wanyu/IteraTeR_v2 huggingface dataset:
Original:  Furthermore, we demonstrate theperformance of update operations of the physical storage modelswhen the system is implemented in two state-of-the-art databasemanagement systems, HBase and Postgres.
Revised:  Furthermore, we demonstrate the performance of update operations of the physical storage modelswhen the system is implemented in two state-of-the-art databasemanagement systems, HBase and Postgres.
Edit intent: fluency
Original:  Furthermore, we demonstrate theperformance of update operations of the physical storage modelswhen the system is implemented in two state-of-the-art databasemanagement systems, HBase and Postgres.
Revised:  Furthermore, we demonstrate theperformance of update operations of the physical storage models when the system is implemented in two state-of-the-art databasemanagement systems, HBase and Postgres.
Edit intent: fluency
Original:  Furthermore, we demonstrate theperformance of update operations of the physical storage modelswhen the system is implemented in two state-of-the-art databasemanagement systems, HBase and Postgres.
Revised:  Furthermore, we demonstrate theperformance of update operations of the physical storage modelswhen the system is implemented in two state-of-the-art database management systems, HBase and Postgres.
Edit intent: fluency
Original: 5}+ n^2d\right) by Cherapanamjeri el al. (COLT '19) and matching the concurrent work by Depersin and Lecu\'e .
Revised: 5}+ n^2d\right) by Cherapanamjeri el al. (COLT '19) .
Edit intent: coherence
Original: In typical collective decision-making scenarios, rank aggregation aims to combine different agents' preferences over the given alternatives into an aggregated ranking that agrees the most with all the preferences.
Revised: In typical collective decision-making scenarios, rank aggregation aims to combine different agents' preferences over the given alternatives into an aggregate ranking that agrees the most with all the preferences.
Edit intent: fluency
Original:  We propose an effective and efficient protocol LDP-KwikSort , with the appealing property that each agent only needs to answer a small number of pairwise comparison queries from the untrusted curator with controllable noise, and the aggregated ranking could maintain an acceptable utility compared with that of the non-private protocol .
Revised:  We propose an effective and efficient protocol LDP-KwikSort .
Edit intent: coherence
Original:  Theoretical and empirical results demonstrate that the proposed solution can achieve the practical trade-off between the utility of aggregated ranking and the privacy protection of agents' pairwise preferences.
Revised:  Theoretical and empirical results demonstrate that the proposed solution can achieve the practical trade-off between the utility of aggregate ranking and the privacy protection of agents' pairwise preferences.
Edit intent: fluency
Original: In typical collective decision-making scenarios , rank aggregation aims to combine different agents'  preferences over the given alternatives into an aggregate ranking that agrees the most with all the preferences.
Revised: In typical collective decision-making scenarios , rank aggregation aims to combine different agents' preferences over the given alternatives into an aggregate ranking which agrees the most with all the preferences.
Edit intent: fluency
Original: In typical collective decision-making scenarios , rank aggregation aims to combine different agents'  preferences over the given alternatives into an aggregate ranking that agrees the most with all the preferences.
Revised: In typical collective decision-making scenarios , rank aggregation aims to combine different agents' preferences over the given alternatives into an aggregate ranking that agrees the most with the preferences.
Edit intent: clarity
Original:  All existing works that guarantee differential privacy in rank aggregation  assume that the data curator is trusted.
Revised:  Existing works that guarantee differential privacy in rank aggregation assume that the data curator is trusted.
Edit intent: fluency
Original:  All existing works that guarantee differential privacy in rank aggregation  assume that the data curator is trusted.
Revised:  All existing works that guarantee differential privacy in rank aggregation all assume that the data curator is trusted.
Edit intent: clarity
Original:  In this paper, we first formulate and address the problem of locally differentially private rank aggregation, in which the agents have no trust in the data curator.
Revised:  In this paper, we formulate and address the problem of locally differentially private rank aggregation, in which the agents have no trust in the data curator.
Edit intent: coherence
Original:  By leveraging the approximate rank aggregation algorithm KwikSort, the Randomized Response technique and the Laplace mechanism, we propose an effective and efficient protocol LDP-KwikSort.
Revised:  By leveraging the approximate rank aggregation algorithm KwikSort, the Randomized Response mechanism, and the Laplace mechanism, we propose an effective and efficient protocol LDP-KwikSort.
Edit intent: clarity
Original:  Theoretical and empirical results demonstrate that the solution LDP-KwikSort:RR can achieve the practical trade-off between the utility of aggregate ranking and the privacy protection of agents' pairwise preferences.
Revised:  Theoretical and empirical results show that the solution LDP-KwikSort:RR can achieve the practical trade-off between the utility of aggregate ranking and the privacy protection of agents' pairwise preferences.
Edit intent: fluency
Original:  Theoretical and empirical results demonstrate that the solution LDP-KwikSort:RR can achieve the practical trade-off between the utility of aggregate ranking and the privacy protection of agents' pairwise preferences.
Revised:  Theoretical and empirical results demonstrate that the solution LDP-KwikSort:RR can achieve the acceptable trade-off between the utility of aggregate ranking and the privacy protection of agents' pairwise preferences.
Edit intent: clarity
Original: For the general class of residual distribution (RD) schemes, including many finite element (such as continuous/discontinuous Galerkin) and flux reconstruction methods, an approach to construct entropy conservative semidiscretisations by adding suitable correction terms has been proposed recently by Abgrall (J. Comp.
Revised: For the general class of residual distribution (RD) schemes, including many finite element (such as continuous/discontinuous Galerkin) and flux reconstruction methods, an approach to construct entropy conservative semidiscretisations by adding suitable correction terms has been proposed by Abgrall (J. Comp.
Edit intent: clarity
Original:  Here, these correction terms are characterised as solutions of certain optimisation problems and adapted to discontinuous element based schemes such as discontinuous Galerkin and (multi-block) finite difference methods.
Revised:  In this work, the correction terms are characterised as solutions of certain optimisation problems and adapted to discontinuous element based schemes such as discontinuous Galerkin and (multi-block) finite difference methods.
Edit intent: clarity
Original:  Here, these correction terms are characterised as solutions of certain optimisation problems and adapted to discontinuous element based schemes such as discontinuous Galerkin and (multi-block) finite difference methods.
Revised:  Here, these correction terms are characterized as solutions of certain optimisation problems and adapted to discontinuous element based schemes such as discontinuous Galerkin and (multi-block) finite difference methods.
Edit intent: fluency
Original:  Novel generalisations to entropy inequalities, multiple constraints, and kinetic energy preservation for the Euler equations are developed and tested in numerical experiments.
Revised:  Novel generalizations to entropy inequalities, multiple constraints, and kinetic energy preservation for the Euler equations are developed and tested in numerical experiments.
Edit intent: fluency
Original:  Finally, the underlying idea to use optimisation problems is applied to grid refinement and coarsening operators, resulting in entropy stable /dissipative grid transfers .
Revised:  For all of these optimization problems, explicit solutions are provided. Additionally, the correction approach is applied for the first time to obtain a fully discrete entropy conservative /dissipative grid transfers .
Edit intent: coherence
Original: For the general class of residual distribution (RD) schemes, including many finite element (such as continuous/discontinuous Galerkin) and flux reconstruction methods, an approach to construct entropy conservative/ dissipative semidiscretizations by adding suitable correction terms has been proposed by Abgrall (J. Comp.
Revised: For the general class of residual distribution (RD) schemes, including many finite element (such as continuous/discontinuous Galerkin) and flux reconstruction methods, an approach to construct entropy conservative/ dissipative semidiscretizations by adding suitable correction terms has been proposed by Abgrall (J. ~Comp.~
Edit intent: fluency
Original:  Phys. 372: pp. 640-666 , 2018).
Revised:  Phys. 372: pp. 640--666 , 2018).
Edit intent: fluency
Original:  However, the specific features of clonal growth are not taken into account in prototypical models to study vegetation patterns.
Revised:  However, the spatial colonization mechanisms of clonal plants, driven by the growth of a rhizome, are usually not considered in prototypical models to study vegetation patterns.
Edit intent: clarity
Original:  Here we propose a generic model that includes all main clonal-growth features and reproduces the phase diagram of a fully detailed model  .
Revised:  Here we propose a generic model that includes all main clonal-growth features and reproduces the phase diagram of a fully detailed model of clonal growth .
Edit intent: clarity
Original: Many real-world prediction tasks have outcome (a.k.a.~target or response) variables that have characteristic heavy-tail distributions.
Revised: Many real-world prediction tasks have outcome variables that have characteristic heavy-tail distributions.
Edit intent: clarity
Original:  By learning heavy-tailed distributions, `` big and rare '' instances (e.g., the best-sellers) will have accurate predictions.
Revised:  By learning heavy-tailed distributions, " big and rare '' instances (e.g., the best-sellers) will have accurate predictions.
Edit intent: fluency
Original:  By learning heavy-tailed distributions, `` big and rare '' instances (e.g., the best-sellers) will have accurate predictions.
Revised:  By learning heavy-tailed distributions, `` big and rare " instances (e.g., the best-sellers) will have accurate predictions.
Edit intent: fluency
Original:  To tackle this problem, we introduce Learning to Place (L2P ), which exploits the pairwise relationships between instances to learn from a proportionally higher number of rare instances.
Revised:  To tackle this problem, we introduce (L2P ), which exploits the pairwise relationships between instances to learn from a proportionally higher number of rare instances.
Edit intent: coherence
Original:  To tackle this problem, we introduce Learning to Place (L2P ), which exploits the pairwise relationships between instances to learn from a proportionally higher number of rare instances.
Revised:  To tackle this problem, we introduce Learning to Place L2P ), which exploits the pairwise relationships between instances to learn from a proportionally higher number of rare instances.
Edit intent: clarity
Original:  To tackle this problem, we introduce Learning to Place (L2P ), which exploits the pairwise relationships between instances to learn from a proportionally higher number of rare instances.
Revised:  To tackle this problem, we introduce Learning to Place (Learning to Place (L2P ), which exploits the pairwise relationships between instances to learn from a proportionally higher number of rare instances.
Edit intent: fluency
Original:  To tackle this problem, we introduce Learning to Place (L2P ), which exploits the pairwise relationships between instances to learn from a proportionally higher number of rare instances.
Revised:  To tackle this problem, we introduce Learning to Place (L2P ), which exploits the pairwise relationships between instances 
Edit intent: clarity
Original: L2P consists of two stages.
Revised:  consists of two stages.
Edit intent: clarity
Original:  In Stage 1,L2P learns a pairwise preference classifier: is instance A > instance B? . In Stage 2,L2P learns to place a new instance into an ordinal ranking of known instances.
Revised:  In Stage 1,L2P learns a pairwise preference classifier: . In Stage 2,L2P learns to place a new instance into an ordinal ranking of known instances.
Edit intent: coherence
Original:  In Stage 1,L2P learns a pairwise preference classifier: is instance A > instance B? . In Stage 2,L2P learns to place a new instance into an ordinal ranking of known instances.
Revised:  In Stage 1,L2P learns a pairwise preference classifier: is instance A > instance B? L2P learns to place a new instance into an ordinal ranking of known instances.
Edit intent: coherence
Original:  In Stage 1,L2P learns a pairwise preference classifier: is instance A > instance B? . In Stage 2,L2P learns to place a new instance into an ordinal ranking of known instances.
Revised:  In Stage 1,L2P learns a pairwise preference classifier: is instance A > instance B? . In Stage 2, learns to place a new instance into an ordinal ranking of known instances.
Edit intent: fluency
Original:  Experiments on real data show that L2P outperforms competing approaches in terms of accuracy and capability to reproduce heavy-tailed outcome distribution.
Revised:  Experiments on real data show that L2P outperforms competing approaches in terms of accuracy and capability to reproduce heavy-tailed outcome distribution.
Edit intent: fluency
Original:  Experiments on real data show that L2P outperforms competing approaches in terms of accuracy and capability to reproduce heavy-tailed outcome distribution.
Revised:  Experiments on real data show that L2P outperforms competing approaches in terms of accuracy and ability to reproduce heavy-tailed outcome distribution.
Edit intent: clarity
Original:  In addition, \texttt{L2P can provide an interpretable model with explainable outcomes by placing each predicted instance in context with its comparable neighbors  .
Revised:  In addition, \texttt{L2P L2P provides an interpretable model with explainable outcomes by placing each predicted instance in context with its comparable neighbors .
Edit intent: clarity
Original:  In addition, \texttt{L2P can provide an interpretable model with explainable outcomes by placing each predicted instance in context with its comparable neighbors  .
Revised:  In addition, \texttt{L2P can provide an interpretable model by placing each predicted instance in context with its comparable neighbors .
Edit intent: clarity
Original:  In addition, \texttt{L2P can provide an interpretable model with explainable outcomes by placing each predicted instance in context with its comparable neighbors  .
Revised:  In addition, \texttt{L2P can provide an interpretable model with explainable outcomes by placing each predicted instance in relation to its comparable neighbors .
Edit intent: clarity
Original: Modularity based community detection encompasses a number of widely used, efficient heuristics for identification of structure in single- and multilayer networks.
Revised: Modularity based community detection encompasses a number of widely used, efficient heuristics for identification of structure in networks.
Edit intent: clarity
Original:  We show that  the resolution parameter affects the convergence properties of the algorithm and yields different community structures than the baseline.
Revised:  We show that adjusting the resolution parameter affects the convergence properties of the algorithm and yields different community structures than the baseline.
Edit intent: clarity
Original:  Finally, we apply multilayer modularity belief propagation to two real-world multilayer networks and discuss practical concerns in implementing our method, which we have released as a Python package for general use.
Revised:  Finally, we apply multilayer modularity belief propagation to two real-world multilayer networks and discuss practical concerns in implementing our method, which we have released as a Python package for convenient use.
Edit intent: clarity
Original:  Hence a previous solution of this problem, known as posterior repartitioning (PR), redefines the prior and likelihood while keeping their product fixed, so that the posterior inferences and evidence estimates remain unchanged, but the efficiency of the NS process is significantly increased.
Revised:  Hence our previous solution to this problem, known as posterior repartitioning (PR), redefines the prior and likelihood while keeping their product fixed, so that the posterior inferences and evidence estimates remain unchanged, but the efficiency of the NS process is significantly increased.
Edit intent: fluency
Original:  We present here an alternative Bayesian `automated PR' method , in which \beta is instead treated as a hyperparameter that is inferred from the data alongside the original parameters of the problem, and then marginalised over to obtain the final inference.
Revised:  Here we present a very simple yet powerful alternative Bayesian approach , in which \beta is instead treated as a hyperparameter that is inferred from the data alongside the original parameters of the problem, and then marginalised over to obtain the final inference.
Edit intent: clarity
Original:  We show through numerical examples that this approach provides a robust and efficient `hands-off' solution to addressing the issue of unrepresentative priors in Bayesian inference using NS. Moreover,  we show that for problems with representative priors the method has a negligible computational overhead relative to standard nesting sampling, which suggests that it should be used in as a matter of course in all NS analyses.
Revised:  We show through numerical examples that this approach provides a robust and efficient `hands-off' solution to the problem of unrepresentative priors in Bayesian inference using NS. Moreover, we show that for problems with representative priors the method has a negligible computational overhead relative to standard nesting sampling, which suggests that it should be used in as a matter of course in all NS analyses.
Edit intent: clarity
Original:  We show through numerical examples that this approach provides a robust and efficient `hands-off' solution to addressing the issue of unrepresentative priors in Bayesian inference using NS. Moreover,  we show that for problems with representative priors the method has a negligible computational overhead relative to standard nesting sampling, which suggests that it should be used in as a matter of course in all NS analyses.
Revised:  We show through numerical examples that this approach provides a robust and efficient `hands-off' solution to addressing the issue of unrepresentative priors in Bayesian inference using NS. Moreover, we show that even for representative priors BPR has a negligible computational overhead relative to standard nesting sampling, which suggests that it should be used in as a matter of course in all NS analyses.
Edit intent: clarity
Original:  We show through numerical examples that this approach provides a robust and efficient `hands-off' solution to addressing the issue of unrepresentative priors in Bayesian inference using NS. Moreover,  we show that for problems with representative priors the method has a negligible computational overhead relative to standard nesting sampling, which suggests that it should be used in as a matter of course in all NS analyses.
Revised:  We show through numerical examples that this approach provides a robust and efficient `hands-off' solution to addressing the issue of unrepresentative priors in Bayesian inference using NS. Moreover, we show that for problems with representative priors the method has a negligible computational overhead relative to standard nesting sampling, which suggests that it should be used as the default in all NS analyses.
Edit intent: clarity
Original:  Identifying and using a performance-optimal setting in feature-rich frameworks, however, involves a non-trivial amount of performance characterization and domain-specific knowledge.
Revised:  Identifying and using a performance-optimal setting in feature-rich frameworks, however, involves a non-trivial amount of performance profiling efforts and often relies on domain-specific knowledge.
Edit intent: clarity
Original:  We  prove that there is a physical interpretation of the procedure and demonstrate its capabilities by applying the method to  benchmark data sets of molecular dynamics simulation .
Revised:  We prove that there is a physical interpretation of the procedure and demonstrate its capabilities by applying the method to several benchmark data sets of molecular dynamics simulation .
Edit intent: clarity
Original:  We  prove that there is a physical interpretation of the procedure and demonstrate its capabilities by applying the method to  benchmark data sets of molecular dynamics simulation .
Revised:  We prove that there is a physical interpretation of the procedure and demonstrate its capabilities by applying the method to benchmark data sets .
Edit intent: clarity
Original: Recent years have seen rapid advances in the data-driven analysis of dynamical systems based on Koopman operator theory -- with extended dynamic mode decomposition (EDMD) being a cornerstone of the field .
Revised: Recent years have seen rapid advances in the data-driven analysis of dynamical systems based on Koopman operator theory and related approaches .
Edit intent: clarity
Original:  In this work, we combine EDMD and the TT format, enabling the application of EDMD to high-dimensional problems in conjunction with a large set of features.
Revised:  In this work, we combine EDMD and the TT format, enabling their application to high-dimensional problems in conjunction with a large set of features.
Edit intent: clarity
Original:  We prove that there is a physical interpretation of the procedure and demonstrate its capabilities by applying the method to several benchmark data sets.
Revised:  We establish the infinite-data limit of these matrix representations, and demonstrate our methods' capabilities using several benchmark data sets.
Edit intent: clarity
Original:  The best CNN classified a separated set containing 2,250 segments with 99.9\% overall accuracy.
Revised:  The best CNN classified a separate set containing 2,250 segments with 99.9\% overall accuracy.
Edit intent: fluency
Original:  They are  the problem with  point source on a whole plane, the problem of diffraction by a half-plane, and the problem of diffraction by a right-angled wedge .
Revised:  They are the problem with a point source on a whole plane, the problem of diffraction by a half-plane, and the problem of diffraction by a right-angled wedge .
Edit intent: fluency
Original:  It is showed that total field is represented as an integral from an algebraic function on a manifold.
Revised:  It is shown that total field is represented as an integral from an algebraic function on a manifold.
Edit intent: fluency
Original:  It is showed that total field is represented as an integral from an algebraic function on a manifold.
Revised:  It is showed that total field can be represented as an integral from an algebraic function on a manifold.
Edit intent: clarity
Original:  It is showed that total field is represented as an integral from an algebraic function on a manifold.
Revised:  It is showed that total field is represented as an integral of an algebraic function on a manifold.
Edit intent: fluency