wanyu/IteraTeR_v2 huggingface dataset:
Original:  SC has  serious drawbacks that are the significant increase in the computational complexity derived from the eigendecomposition and the memory space complexities to store the similarity matrix.
Revised:  SC has serious drawbacks that are the significant increase in the computational complexity derived from the eigendecomposition and the memory space complexity to store the similarity matrix.
Edit intent: fluency
Original:  The similarity matrix used by ASC with GNG is made from both the reference vectors and the topology of the network generated by GNG. Using the network generated from a dataset by GNG, we achieve to reduce the computational and space complexities and to improve clustering quality.
Revised:  ASC with GNG is made from both the reference vectors and the topology of the network generated by GNG. Using the network generated from a dataset by GNG, we achieve to reduce the computational and space complexities and to improve clustering quality.
Edit intent: clarity
Original:  The similarity matrix used by ASC with GNG is made from both the reference vectors and the topology of the network generated by GNG. Using the network generated from a dataset by GNG, we achieve to reduce the computational and space complexities and to improve clustering quality.
Revised:  The similarity matrix used by ASC with GNG makes the similarity matrix from both the reference vectors and the topology of the network generated by GNG. Using the network generated from a dataset by GNG, we achieve to reduce the computational and space complexities and to improve clustering quality.
Edit intent: clarity
Original:  The similarity matrix used by ASC with GNG is made from both the reference vectors and the topology of the network generated by GNG. Using the network generated from a dataset by GNG, we achieve to reduce the computational and space complexities and to improve clustering quality.
Revised:  The similarity matrix used by ASC with GNG is made from both the reference vectors and the topology of the network generated by GNG. Using the network generated from a dataset by GNG, ASC with GNG achieves to reduce the computational and space complexities and to improve clustering quality.
Edit intent: coherence
Original:  The similarity matrix used by ASC with GNG is made from both the reference vectors and the topology of the network generated by GNG. Using the network generated from a dataset by GNG, we achieve to reduce the computational and space complexities and to improve clustering quality.
Revised:  The similarity matrix used by ASC with GNG is made from both the reference vectors and the topology of the network generated by GNG. Using the network generated from a dataset by GNG, we achieve to reduce the computational and space complexities and improve clustering quality.
Edit intent: fluency
Original:  Moreover, the results of this study show that the proposed method displays equal to or better performance of clustering than SC.
Revised:  Moreover, the results of this study show that the proposed method displays equal to or better clustering performance than SC.
Edit intent: clarity
Original:  SC has the serious drawbacksthat are the significant increases in the time complexity derived from the computation of eigenvectors and the memory space complexity to store the similarity matrix.
Revised:  SC has serious drawbacks: the significant increases in the time complexity derived from the computation of eigenvectors and the memory space complexity to store the similarity matrix.
Edit intent: coherence
Original:  ASC with GNG makes the similarity matrix from both the reference vectors and the topology of the network generated by GNG. Using the network generated from a dataset by GNG, ASC with GNG achieves to reduce the computational and space complexities and improve clustering quality.
Revised:  ASC with GNG calculates the similarity matrix from both the reference vectors and the topology of the network generated by GNG. Using the network generated from a dataset by GNG, ASC with GNG achieves to reduce the computational and space complexities and improve clustering quality.
Edit intent: clarity
Original:  In this study, I demonstrate that ASC with GNG effectively reduces  computational time.
Revised:  In this study, I demonstrate that ASC with GNG effectively reduces the computational time.
Edit intent: fluency
Original:  Moreover, this study shows that ASC with GNG displays equal to or better clustering performance than SC.
Revised:  Moreover, this study shows that ASC with GNG provides equal to or better clustering performance than SC.
Edit intent: clarity
Original:  Despite their success in capturing complex distributions, existing generative models face three challenges when used for design problems: 1) generated designs have limited design space coverage, 2) the generator ignores design performance, and 3)  the new parameterization is unable to represent designs beyond training data.
Revised:  Despite their success in capturing complex distributions, existing generative models face three challenges when used for design problems: 1) generated designs have limited design space coverage, 2) the generator ignores design performance, and 3) ~ the new parameterization is unable to represent designs beyond training data.
Edit intent: fluency
Original:  Our analysis focuses on how close and how fast the incentive scheme, which endogenously emerges from the agent-based model, converges to the second-best solution proposed by the standard hidden-action model.
Revised:  Our analysis focuses on how close and how fast the incentive scheme, which endogenously emerges from the agent-based model, converges to the solution proposed by the standard hidden-action model.
Edit intent: clarity
Original:  Besides, we are allowed to use discontinuous weight functions for developing other efficient numerical algorithms .
Revised:  Besides, the discontinuous PU weight functions can now be utilized to develop the method in a more efficient and less expensive way .
Edit intent: clarity
Original:  Query expansion methods can suffer from introducing non-relevant information when expanding the query , however .
Revised:  However, query expansion methods can suffer from introducing non-relevant information when expanding the query , however .
Edit intent: coherence
Original:  Query expansion methods can suffer from introducing non-relevant information when expanding the query , however .
Revised:  Query expansion methods can suffer from introducing non-relevant information when expanding the query .
Edit intent: coherence
Original:  In evaluations on the standard TREC Robust04 and GOV2 test collections, the proposed BERT-QE model significantly outperforms BERT-Large models commonly used for document retrieval .
Revised:  In evaluations on the standard TREC Robust04 and GOV2 test collections, the proposed BERT-QE model significantly outperforms BERT-Large models .
Edit intent: clarity
Original:  To examine this challenge and find solutions, the Montreal AI Ethics Institute (MAIEI) collaborated with the Partnership on AI in May 2020 to host two public consultation meetups.
Revised:  To examine this challenge and find solutions, the Montreal AI Ethics Institute (MAIEI) co-hosted two public consultations with the Partnership on AI in May 2020 to host two public consultation meetups.
Edit intent: clarity
Original:  To examine this challenge and find solutions, the Montreal AI Ethics Institute (MAIEI) collaborated with the Partnership on AI in May 2020 to host two public consultation meetups.
Revised:  To examine this challenge and find solutions, the Montreal AI Ethics Institute (MAIEI) collaborated with the Partnership on AI in May 2020.
Edit intent: coherence
Original:  A^+b\| \leq \varepsilon\|A^+b\| in \mathcal{O} %DIFDELCMD < \left(%%% \frac{\|A\|_{\mathrm{F}^6\|A\|^2}{\sigma^8\varepsilon^4}}%DIFDELCMD < \right) %DIFDELCMD < %%% \Big(}^6\|A\|^2}{\sigma^8\varepsilon^4}}\Big) time, improving on previous "quantum-inspired" algorithms in this line of research by a factor of \|A\|^{14}{\sigma^{14}\varepsilon^2} [Chia et al., STOC'20].
Revised:  A^+b\| \leq \varepsilon\|A^+b\| in \mathcal{O} %DIFDELCMD < \left(%%% \frac{\|A\|_{\mathrm{F}^6\|A\|^2}{\sigma^8\varepsilon^4}}%DIFDELCMD < \right) %DIFDELCMD < %%% \Big(\frac{\|A\|_{\mathrm{F}^6\|A\|^2}{\sigma^8\varepsilon^4}}\Big) time, improving on previous "quantum-inspired" algorithms in this line of research by a factor of \|A\|^{14}{\sigma^{14}\varepsilon^2} [Chia et al., STOC'20].
Edit intent: coherence
Original:  The online evaluation system stays open and is available at the project website: bop.felk.cvut.cz.
Revised:  The online evaluation system stays open and is available on the project website: bop.felk.cvut.cz.
Edit intent: fluency
Original: Often---for example in war games, strategy video games, and financial simulations---the game is given to us only as a black-box simulator in which we can play it.
Revised: Often -- for example in war games, strategy video games, and financial simulations---the game is given to us only as a black-box simulator in which we can play it.
Edit intent: fluency
Original: Often---for example in war games, strategy video games, and financial simulations---the game is given to us only as a black-box simulator in which we can play it.
Revised: Often---for example in war games, strategy video games, and financial simulations -- the game is given to us only as a black-box simulator in which we can play it.
Edit intent: fluency
Original:  In particular, one of the main uses of behavior prediction in autonomous systems is to inform ego-robot motion planning and control.
Revised:  In particular, one of the main uses of behavior prediction in autonomous systems is to inform robot motion planning and control.
Edit intent: clarity
Original:  Conventional debiasing research mainly studies from the view of data representation, e.g. balancing data distribution or learning unbiased models and representations, ignoring the mechanism that how humans accomplish this task.
Revised:  Conventional debiasing research mainly studies from the view of balancing data distribution or learning unbiased models and representations, ignoring the mechanism that how humans accomplish this task.
Edit intent: clarity
Original:  Then, we propose a hierarchical loss specially for this cognitive structure, which supports coarse-to-fine distinction for the correct relationships while progressively eliminating the interference of irrelevant ones .
Revised:  Then, we propose a hierarchical loss specially for this cognitive structure, which supports coarse-to-fine distinction for the correct relationships .
Edit intent: clarity
Original: Despite the effectiveness of utilizing BERT for document ranking, the  computational cost of such approaches is non-negligible when compared to other retrieval methods .
Revised: Despite the effectiveness of utilizing the BERT model for document ranking, the computational cost of such approaches is non-negligible when compared to other retrieval methods .
Edit intent: clarity
Original: Despite the effectiveness of utilizing BERT for document ranking, the  computational cost of such approaches is non-negligible when compared to other retrieval methods .
Revised: Despite the effectiveness of utilizing BERT for document ranking, the computational cost of such approaches limits their uses .
Edit intent: clarity
Original:  To this end, this paper first empirically investigates the applications of knowledge distillation models on  document ranking task.
Revised:  To this end, this paper first empirically investigates the applications of knowledge distillation models on the document ranking task.
Edit intent: fluency
Original:  In addition, on top of the recent TinyBERT , two simplifications are proposed.
Revised:  In addition, on top of the recently proposed TinyBERT model , two simplifications are proposed.
Edit intent: clarity
Original:  This paper presents a  perturbation analysis for the truncated singular value decomposition for real matrices.
Revised:  This paper presents a novel perturbation analysis for the truncated singular value decomposition for real matrices.
Edit intent: clarity
Original:  In the first part, we provide perturbation expansions for the singular value truncation of order r.
Revised:  First, we describe perturbation expansions for the singular value truncation of order r.
Edit intent: clarity
Original:  In the second part of the paper , we introduce the first-known error bound on the linear approximation of the truncated singular value decomposition of a perturbed rank-r matrix.
Revised:  Second , we introduce the first-known error bound on the linear approximation of the truncated singular value decomposition of a perturbed rank-r matrix.
Edit intent: clarity
Original:  More complex EOS other than the ideal or perfect gas equation of state (EOS ) is used in describing dense gases.
Revised:  More complex equation of state (EOS) other than the ideal or perfect gas equation of state (EOS ) is used in describing dense gases.
Edit intent: fluency
Original:  More complex EOS other than the ideal or perfect gas equation of state (EOS ) is used in describing dense gases.
Revised:  More complex EOS other than the ideal or perfect EOS is typically used in describing dense gases.
Edit intent: clarity
Original:  Algorithms designed for perfect gas EOS can not be extended directly to arbitrary real gas EOS with known EOS formulation .
Revised:  Extending the algorithms designed for perfect gas EOS can not be extended directly to arbitrary real gas EOS with known EOS formulation .
Edit intent: clarity
Original:  Most of the algorithms designed with prefect gas EOS are modified significantly when applied to real gas EOS with the known formulation .
Revised:  Most of the algorithms designed for prefect gas EOS are modified significantly when applied to real gas EOS with the known formulation .
Edit intent: fluency
Original:  Most of the algorithms designed with prefect gas EOS are modified significantly when applied to real gas EOS with the known formulation .
Revised:  Most of the algorithms designed with prefect gas EOS are modified significantly when applied to real gas EOS .
Edit intent: clarity
Original:  Various  1D and 2D  benchmark test cases are validated using these algorithms, and the results  compared with the data obtained from the literature.
Revised:  Various 1D and 2D ) benchmark test cases are validated using these algorithms, and the results compared with the data obtained from the literature.
Edit intent: fluency
Original:  Various  1D and 2D  benchmark test cases are validated using these algorithms, and the results  compared with the data obtained from the literature.
Revised:  Various 1D and 2D benchmark test cases are validated using these algorithms, and the results are compared with the data obtained from the literature.
Edit intent: fluency
Original:  Various  1D and 2D  benchmark test cases are validated using these algorithms, and the results  compared with the data obtained from the literature.
Revised:  Various 1D and 2D benchmark test cases are validated using these algorithms, and the results compared with the those obtained from the literature.
Edit intent: clarity
Original: In recent years, the use of deep learning in language models , text auto-completion, and text generation has made tremendous progress and gained much attention from the research community.
Revised: In recent years, the use of deep learning in language models gained much attention from the research community.
Edit intent: clarity
Original:  Among the different areas related to language processing, one of the most notable in applying this type of modeling is the processing of programming languages.
Revised:  Among the different areas related to language processing, one of the most notable in applying this type of modeling is programming languages.
Edit intent: clarity
Original:  For years, the Machine Learning community has been researching in this Big Code area, pursuing goals like applying different approaches to auto-complete  generate, fix, or evaluate code programmed by humans.
Revised:  For years, the Machine Learning community has been researching in this Big Code area, pursuing goals like applying different approaches to auto-complete , generate, fix, or evaluate code programmed by humans.
Edit intent: fluency
Original:  One of the approaches followed in recent years to pursue these goals is the use of Deep-Learning-enabled language models.
Edit intent: coherence
Original:  Considering the increasing popularity of that approach, we detected a lack of empirical papers that compare different methods and deep learning architectures to create and use language models based on programming code.
Revised:  Considering the increasing popularity of that approach, we detected a lack of empirical papers that compare different deep learning architectures to create and use language models based on programming code.
Edit intent: clarity
Original:  Considering the increasing popularity of the Deep-Learning-enabled 8 language models approach, we detected a lack of empirical papers that compare different deep learning architectures to create and use language models based on programming code.
Revised:  Considering the increasing popularity of the Deep-Learning-enabled language models approach, we detected a lack of empirical papers that compare different deep learning architectures to create and use language models based on programming code.
Edit intent: fluency
Original: Attitudinal Network Graphs (ANG) are network graphs where edges capture an expressed opinion: two vertices connected by an edge can be agreeable (positive) or antagonistic (negative).
Revised: Attitudinal Network Graphs are signed graphs where edges capture an expressed opinion: two vertices connected by an edge can be agreeable (positive) or antagonistic (negative).
Edit intent: clarity
Original:  In this paper, we propose to measure the consensus in the graph by expanding the notion of frustration index to a frustration cloud, a collection of nearest balanced states for a given network.
Revised:  In this paper, we propose to expand the measures of consensus from a single balanced state associated to the frustration index to a frustration cloud, a collection of nearest balanced states for a given network.
Edit intent: clarity
Original:  In this paper, we propose to measure the consensus in the graph by expanding the notion of frustration index to a frustration cloud, a collection of nearest balanced states for a given network.
Revised:  In this paper, we propose to measure the consensus in the graph by expanding the notion of frustration index to the set of nearest balanced states for a given network.
Edit intent: clarity
Original: Attitudinal Network Graphs are signed graphs where edges capture an expressed opinion : two vertices connected by an edge can be agreeable (positive) or antagonistic (negative).
Revised: Attitudinal Network Graphs are signed graphs where edges capture an expressed opinion ; two vertices connected by an edge can be agreeable (positive) or antagonistic (negative).
Edit intent: fluency
Original:  Balance is often characterized by  frustration index or by finding a single convergent balanced state i.e. network consensus.
Revised:  Balance is often characterized by the frustration index or by finding a single convergent balanced state i.e. network consensus.
Edit intent: clarity
Original:  Balance is often characterized by  frustration index or by finding a single convergent balanced state i.e. network consensus.
Revised:  Balance is often characterized by frustration index or by finding a single convergent balanced state of network consensus.
Edit intent: clarity
Original:  In this paper, we propose to expand the measures of consensus from a single balanced state associated to the frustration index to the set of nearest balanced states.
Revised:  In this paper, we propose to expand the measures of consensus from a single balanced state associated with the frustration index to the set of nearest balanced states.
Edit intent: fluency
Original:  We introduce the frustration cloud as a set of all nearest balanced states , and use a graph balancing algorithm to find all nearest balanced states in  deterministic way.
Revised:  We introduce the frustration cloud as a set of all nearest balanced states and use a graph balancing algorithm to find all nearest balanced states in deterministic way.
Edit intent: fluency
Original:  We introduce the frustration cloud as a set of all nearest balanced states , and use a graph balancing algorithm to find all nearest balanced states in  deterministic way.
Revised:  We introduce the frustration cloud as a set of all nearest balanced states , and use a graph-balancing algorithm to find all nearest balanced states in deterministic way.
Edit intent: fluency
Original:  We introduce the frustration cloud as a set of all nearest balanced states , and use a graph balancing algorithm to find all nearest balanced states in  deterministic way.
Revised:  We introduce the frustration cloud as a set of all nearest balanced states , and use a graph balancing algorithm to find all nearest balanced states in a deterministic way.
Edit intent: fluency
Original:  We introduce new global measure of controversy for a given signed graph , and show that vertex status is a zero-sum game in the signed network.
Revised:  We also introduce a new global measure of controversy for a given signed graph , and show that vertex status is a zero-sum game in the signed network.
Edit intent: clarity
Original:  We introduce new global measure of controversy for a given signed graph , and show that vertex status is a zero-sum game in the signed network.
Revised:  We introduce new global measure of controversy for a given signed graph and show that vertex status is a zero-sum game in the signed network.
Edit intent: fluency
Original:  We propose an efficient scalable algorithm for calculating frustration cloud based measures in social network and survey data of up to 80,000 vertices and half-a-million edges , and we demonstrate the power of the proposed approach to provide discriminant features for community discovery when compared to spectral clustering and to automatically identify dominant vertices and anomalous decisions in the network.
Revised:  We propose an efficient scalable algorithm for calculating frustration cloud-based measures in social network and survey data of up to 80,000 vertices and half-a-million edges , and we demonstrate the power of the proposed approach to provide discriminant features for community discovery when compared to spectral clustering and to automatically identify dominant vertices and anomalous decisions in the network.
Edit intent: fluency
Original:  We propose an efficient scalable algorithm for calculating frustration cloud based measures in social network and survey data of up to 80,000 vertices and half-a-million edges , and we demonstrate the power of the proposed approach to provide discriminant features for community discovery when compared to spectral clustering and to automatically identify dominant vertices and anomalous decisions in the network.
Revised:  We propose an efficient scalable algorithm for calculating frustration cloud based measures in social network and survey data of up to 80,000 vertices and half-a-million edges . We also demonstrate the power of the proposed approach to provide discriminant features for community discovery when compared to spectral clustering and to automatically identify dominant vertices and anomalous decisions in the network.
Edit intent: clarity
Original: We present CoDEx, a set of knowledge graph Completion Datasets Extracted from Wikidata and Wikipedia that improve upon existing knowledge graph completion benchmarks in scope and level of difficulty.
Revised: We present CoDEx, a set of knowledge graph completion datasets extracted from Wikidata and Wikipedia that improve upon existing knowledge graph completion benchmarks in scope and level of difficulty.
Edit intent: fluency
Original:  The main goal is to establish mobility and collaboration profiles at the region and country levels.
Edit intent: coherence