameters aggregate a large number of direct and spillover effects for different compliance types, and hence they may not have a clear causal interpretation. I provide three alternative conditions to point identify average direct and spillover effects on specific subpopulations, by restricting either (i) the number of spillover effects , (ii) the degree of noncompliance, or (iii) the degree of heterogeneity in average causal parameters. I propose simple estimators that are consistent and asymptotically normal under mild conditions , and illustrate my results using data from an experiment that analyzes the effect of social interactions in the household on voting behavior.
Revised:  I show that intention-to-treat parameters aggregate a large number of direct and spillover effects for different compliance types, and hence they may not have a clear link to causally interpretable parameters. Moreover, rescaling ITT parameters by first-stage estimands generally recovers a weighted combination of average effects where the sum of weights is larger than one. I then analyze identification of causal direct and spillover effects under one-sided noncompliance, and propose simple estimators that are consistent and asymptotically normal under mild conditions , and illustrate my results using data from an experiment that analyzes the effect of social interactions in the household on voting behavior.
Edit intent: meaning-changed
Original:  I then analyze identification of causal direct and spillover effects under one-sided noncompliance, and propose simple estimators that are consistent and asymptotically normal under mild conditions. I use the proposed methods to analyze an experiment on social interactions and voting behavior.
Revised:  I then analyze identification of causal direct and spillover effects under one-sided noncompliance, and show that these effects can be estimated by 2SLS. I illustrate the proposed methods to analyze an experiment on social interactions and voting behavior.
Edit intent: meaning-changed
Original: Semi-supervised graph embedding methods represented by graph convolutional network has become one of the most popular methods for utilizing deep learning approaches to process the graph-based data for applications. Mostly existing work focus on designing novel algorithm structure to improve the performance, but ignore one common training problem, i.e., could these methods achieve the same performance with limited labelled data?
Revised: Graph convolution network (GCN) attracts intensive research interest with broad applications. While existing work mainly focused on designing novel algorithm structure to improve the performance, but ignore one common training problem, i.e., could these methods achieve the same performance with limited labelled data?
Edit intent: clarity
Original:  Mostly existing work focus on designing novel algorithm structure to improve the performance, but ignore one common training problem, i.e., could these methods achieve the same performance with limited labelled data? To tackle this research gap , we propose a sampling-based training framework for semi-supervised graph embedding methods to achieve better performance with smaller training data set.
Revised:  Mostly existing work focus on designing novel GCN architectures for better performance, few of them studied a practical yet challenging problem: How to learn GCNs from data with extremely limited annotation? In this paper , we propose a sampling-based training framework for semi-supervised graph embedding methods to achieve better performance with smaller training data set.
Edit intent: clarity
Original:  To tackle this research gap , we propose a sampling-based training framework for semi-supervised graph embedding methods to achieve better performance with smaller training data set. The key idea is to integrate the sampling theory and embedding methods by a pipeline form, which has the following advantages: 1) the sampled training data can maintain more accurate graph characteristics than uniformly chosen data, which eliminates the model deviation ;
Revised:  To tackle this research gap , we propose a new learning method by sampling strategy and model compression to overcome this challenge. Our approach has multifold advantages: 1) the sampled training data can maintain more accurate graph characteristics than uniformly chosen data, which eliminates the model deviation ;
Edit intent: meaning-changed
Original:  The key idea is to integrate the sampling theory and embedding methods by a pipeline form, which has the following advantages: 1) the sampled training data can maintain more accurate graph characteristics than uniformly chosen data, which eliminates the model deviation ;
Revised:  The key idea is to integrate the sampling theory and embedding methods by a pipeline form, which has the following advantages: 1) the adaptive sampling strategy largely suppresses the GCN training deviation over uniform sampling ;
Edit intent: clarity
Original:  2)  the smaller scale of training data is beneficial to reduce the human resource cost to label them ; The extensive experiments show that the sampling-based methodcan achieve the same performance only with 10 \\%DIF < -50\\% of the scale of training data.
Revised:  2) the smaller scale of training data is beneficial to reduce the human resource cost to label them . We choose six popular GCN baselines and conduct extensive experiments on three real-world datasets. The results show that by applying our method, all GCN baselines cut down the annotation requirement by as much as 90 \\%DIF < -50\\% of the scale of training data.
Edit intent: meaning-changed
Original:  We introduce a representation learning model based on word embeddings, convolutional neural networks  and autoencoders (i.e., "ConvAE" ) to transform patient trajectories into low-dimensional latent vectors.
Revised:  We introduce a representation learning model based on word embeddings, convolutional neural networks , and autoencoders (i.e., "ConvAE" ) to transform patient trajectories into low-dimensional latent vectors.
Edit intent: fluency
Original:  We introduce a representation learning model based on word embeddings, convolutional neural networks  and autoencoders (i.e., "ConvAE" ) to transform patient trajectories into low-dimensional latent vectors.
Revised:  We introduce a representation learning model based on word embeddings, convolutional neural networks and autoencoders (i.e., ConvAE ) to transform patient trajectories into low-dimensional latent vectors.
Edit intent: fluency
Original:  Conclusions: Patient representations derived from modeling EHRs with ConvAE can help develop personalized medicine therapeutic strategies and better understand varying etiologies in heterogeneous sub-populations  .
Revised:  Conclusions: Patient representations derived from modeling EHRs with ConvAE can help develop personalized medicine therapeutic strategies and better understand varying etiologies in heterogeneous sub-populations and unlock patterns for EHR-based research in the realm of personalized medicine .
Edit intent: meaning-changed
Original:  In this study, we propose an effective edge-based approach for promoting the spreading dynamics of the susceptible-infected-recovered model on complex networks by adding connections that do not exist in the original networks.
Revised:  In this study, we propose an effective edge-based approach for promoting the spreading dynamics of the susceptible-infected-recovered model on complex networks by adding latent edges (that is, edges that do not exist in the original networks.
Edit intent: meaning-changed
Original:  We verify the effectiveness of our strategy by extensive numerical simulations and prove that our strategy outperforms those approaches that only consider the network structure .
Revised:  We verify the effectiveness of our strategy by extensive numerical simulations and prove that our strategy outperforms certain static strategies, namely, adding the latent edges between nodes with the largest degree or eigenvector centrality. This study fills up the research blankness of promoting the SIR spreading dynamics by structural perturbations to some extent .
Edit intent: meaning-changed
Original:  In this study, we propose an effective edge-based strategy to promote the spreading dynamics of the susceptible-infected-recovered model on complex networks by adding latent edges (that is, edges that do not exist before) to the networks.
Revised:  In this study, we propose a strategy for promoting the spreading of the susceptible-infected-recovered model on complex networks by adding latent edges (that is, edges that do not exist before) to the networks.
Edit intent: clarity
Original:  In this study, we propose an effective edge-based strategy to promote the spreading dynamics of the susceptible-infected-recovered model on complex networks by adding latent edges (that is, edges that do not exist before) to the networks.
Revised:  In this study, we propose an effective edge-based strategy to promote the spreading dynamics of the susceptible-infected-recovered model , which is widely applied to describe these common spreading dynamics in real life. Specifically, we first quantify the potential influence that the addition of each latent edge (that is, edges that do not exist before) to the networks.
Edit intent: meaning-changed
Original:  In this study, we propose an effective edge-based strategy to promote the spreading dynamics of the susceptible-infected-recovered model on complex networks by adding latent edges (that is, edges that do not exist before) to the networks. Specifically, we first quantify the influence of each latent edge by a mathematical model.
Revised:  In this study, we propose an effective edge-based strategy to promote the spreading dynamics of the susceptible-infected-recovered model on complex networks by adding latent edges (that is, edges that do not exist before) could cause to the spreading dynamics.
Edit intent: clarity
Original:  The result of numerical simulations verifies the effectiveness of our strategy and demonstrates that our strategy outperforms certain static strategies, namely, adding the latent edges between nodes with the largest degree or eigenvector centrality.
Revised:  The result of numerical simulations verifies the effectiveness of our strategy and demonstrate that our strategy outperforms certain static strategies, namely, adding the latent edges between nodes with the largest degree or eigenvector centrality.
Edit intent: fluency
Original:  The result of numerical simulations verifies the effectiveness of our strategy and demonstrates that our strategy outperforms certain static strategies, namely, adding the latent edges between nodes with the largest degree or eigenvector centrality.
Revised:  The result of numerical simulations verifies the effectiveness of our strategy and demonstrates that our strategy outperforms several static strategies, namely, adding the latent edges between nodes with the largest degree or eigenvector centrality.
Edit intent: clarity
Original:  The paper presents a simple mathematical model of the process  and uses well-known results from control theory to prove that the approach taken by China and, to a slightly lesser extent, by Italy can work if the effect of delays is accounted for when taking the decision of the country lockdown, while the approach currently announced in the UK is likely to fail.
Revised:  The paper presents a suitable mathematical model of the process and uses well-known results from control theory to prove that the approach taken by China and, to a slightly lesser extent, by Italy can work if the effect of delays is accounted for when taking the decision of the country lockdown, while the approach currently announced in the UK is likely to fail.
Edit intent: clarity
Original:  The paper presents a simple mathematical model of the process  and uses well-known results from control theory to prove that the approach taken by China and, to a slightly lesser extent, by Italy can work if the effect of delays is accounted for when taking the decision of the country lockdown, while the approach currently announced in the UK is likely to fail.
Revised:  The paper presents a simple mathematical model of the process for feedback control analysis and uses well-known results from control theory to prove that the approach taken by China and, to a slightly lesser extent, by Italy can work if the effect of delays is accounted for when taking the decision of the country lockdown, while the approach currently announced in the UK is likely to fail.
Edit intent: meaning-changed
Original:  The paper presents a simple mathematical model of the process  and uses well-known results from control theory to prove that the approach taken by China and, to a slightly lesser extent, by Italy can work if the effect of delays is accounted for when taking the decision of the country lockdown, while the approach currently announced in the UK is likely to fail.
Revised:  The paper presents a simple mathematical model of the process and uses well-known results from control theory to prove that suppression strategies based on daily test reports can be effective if enacted very early, while mitigation strategies, including trying to achieve herd immunity, are likely to fail.
Edit intent: meaning-changed
Original:  The paper presents a suitable mathematical model of the process for feedback control analysis and uses well-known results from control theory to prove that suppression strategies based on daily test reports can be effective if enacted very early, while mitigation strategies, including trying to achieve herd immunity, are likely to fail.
Revised:  The paper presents a suitable mathematical model of the process for feedback control analysis and uses well-known results from control theory to prove that suppression strategies can be effective if enacted very early, while mitigation strategies, including trying to achieve herd immunity, are likely to fail.
Edit intent: coherence
Original: Short answer: not much, and only with lots of caution. The paper presents a suitable mathematical model of the process for feedback control analysisand uses well-known results from control theory to prove that suppression strategies can be effective if enacted very early , while mitigation strategies , including trying to achieve herd immunity, are likely to fail .
Revised: This paper studies if and to which extent COVID-19 epidemics can be controlled by authorities taking decisions on public health measures on the basis of daily reports of swab test results, active cases and total cases. A suitably simplified process model is derived to support the controllability analysis, highlighting the presence of very significant time delay; the model is validated with data from several outbreaks. The analysis shows that suppression strategies can be effective if enacted very early , while mitigation strategies , including trying to achieve herd immunity, are likely to fail .
Edit intent: meaning-changed
Original:  The paper presents a suitable mathematical model of the process for feedback control analysisand uses well-known results from control theory to prove that suppression strategies can be effective if enacted very early , while mitigation strategies , including trying to achieve herd immunity, are likely to fail .
Revised:  The paper presents a suitable mathematical model of the process for feedback control analysisand uses well-known results from control theory to prove that suppression strategies can be effective if strong enough and enacted early on. It also shows how mitigation strategies can fail because of the combination of delay, unstable dynamics, and uncertainty in the feedback loop; approximate conditions based on the theory of limitation of linear control are given for feedback control to be feasible .
Edit intent: meaning-changed
Original:  We demonstrate how schemes based on the move of single nodes between groups systematically fail at correctly sampling from the posterior distribution even on small networks, and how our merge-split approach behaves significantly better, and improves the mixing time of the  chain by several orders of magnitudes in typical cases.
Revised:  We demonstrate how schemes based on the move of single nodes between groups systematically fail at correctly sampling from the posterior distribution even on small networks, and how our merge-split approach behaves significantly better, and improves the mixing time of the Markov chain by several orders of magnitudes in typical cases.
Edit intent: meaning-changed
Original:  We demonstrate how schemes based on the move of single nodes between groups systematically fail at correctly sampling from the posterior distribution even on small networks, and how our merge-split approach behaves significantly better, and improves the mixing time of the  chain by several orders of magnitudes in typical cases.
Revised:  We demonstrate how schemes based on the move of single nodes between groups systematically fail at correctly sampling from the posterior distribution even on small networks, and how our merge-split approach behaves significantly better, and improves the mixing time of the chain by several orders of magnitude in typical cases.
Edit intent: fluency
Original:  We prove that this problem cannot be solved in  O((N \cdot t_{max})^{1-\epsilon}) time , for t_{max}=\max_i t_i and any \epsilon > 0, assuming the \forall \exists Strong Exponential Time Hypothesis (\forall \exists-SETH).
Revised:  We prove that this problem cannot be solved in time O((N \cdot t_{max})^{1-\epsilon}) time , for t_{max}=\max_i t_i and any \epsilon > 0, assuming the \forall \exists Strong Exponential Time Hypothesis (\forall \exists-SETH).
Edit intent: meaning-changed
Original:  We prove that this problem cannot be solved in  O((N \cdot t_{max})^{1-\epsilon}) time , for t_{max}=\max_i t_i and any \epsilon > 0, assuming the \forall \exists Strong Exponential Time Hypothesis (\forall \exists-SETH).
Revised:  We prove that this problem cannot be solved in O((N \cdot t_{max})^{1-\epsilon}) , for t_{max}=\max_i t_i and any \epsilon > 0, assuming the \forall \exists Strong Exponential Time Hypothesis (\forall \exists-SETH).
Edit intent: clarity
Original: Our main contribution lies in formulation of a generalized , parametric model for stochastic frontier analysis (SFA) that nests virtually all forms used so far in the field and includes certain special cases that have not been considered so far.
Revised: Contribution of this paper lies in the formulation and estimation of a generalized , parametric model for stochastic frontier analysis (SFA) that nests virtually all forms used so far in the field and includes certain special cases that have not been considered so far.
Edit intent: meaning-changed
Original: Our main contribution lies in formulation of a generalized , parametric model for stochastic frontier analysis (SFA) that nests virtually all forms used so far in the field and includes certain special cases that have not been considered so far.
Revised: Our main contribution lies in formulation of a generalized model for stochastic frontier analysis (SFA) that nests virtually all forms used so far in the field and includes certain special cases that have not been considered so far.
Edit intent: clarity
Original: Our main contribution lies in formulation of a generalized , parametric model for stochastic frontier analysis (SFA) that nests virtually all forms used so far in the field and includes certain special cases that have not been considered so far.
Revised: Our main contribution lies in formulation of a generalized , parametric model for stochastic frontier analysis (SFA) that nests virtually all forms used and includes some that have not been considered so far.
Edit intent: clarity
Original:  We use the general model framework for the purpose of formal testing and comparison of alternative specifications, which provides a way to deal with model uncertainty - the main issue of SFA that has not been resolved so far. SFA dates back to Aigner et al. (1977) and Meeusen and van den Broeck (1977), and relies upon the idea of compound error specification with at least two error terms, one representing the observation error and the other interpreted as some form of inefficiency. The models considered here are based on the generalized t distribution for the observation error and the generalized beta distribution of the second kind for the inefficiency-related term.
Revised:  The model is based on the generalized t distribution for the observation error and the generalized beta distribution of the second kind for the inefficiency-related term.
Edit intent: clarity
Original:  Hence, it is possible to relax a number of various potentially restrictive assumptions embedded in modelsused so far .
Revised:  We use this general error structure framework for formal testing, to compare alternative specifications and to conduct model averaging. This allows us to deal with model specification uncertainty, which is one of the main unresolved issues in SFA, and to relax a number of various potentially restrictive assumptions embedded in modelsused so far .
Edit intent: meaning-changed