wanyu/IteraTeR_v2 huggingface dataset:
Original: In geometry processing, symmetry is the universally high-level structural information of the 3d models and benefits many geometry processing tasks including shape segmentation, alignment, matching, completion , e. g..
Revised: In geometry processing, symmetry is a universal type of high-level structural information of the 3d models and benefits many geometry processing tasks including shape segmentation, alignment, matching, completion , e. g..
Edit intent: clarity
Original: In geometry processing, symmetry is the universally high-level structural information of the 3d models and benefits many geometry processing tasks including shape segmentation, alignment, matching, completion , e. g..
Revised: In geometry processing, symmetry is the universally high-level structural information of 3D models and benefits many geometry processing tasks including shape segmentation, alignment, matching, completion , e. g..
Edit intent: fluency
Original: In geometry processing, symmetry is the universally high-level structural information of the 3d models and benefits many geometry processing tasks including shape segmentation, alignment, matching, completion , e. g..
Revised: In geometry processing, symmetry is the universally high-level structural information of the 3d models and benefits many geometry processing tasks including shape segmentation, alignment, matching, completion .
Edit intent: coherence
Original:  Thus it is an important problem to analyze various forms of the symmetry of 3D shapes.
Revised:  Thus it is an important problem to analyze various symmetry forms of 3D shapes.
Edit intent: fluency
Original:  The planar reflective symmetry is the most fundamental one.
Revised:  Planar reflective symmetry is the most fundamental one.
Edit intent: coherence
Original:  Traditional methods based on spatial sampling can be time consuming and may not be able to identify all the symmetry planes.
Revised:  Traditional methods based on spatial sampling can be time-consuming and may not be able to identify all the symmetry planes.
Edit intent: fluency
Original:  Our neural network-based method is hundreds of times faster than the state-of-the-art method, which is based on sampling.
Revised:  Our neural network based method is hundreds of times faster than the state-of-the-art method, which is based on sampling.
Edit intent: fluency
Original:  Our neural network-based method is hundreds of times faster than the state-of-the-art method, which is based on sampling.
Revised:  Our neural network-based method is hundreds of times faster than the state-of-the-art methods, which are based on sampling.
Edit intent: fluency
Original: In geometry processing, symmetry is a universal type of high-level structural information of 3D models and benefits many geometry processing tasks including shape segmentation, alignment, matching,  completion.
Revised: In geometry processing, symmetry is a universal type of high-level structural information of 3D models and benefits many geometry processing tasks including shape segmentation, alignment, matching, and completion.
Edit intent: fluency
Original:  Despite its long-standing history and important applicability , the Colonel Blotto game still lacks a complete Nash equilibrium characterization in its most general form-the non-constant-sum version with asymmetric players and heterogeneous battlefields .
Revised:  Despite its long-standing history and important applications , the Colonel Blotto game still lacks a complete Nash equilibrium characterization in its most general form-the non-constant-sum version with asymmetric players and heterogeneous battlefields .
Edit intent: clarity
Original:  In this work, we propose a simply-constructed class of strategies-the independently uniform strategies-and we prove them to be approximate equilibria of the non-constant-sum Colonel Blotto game;
Revised:  In this work, we propose a simply-constructed class of strategies---the independently uniform strategies---and we prove that they are approximate equilibria of the non-constant-sum Colonel Blotto game;
Edit intent: fluency
Original:  moreover, we also characterize the approximation error according to the game's parameters.
Revised:  moreover, we characterize the approximation error according to the game's parameters.
Edit intent: coherence
Original:  We also introduce an extension called the  Lottery Blotto game, with stochastic winner-determination rules allowing more flexibility in modeling practical contexts .
Revised:  We also consider an extension called the Lottery Blotto game, with stochastic winner-determination rules allowing more flexibility in modeling practical contexts .
Edit intent: clarity
Original:  We also introduce an extension called the  Lottery Blotto game, with stochastic winner-determination rules allowing more flexibility in modeling practical contexts .
Revised:  We also introduce an extension called the Lottery Blotto game, with stochastic winner-determination rules allowing more flexibility in modeling practical contests .
Edit intent: clarity
Original:  Given a query graph G_Q,  graph query finds subgraphs in a knowledge graph G that exactly or approximately match G_Q. We face two challenges on graph query over a knowledge graph : (1) the structural gap between G_Q and the predefined schema in G causes mismatch with query graph, (2) users cannot view the answers until the graph query terminates, leading to a longer system response time (SRT).
Revised:  Given a query graph G_Q, the graph query finds subgraphs in a knowledge graph G that exactly or approximately match G_Q. We face two challenges on graph query over a knowledge graph : (1) the structural gap between G_Q and the predefined schema in G causes mismatch with query graph, (2) users cannot view the answers until the graph query terminates, leading to a longer system response time (SRT).
Edit intent: fluency
Original:  Given a query graph G_Q,  graph query finds subgraphs in a knowledge graph G that exactly or approximately match G_Q. We face two challenges on graph query over a knowledge graph : (1) the structural gap between G_Q and the predefined schema in G causes mismatch with query graph, (2) users cannot view the answers until the graph query terminates, leading to a longer system response time (SRT).
Revised:  Given a query graph G_Q, graph query finds subgraphs in a knowledge graph G that exactly or approximately match G_Q. We face two challenges on graph query : (1) the structural gap between G_Q and the predefined schema in G causes mismatch with query graph, (2) users cannot view the answers until the graph query terminates, leading to a longer system response time (SRT).
Edit intent: clarity
Original:  In this paper, we propose a semantic guided and response-time-bounded graph query to return  top-k answers effectively and efficiently.
Revised:  In this paper, we propose a semantic-guided and response-time-bounded graph query to return top-k answers effectively and efficiently.
Edit intent: fluency
Original:  In this paper, we propose a semantic guided and response-time-bounded graph query to return  top-k answers effectively and efficiently.
Revised:  In this paper, we propose a semantic guided and response-time-bounded graph query to return the top-k answers effectively and efficiently.
Edit intent: fluency
Original:  We first leverage a knowledge graph embedding model to build the semantic graph SG_Q for each G_Q. Then we define the path semantic similarity (pss) over SG_Q  to evaluate the match 's quality.
Revised:  We leverage a knowledge graph embedding model to build the semantic graph SG_Q for each G_Q. Then we define the path semantic similarity (pss) over SG_Q to evaluate the match 's quality.
Edit intent: coherence
Original:  We first leverage a knowledge graph embedding model to build the semantic graph SG_Q for each G_Q. Then we define the path semantic similarity (pss) over SG_Q  to evaluate the match 's quality.
Revised:  We first leverage a knowledge graph embedding model to build the semantic graph SG_Q , and we define the path semantic similarity (pss) over SG_Q to evaluate the match 's quality.
Edit intent: coherence
Original:  We first leverage a knowledge graph embedding model to build the semantic graph SG_Q for each G_Q. Then we define the path semantic similarity (pss) over SG_Q  to evaluate the match 's quality.
Revised:  We first leverage a knowledge graph embedding model to build the semantic graph SG_Q for each G_Q. Then we define the path semantic similarity (pss) over SG_Q as the metric to evaluate the match 's quality.
Edit intent: clarity
Original:  We first leverage a knowledge graph embedding model to build the semantic graph SG_Q for each G_Q. Then we define the path semantic similarity (pss) over SG_Q  to evaluate the match 's quality.
Revised:  We first leverage a knowledge graph embedding model to build the semantic graph SG_Q for each G_Q. Then we define the path semantic similarity (pss) over SG_Q to evaluate the answer 's quality.
Edit intent: clarity
Original:  We propose an A* semantic search on SG_Q to find  top-k answers with the greatest pss via a heuristic pss estimation.
Revised:  Then, we propose an A* semantic search on SG_Q to find top-k answers with the greatest pss via a heuristic pss estimation.
Edit intent: coherence
Original:  We propose an A* semantic search on SG_Q to find  top-k answers with the greatest pss via a heuristic pss estimation.
Revised:  We propose an A* semantic search on SG_Q to find the top-k answers with the greatest pss via a heuristic pss estimation.
Edit intent: fluency
Original:  Extensive experiments over real datasets confirm the effectiveness and efficiency  .
Revised:  Extensive experiments over real datasets confirm the effectiveness and efficiency of our solution .
Edit intent: clarity
Original:  In addition, the energy consumption between nodes communication is high due to limited processing.
Revised:  In addition, the energy consumption in communication nodes is high due to limited processing.
Edit intent: clarity
Original: In this paper, we consider maximum likelihood estimation of the degree of freedom parameter \nu, the location paramter \mu and the scatter matrix \Sigma of the multivariate Student-t distribution.
Revised: In this paper, we consider maximum likelihood estimations of the degree of freedom parameter \nu, the location paramter \mu and the scatter matrix \Sigma of the multivariate Student-t distribution.
Edit intent: fluency
Original: In this paper, we consider maximum likelihood estimation of the degree of freedom parameter \nu, the location paramter \mu and the scatter matrix \Sigma of the multivariate Student-t distribution.
Revised: In this paper, we consider maximum likelihood estimation of the degree of freedom parameter \nu, the location parameter \mu and the scatter matrix \Sigma of the multivariate Student-t distribution.
Edit intent: fluency
Original:  In particular, we are interested in approximating the degree of freedom parameter \nu that determines the tails of the corresponding probability density function  .
Revised:  In particular, we are interested in estimating the degree of freedom parameter \nu that determines the tails of the corresponding probability density function .
Edit intent: clarity
Original:  We compare the algorithms by numerical examples and apply one of these algorithms for estimating the degree of freedom parameter in images corrupted by Student-t noise.
Revised:  We compare the algorithms by numerical examples and apply one of them for estimating the degree of freedom parameter in images corrupted by Student-t noise.
Edit intent: clarity
Original: This paper employs the recently introduced linear nested sequent framework to design a new cut-free calculus LNIF for intuitionistic fuzzy logic--the first-order G\"odel logic characterized by linear relational frames with constant domains.
Revised: This paper employs the linear nested sequent framework to design a new cut-free calculus LNIF for intuitionistic fuzzy logic--the first-order G\"odel logic characterized by linear relational frames with constant domains.
Edit intent: coherence
Original:  The paper concludes with an empirical study on the leverage effect patterns estimated using high-frequency data for the S P 500 futures between January 2007 and December 2008.
Revised:  The paper concludes with an empirical study on the leverage effect patterns estimated using high-frequency data for the S 
Edit intent: clarity
Original: We analyse the stochastic leverage effect, which is defined as the instantaneous correlation between the returns and their related volatility increments, in a stochastic volatility model set-up.
Revised: The stochastic leverage effect, which is defined as the instantaneous correlation between the returns and their related volatility increments, in a stochastic volatility model set-up.
Edit intent: coherence
Original: We analyse the stochastic leverage effect, which is defined as the instantaneous correlation between the returns and their related volatility increments, in a stochastic volatility model set-up.
Revised: We analyse the stochastic leverage effect, defined as the instantaneous correlation between the returns and their related volatility increments, in a stochastic volatility model set-up.
Edit intent: coherence
Original: We analyse the stochastic leverage effect, which is defined as the instantaneous correlation between the returns and their related volatility increments, in a stochastic volatility model set-up.
Revised: We analyse the stochastic leverage effect, which is defined as the instantaneous correlation between the returns and their related volatility , is analyzed in a stochastic volatility model set-up.
Edit intent: clarity
Original:  We establish the consistency of the estimator and we analyse its finite sample performance in the presence of microstructure noise contamination.
Revised:  We establish the consistency of the estimator is proven. Moreover, its finite sample performance in the presence of microstructure noise contamination.
Edit intent: coherence
Original:  We establish the consistency of the estimator and we analyse its finite sample performance in the presence of microstructure noise contamination.
Revised:  We establish the consistency of the estimator and we analyse its finite sample properties are studied in the presence of microstructure noise contamination.
Edit intent: clarity
Original: Recently, many studies proposed CNN accelerator architectures with custom computation units that try to improve energy-efficiency and performance of CNN by minimizing data transfers from DRAM-based main memory.
Revised: Recently, many studies proposed CNN accelerator architectures with custom computation units that try to improve energy-efficiency and performance of CNNs by minimizing data transfers from DRAM-based main memory.
Edit intent: clarity
Original:  However, in these architectures, DRAM still contributes half of the overall energy consumption of the system, on average.
Revised:  However, in these architectures, DRAM is still responsible for half of the overall energy consumption of the system, on average.
Edit intent: clarity
Original:  We propose a new mechanism, Refresh Triggered Computation (RTC), that exploits the memory access patterns of CNN applications to reduce the number of refresh operations.
Revised:  In this paper, we propose a new mechanism, Refresh Triggered Computation (RTC), that exploits the memory access patterns of CNN applications to reduce the number of refresh operations.
Edit intent: clarity
Original:  Even the most aggressive design of RTC imposes an area overhead of only 0.18\% in a 2Gb DRAM chip and can have less overhead for denser chips.
Revised:  Even the most aggressive RTC design (i.e., full-RTC) imposes an area overhead of only 0.18\% in a 2Gb DRAM chip and can have less overhead for denser chips.
Edit intent: clarity
Original:  Our experimental evaluation on three well-known CNNs (i.e., AlexNet, LeNet, and GoogleNet) show that RTC can reduce the DRAM refresh energy from 25\% to 96 \%, for the least aggressive and the most aggressive designs , respectively.
Revised:  Our experimental evaluation on three well-known CNNs show that RTC can reduce the DRAM refresh energy from 25\% to 96 \%, for the least aggressive and the most aggressive designs , respectively.
Edit intent: clarity
Original:  Our experimental evaluation on three well-known CNNs (i.e., AlexNet, LeNet, and GoogleNet) show that RTC can reduce the DRAM refresh energy from 25\% to 96 \%, for the least aggressive and the most aggressive designs , respectively.
Revised:  Our experimental evaluation on three well-known CNNs (i.e., AlexNet, LeNet, and GoogleNet) show that RTC can reduce the DRAM refresh energy from 25\% to 96 \%, for the least aggressive and the most aggressive RTC implementations , respectively.
Edit intent: clarity
Original:  Although we mainly use CNNsin our evaluations, we believe RTC can be applied to a wide range of applications , whose memory access patterns remain predictable for  sufficiently long time.
Revised:  Although we mainly use CNNsin our evaluations, we believe RTC can be applied to other applications whose memory access patterns remain predictable for sufficiently long time.
Edit intent: clarity
Original:  Although we mainly use CNNsin our evaluations, we believe RTC can be applied to a wide range of applications , whose memory access patterns remain predictable for  sufficiently long time.
Revised:  Although we mainly use CNNsin our evaluations, we believe RTC can be applied to a wide range of applications , whose memory access patterns remain predictable for a sufficiently long time.
Edit intent: fluency
Original: Previous works \mbox{ donahue2018adversarial, engel2019gansynth have found that generating coherent raw audio waveforms with GANs is challenging.
Revised: Previous works (Donahue et al., 2018a; Engel et al., 2019a) have found that generating coherent raw audio waveforms with GANs is challenging.
Edit intent: clarity
Original:  Our pytorch implementation runs at more than 100x faster than realtime on GTX 1080Ti GPU and more than 2x faster than real-time on CPU, without any hardware specific optimization tricks . Blog post with samples and accompanying code coming soon .
Revised:  Our pytorch implementation runs at more than 100x faster than realtime on GTX 1080Ti GPU and more than 2x faster than real-time on CPU, without any hardware specific optimization tricks .
Edit intent: clarity
Original:  We propose a conceptual model that can reconcile such conflict by showing cities have an extra growth of urban land over time besides the supposed growth predicted by the cross-sectional scaling law.
Revised:  We propose a conceptual model that can reconcile such conflict by showing cities have an extra growth of built-up area over time besides the supposed growth predicted by the cross-sectional scaling law.
Edit intent: clarity
Original:  Differences of extra growth among different-sized cities change the cross-sectional scaling exponent.
Revised:  Disparities of extra growth among different-sized cities change the cross-sectional scaling exponent.
Edit intent: clarity
Original:  Further analyses of GDP and other indicators confirm the contradiction between cross-sectional and temporal scaling laws and the validity of the conceptual model.
Revised:  Further analyses of GDP and other indicators confirm the contradiction between cross-sectional and temporal scaling relationships and the validity of the conceptual model.
Edit intent: clarity
Original: The Transformer architecture recently replaced recurrent neural networks such as LSTM or GRU on many natural language processing (NLP) tasks by presenting new state of the art performance.
Revised: Transformer neural networks (TNN) demonstrated state-of-art performance on many natural language processing (NLP) tasks by presenting new state of the art performance.
Edit intent: clarity
Original:  Self-attention is a core building block for Transformer, which not only enables parallelization of sequence computation  but also provides the constant path length between symbols that is essential to learn long-range dependencies.
Revised:  Self-attention is a core building block of the Transformer, which not only enables parallelization of sequence computation but also provides the constant path length between symbols that is essential to learn long-range dependencies.
Edit intent: fluency
Original:  Self-attention is a core building block for Transformer, which not only enables parallelization of sequence computation  but also provides the constant path length between symbols that is essential to learn long-range dependencies.
Revised:  Self-attention is a core building block for Transformer, which not only enables parallelization of sequence computation , but also provides the constant path length between symbols that is essential to learn long-range dependencies.
Edit intent: fluency
Original:  Self-attention is a core building block for Transformer, which not only enables parallelization of sequence computation  but also provides the constant path length between symbols that is essential to learn long-range dependencies.
Revised:  Self-attention is a core building block for Transformer, which not only enables parallelization of sequence computation but also provides the constant path length between symbols that is essential to learning long-range dependencies.
Edit intent: fluency
Original:  However, Transformer did not perform well for speech enhancement due to the lack of consideration for speech and noise physical characteristics.
Edit intent: coherence
Original:  The experimental results showed that the proposed attention scheme significantly improved over the original Transformer as well as recurrent networks .
Revised:  The experimental results show that the proposed attention scheme significantly improved over the original Transformer as well as recurrent networks .
Edit intent: fluency
Original:  The experimental results showed that the proposed attention scheme significantly improved over the original Transformer as well as recurrent networks .
Revised:  The experimental results showed that the proposed T-GSA has significantly improved speech-enhancement performance, compared to the Transformer and RNNs .
Edit intent: clarity
Original: Academic publications have been evaluated with the impact on research communities based on  the number of citations.
Revised: Academic publications have been evaluated in terms of their impact on research communities based on the number of citations.
Edit intent: clarity
Original: Academic publications have been evaluated with the impact on research communities based on  the number of citations.
Revised: Academic publications have been evaluated with the impact on research communities based on many metrics, such as the number of citations.
Edit intent: clarity
Original:  We propose an automated approach of detecting academic publications based on Named Entity Recognition, and achieve 0.90 in F_1 as detection accuracy.
Revised:  We propose an automated approach for detecting academic publications based on Named Entity Recognition, and achieve 0.90 in F_1 as detection accuracy.
Edit intent: fluency
Original:  We conduct a large-scale study of publication citations with 319,438,977 comments collected from active 25,925  repositories written in seven programming languages.
Revised:  We conduct a large-scale study of publication citations with 319,438,977 comments collected from 25,925 repositories written in seven programming languages.
Edit intent: clarity
Original: In this study, we have investigated the landscape of academic literature in quantum information technologies (QIT ) using bibliometric tools of analysis.
Revised: In this study, we investigated the academic literature on quantum technologies (QT ) using bibliometric tools of analysis.
Edit intent: clarity