wanyu/IteraTeR_v2 huggingface dataset:
Original: We study a class of type isomorphisms arising from a well-known correspondence between the Yoneda lemma of category theoryand the polymorphic lambda-calculus.
Revised: In this paper we explore a family of type isomorphisms arising from a well-known correspondence between the Yoneda lemma of category theoryand the polymorphic lambda-calculus.
Edit intent: clarity
Original:  Currently, this does not result in improved query times so that it remains an open question whether these edge hierarchies can lead to overall improved performance.
Revised:  Currently, this rarely implies improved query times so that it remains an open question whether these edge hierarchies can lead to overall improved performance.
Edit intent: clarity
Original:  Currently, this does not result in improved query times so that it remains an open question whether these edge hierarchies can lead to overall improved performance.
Revised:  Currently, this does not result in improved query times so that it remains an open question whether edge hierarchies can lead to overall improved performance.
Edit intent: clarity
Original:  Currently, this does not result in improved query times so that it remains an open question whether these edge hierarchies can lead to overall improved performance.
Revised:  Currently, this does not result in improved query times so that it remains an open question whether these edge hierarchies can lead to consistently improved performance.
Edit intent: clarity
Original: In this paper we propose P4-IPsec which follows the software-defined networking (SDN) paradigm.
Revised: In this work, we present P4-IPsec which follows the software-defined networking (SDN) paradigm.
Edit intent: clarity
Original: In this paper we propose P4-IPsec which follows the software-defined networking (SDN) paradigm.
Revised: In this paper we propose P4-IPsec which follows the software-defined networks (SDN) paradigm.
Edit intent: fluency
Original:  To validate the P4-based approach for IPsec gateways, we provide three prototypes leveraging the software switch BMv2, the  NetFPGA SUME card, and the Edgecore Wedge 100BF-32X switch as P4 targets.
Revised:  To validate the P4-based approach for IPsec gateways, we provide three prototypes leveraging the software switch BMv2, the NetFPGA SUME board. For the Edgecore Wedge 100BF-32X switch as P4 targets.
Edit intent: clarity
Original:  We review recent advances in linking ( empirical and simulated ) brain network organization with cognitive information processing.
Revised:  We review recent advances in linking empirical and simulated ) brain network organization with cognitive information processing.
Edit intent: fluency
Original:  We review recent advances in linking ( empirical and simulated ) brain network organization with cognitive information processing.
Revised:  We review recent advances in linking ( empirical and simulated brain network organization with cognitive information processing.
Edit intent: fluency
Original:  These models utilize connectivity to specify the transfer of information via neural activity flow processes, successfully predicting the formation of cognitive representations (e.g., face selectivity in the fusiform face area) .
Revised:  These models utilize connectivity to specify the transfer of information via neural activity flow processes, successfully predicting the formation of cognitive representations in empirical neural data .
Edit intent: clarity
Original: We present highly scalable parallel distributed-memory algorithms and associated data structures for a generic finite element framework that supports h-adaptivity on computational domains represented as multiple connected adaptive trees, thus providing multi-scale resolution on problems governed by partial differential equations .
Revised: In this work we formally derive and prove the correctness of the algorithms and data structures in a parallel, distributed-memory, generic finite element framework that supports h-adaptivity on computational domains represented as multiple connected adaptive trees, thus providing multi-scale resolution on problems governed by partial differential equations .
Edit intent: clarity
Original: We present highly scalable parallel distributed-memory algorithms and associated data structures for a generic finite element framework that supports h-adaptivity on computational domains represented as multiple connected adaptive trees, thus providing multi-scale resolution on problems governed by partial differential equations .
Revised: We present highly scalable parallel distributed-memory algorithms and associated data structures for a generic finite element framework that supports h-adaptivity on computational domains represented as forest-of-trees .
Edit intent: clarity
Original:  Along the way, we have identified the requirements that the forest-of-trees layer must fulfill to be coupled into our framework.
Revised:  Along the way, we have identified the requirements it must fulfill to be coupled into our framework.
Edit intent: clarity
Original:  Essentially, it must be able to describe neighboring relationships between cells in the adapted mesh (apart from hierarchical relationships) across the lower-dimensional objects at the boundary of the cells.
Edit intent: coherence
Original:  A comprehensive strong scaling study of this implementation when applied to Poisson and Maxwell problems reveals remarkable scalability up to 32.
Revised:  A strong scaling study of this implementation when applied to Poisson and Maxwell problems reveals remarkable scalability up to 32.
Edit intent: clarity
Original:  Besides, the implementation in FEMPAR of the proposed approach is up to 2.6 and 3.4 times faster than the state-of-the-art deal.ii finite element software  in the h-adaptive approximation of a Poisson problem with first- and second-order Lagrangian finite elements, respectively (excluding the linear solver step from the comparison) .
Revised:  Besides, a comparative performance study of FEMPAR and the state-of-the-art deal.ii finite element software in the h-adaptive approximation of a Poisson problem with first- and second-order Lagrangian finite elements, respectively (excluding the linear solver step from the comparison) .
Edit intent: clarity
Original:  Besides, the implementation in FEMPAR of the proposed approach is up to 2.6 and 3.4 times faster than the state-of-the-art deal.ii finite element software  in the h-adaptive approximation of a Poisson problem with first- and second-order Lagrangian finite elements, respectively (excluding the linear solver step from the comparison) .
Revised:  Besides, the implementation in FEMPAR of the proposed approach is up to 2.6 and 3.4 times faster than the state-of-the-art deal.ii finite element software in the h-adaptive approximation of a Poisson problem with first- and second-order Lagrangian finite elements, respectively .
Edit intent: coherence
Original:  The new results concern(}\Bbb  the endpoint cases  ] ]  .
Revised:  The new results concern(}\Bbb the endpoint cases ], ] .
Edit intent: fluency
Original:  In this paper we extend the concept of neural networks to general  groups and prove that neural networks with a single hidden layer and a bounded non-constant activation function can approximate any L ^p function defined over a locally compact Abelian group.
Revised:  In this paper we extend the definition of neural networks to general groups and prove that neural networks with a single hidden layer and a bounded non-constant activation function can approximate any L ^p function defined over a locally compact Abelian group.
Edit intent: clarity
Original:  In this paper we extend the concept of neural networks to general  groups and prove that neural networks with a single hidden layer and a bounded non-constant activation function can approximate any L ^p function defined over a locally compact Abelian group.
Revised:  In this paper we extend the concept of neural networks to general groups and prove that neural networks with a single hidden layer and a bounded non-constant activation function can approximate any L ^p function defined over any locally compact Abelian group.
Edit intent: fluency
Original:  Our work also obtains as special cases the recent theorems of Qi et al.
Revised:  Our work obtains as special cases the recent theorems of Qi et al.
Edit intent: clarity
Original:  [2019], Keriven and Peyre [2019], and Maron et al. [2019] .
Revised:  [2019], Keriven and Peyre [2019], and Maron et al. [2019] 
Edit intent: fluency
Original:  This study, based on spatial refinement, is supported by a truncation error analysis.
Edit intent: coherence
Original:  In the context of object recognition, for example, a robot should be able to learn (without forgetting) objects of never  seen classes as well as improving its recognition capabilities as new instances of already known classes are discovered.
Revised:  In the context of object recognition, for example, a robot should be able to learn (without forgetting) objects of never before seen classes as well as improving its recognition capabilities as new instances of already known classes are discovered.
Edit intent: clarity
Original:  In this paper, we introduce a novel fine-grained continual learning protocol based on the CORe50 benchmark and propose two rehearsal-free continual learning techniques, CWR* and AR1*, that can learn effectively even in the challenging case of nearly 400 small non-i.i.d. incremental batches.
Revised:  In this paper, we introduce a novel continual learning protocol based on the CORe50 benchmark and propose two rehearsal-free continual learning techniques, CWR* and AR1*, that can learn effectively even in the challenging case of nearly 400 small non-i.i.d. incremental batches.
Edit intent: clarity
Original: For a generalized Hodge--Laplace equation, we prove the quasi-optimal convergence rate of an adaptive mixed finite element method controlling the error in the natural mixed variational norm  .
Revised: For a generalized Hodge Laplace equation, we prove the quasi-optimal convergence rate of an adaptive mixed finite element method controlling the error in the natural mixed variational norm .
Edit intent: fluency
Original: For a generalized Hodge--Laplace equation, we prove the quasi-optimal convergence rate of an adaptive mixed finite element method controlling the error in the natural mixed variational norm  .
Revised: For a generalized Hodge--Laplace equation, we prove the quasi-optimal convergence rate of an adaptive mixed finite element method . This adaptive method can control the error in the natural mixed variational norm .
Edit intent: coherence
Original:  Comparing to existing adaptive mixed methods, the new methods control errors in both two variables.
Revised:  Comparing to existing adaptive mixed methods, the new methods control errors in both variables.
Edit intent: fluency
Original: Bayesian approach  as a useful tool for quantifying uncertainties  has been widely used for solving inverse problems of partial differential equations (PDEs).
Revised: Bayesian approach , as a useful tool for quantifying uncertainties has been widely used for solving inverse problems of partial differential equations (PDEs).
Edit intent: fluency
Original: Bayesian approach  as a useful tool for quantifying uncertainties  has been widely used for solving inverse problems of partial differential equations (PDEs).
Revised: Bayesian approach as a useful tool for quantifying uncertainties , has been widely used for solving inverse problems of partial differential equations (PDEs).
Edit intent: fluency
Original:  Variational Bayes' method (VBM) is firstly and broadly studied in the field of machine learning, which has the ability to extract posterior information approximately by using much lower computational resources compared with the sampling type method .
Revised:  Variational Bayes' method (VBM) is firstly and broadly studied in the field of machine learning, which has the ability to extract posterior information approximately by using much lower computational resources compared with the conventional sampling type methods .
Edit intent: clarity
Original:  General infinite-dimensional mean-field approximate theory has been established, and has been applied to abstract linear inverse problems with Gaussian and Laplace noise assumptions.
Revised:  General infinite-dimensional mean-field approximate theory , and apply this theory to abstract linear inverse problems with Gaussian and Laplace noise assumptions.
Edit intent: clarity
Original:  Finally, some numerical examples are given which illustrate the effectiveness of the proposed approach.
Revised:  The results on some numerical examples are given which illustrate the effectiveness of the proposed approach.
Edit intent: clarity
Original:  Finally, some numerical examples are given which illustrate the effectiveness of the proposed approach.
Revised:  Finally, some numerical examples substantiate the effectiveness of the proposed approach.
Edit intent: clarity
Original: The connectivity of a network conveys information about the dependencies between nodes .
Revised: The connectivity of a network contains information about the dependencies between nodes .
Edit intent: clarity
Original: The connectivity of a network conveys information about the dependencies between nodes .
Revised: The connectivity of a network conveys information about the relationships between nodes, which can denote interactions, associations, or dependencies .
Edit intent: clarity
Original:  Networks with higher effective information contain more information within the dependencies between nodes.
Revised:  Networks with higher effective information contain more information in the relationships between nodes.
Edit intent: fluency
Original:  We show how subgraphs of nodes can be grouped into macro-nodes, reducing the size of a network while increasing its effective information , a phenomenon known as causal emergence  .
Revised:  We show how subgraphs of nodes can be grouped into macro-nodes, reducing the size of a network while increasing its effective information ( a phenomenon known as causal emergence .
Edit intent: fluency
Original:  We show how subgraphs of nodes can be grouped into macro-nodes, reducing the size of a network while increasing its effective information , a phenomenon known as causal emergence  .
Revised:  We show how subgraphs of nodes can be grouped into macro-nodes, reducing the size of a network while increasing its effective information , a phenomenon known as causal emergence ) .
Edit intent: fluency
Original:  Ultimately, these results show that the emergence of higher scales in networks can be directly assessed , and that these higher scales offer a way to create certainty out of uncertainty.
Revised:  These results show that the emergence of higher scales in networks can be directly assessed , and that these higher scales offer a way to create certainty out of uncertainty.
Edit intent: coherence
Original:  Ultimately, these results show that the emergence of higher scales in networks can be directly assessed , and that these higher scales offer a way to create certainty out of uncertainty.
Revised:  Ultimately, these results show that the emergence of higher scales in networks can be directly assessed and that these higher scales offer a way to create certainty out of uncertainty.
Edit intent: fluency
Original:   However, these methods lack modeling dependencies of the constructed methods .
Revised:  However, these methods lack modeling dependencies of the discovered networks .
Edit intent: clarity
Original:  In this study as alternative to these conventional methods , we model dependencies of the network via correlated sparsity patterns.
Revised:  In this study an alternative to these conventional methods , we model dependencies of the network via correlated sparsity patterns.
Edit intent: fluency
Original:  In this study as alternative to these conventional methods , we model dependencies of the network via correlated sparsity patterns.
Revised:  In this study as alternative to these conventional methods is presented in which dependencies of the network via correlated sparsity patterns.
Edit intent: coherence
Original:  In previous numerical studies, we have evaluated the energy dissipation of basic AQFP logic gates and demonstrated sub-kBT switching energy, where kB is  Boltzmann's constant and T is the temperature, by integrating the product of the excitation current and voltage associated with the gates over time.
Revised:  In previous numerical studies, we have evaluated the energy dissipation of basic AQFP logic gates and demonstrated sub-kBT switching energy, where kB is the Boltzmann's constant and T is the temperature, by integrating the product of the excitation current and voltage associated with the gates over time.
Edit intent: fluency
Original:  We also present negative results that include formalizing the concept of a stochasticity gap for LP upper bounds on these problems, showing some new stochasticity gaps for popular LPs, and bounding the worst-case performance of some greedy approaches .
Revised:  Finally, we present negative results that include formalizing the concept of a stochasticity gap for LP upper bounds on these problems, showing some new stochasticity gaps for popular LPs, and bounding the worst-case performance of some greedy approaches .
Edit intent: clarity
Original:  We also present negative results that include formalizing the concept of a stochasticity gap for LP upper bounds on these problems, showing some new stochasticity gaps for popular LPs, and bounding the worst-case performance of some greedy approaches .
Revised:  We also present negative results that include formalizing the concept of a stochasticity gap for LP upper bounds on these problems, bounding the worst-case performance of some greedy approaches .
Edit intent: clarity
Original: We present an efficient and realistic geometric sound simulation approach for generating and augmenting training data in speech-related machine learning tasks.
Revised: We present an efficient and realistic geometric acoustic simulation approach for generating and augmenting training data in speech-related machine learning tasks.
Edit intent: clarity
Original:  Our physically based acoustic simulation method is capable of modeling occlusion, specular and diffuse reflections of sound in complicated acoustic environments, whereas the classical image method can only model specular reflections in simple room settings.
Revised:  Our physically-based acoustic simulation method is capable of modeling occlusion, specular and diffuse reflections of sound in complicated acoustic environments, whereas the classical image method can only model specular reflections in simple room settings.
Edit intent: fluency
Original:  We show that by using our synthetic training data, the same models gain significant performance improvement on real test sets in both speech recognition and keyword spotting tasks, without fine tuning using any real data .
Revised:  We show that by using our synthetic training data, the same neural networks gain significant performance improvement on real test sets in both speech recognition and keyword spotting tasks, without fine tuning using any real data .
Edit intent: clarity
Original:  Photometric consistency is achieved with respect to the photometric intensities given in a reference image that is retained during refinement . Globally blurred imagery and local geometric inconsistencies due to, e.g. , motion are detected and removed prior to image fusion.
Revised:  Photometric consistency is achieved by retaining the photometric intensities given in a reference image that is retained during refinement . Globally blurred imagery and local geometric inconsistencies due to, e.g. , motion are detected and removed prior to image fusion.
Edit intent: clarity
Original:  Photometric consistency is achieved with respect to the photometric intensities given in a reference image that is retained during refinement . Globally blurred imagery and local geometric inconsistencies due to, e.g. , motion are detected and removed prior to image fusion.
Revised:  Photometric consistency is achieved with respect to the photometric intensities given in a reference image , while it is being refined . Globally blurred imagery and local geometric inconsistencies due to, e.g. , motion are detected and removed prior to image fusion.
Edit intent: clarity
Original:  Photometric consistency is achieved with respect to the photometric intensities given in a reference image that is retained during refinement . Globally blurred imagery and local geometric inconsistencies due to, e.g. , motion are detected and removed prior to image fusion.
Revised:  Photometric consistency is achieved with respect to the photometric intensities given in a reference image that is retained during refinement . Globally blurred imagery and local geometric inconsistencies due to, e.g. motion are detected and removed prior to image fusion.
Edit intent: fluency
Original:  We demonstrate the quality and robustness of our approach using several image and video sequences, including hand-held acquisition with mobile phones and zooming sequences with consumer cameras.
Revised:  We demonstrate the quality and robustness of our approach using several image and video sequences, including handheld acquisition with mobile phones and zooming sequences with consumer cameras.
Edit intent: fluency
Original: Recently developed scalar auxiliary variable (SAV) approach has been proven to be a very efficient and powerful way to construct energy stable schemes for gradient flows.
Edit intent: coherence
Original:  In this paper, we consider a deformed scalar auxiliary variable (DSAV) approach to obtain energy stable schemes for gradient flows.
Revised:  In this paper, we consider a novel auxiliary variable method to obtain energy stable schemes for gradient flows.
Edit intent: clarity
Original:  It means that the DSAV approach does not need the assumption that E_1(\phi) is bounded from below which is essential in SAV approach . A comparative study of classical SAV and DSAV approaches is considered to show the accuracy and efficiency.
Revised:  It means that the DSAV approach does not need the assumption that E_1(\phi) is bounded from below which is essential in SAV approach . A comparative study of classical SAV and NAEV approaches is considered to show the accuracy and efficiency.
Edit intent: clarity
Original:  The hardness  for Max-2-Sat applies to monotone Max-2-Sat instances, meaning that we also obtain tight inapproximability for the Max-k-Vertex-Cover problem.
Revised:  The hardness obtained for Max-2-Sat applies to monotone Max-2-Sat instances, meaning that we also obtain tight inapproximability for the Max-k-Vertex-Cover problem.
Edit intent: clarity
Original: For a nonnegative matrix, the eigenvalue with the maximum magnitude or Perron-Frobenius root exists and is unique if the matrix is primitive .
Revised: Following the Perron-Frobenius root exists and is unique if the matrix is primitive .
Edit intent: clarity
Original:  It is shown that for a primitive matrix A, there exists a positive rank one matrix X allowing to have the Hadamard product B = A \circ X, where the row (column) sums of matrix B are the same and equal to the Perron-Frobenius root.
Revised:  It is shown that for a primitive matrix A, there is a positive rank one matrix X allowing to have the Hadamard product B = A \circ X, where the row (column) sums of matrix B are the same and equal to the Perron-Frobenius root.
Edit intent: clarity
Original:  It is shown that for a primitive matrix A, there exists a positive rank one matrix X allowing to have the Hadamard product B = A \circ X, where the row (column) sums of matrix B are the same and equal to the Perron-Frobenius root.
Revised:  It is shown that for a primitive matrix A, there exists a positive rank one matrix X such that B = A \circ X, where the row (column) sums of matrix B are the same and equal to the Perron-Frobenius root.
Edit intent: clarity
Original:  A by product of the proposed algorithm is a new method for calculating the first eigenvector.
Revised:  A byproduct of the proposed algorithm is a new method for calculating the first eigenvector.
Edit intent: fluency