wanyu/IteraTeR_v2 huggingface dataset:
Original:  With these two CNS distributions, we further derive theoretical solutions with respect to two key metrics of predictionperformance : i) Precision and ii) area under the receiver operating characteristic curve (AUC) , which significantly reduce the evaluation cost of link prediction .
Revised:  With these two CNS distributions, we further derive theoretical solutions of two key evaluation metrics in link prediction : i) Precision and ii) area under the receiver operating characteristic curve (AUC) , which significantly reduce the evaluation cost of link prediction .
Edit intent: clarity
Original:  With these two CNS distributions, we further derive theoretical solutions with respect to two key metrics of predictionperformance : i) Precision and ii) area under the receiver operating characteristic curve (AUC) , which significantly reduce the evaluation cost of link prediction .
Revised:  With these two CNS distributions, we further derive theoretical solutions with respect to two key metrics of predictionperformance : i) precision and ii) area under the receiver operating characteristic curve (AUC) , which significantly reduce the evaluation cost of link prediction .
Edit intent: fluency
Original: This study proposed a controllable crack propagation (CCP) method by integrating back propagation neural network (BPNN) assisted particle swarm optimization (PSO), reanalysis and extended finite element methods (X-FEM) . It is well known that the internal crack propagation always leads the failure of engineering structure because the crack crossed the critical domain of the structure.
Revised: A controllable crack propagation (CCP) method by integrating back propagation neural network (BPNN) assisted particle swarm optimization (PSO), reanalysis and extended finite element methods (X-FEM) . It is well known that the internal crack propagation always leads the failure of engineering structure because the crack crossed the critical domain of the structure.
Edit intent: clarity
Original: This study proposed a controllable crack propagation (CCP) method by integrating back propagation neural network (BPNN) assisted particle swarm optimization (PSO), reanalysis and extended finite element methods (X-FEM) . It is well known that the internal crack propagation always leads the failure of engineering structure because the crack crossed the critical domain of the structure.
Revised: This study proposed a controllable crack propagation (CCP) strategy is suggested . It is well known that the internal crack propagation always leads the failure of engineering structure because the crack crossed the critical domain of the structure.
Edit intent: clarity
Original: This study proposed a controllable crack propagation (CCP) method by integrating back propagation neural network (BPNN) assisted particle swarm optimization (PSO), reanalysis and extended finite element methods (X-FEM) . It is well known that the internal crack propagation always leads the failure of engineering structure because the crack crossed the critical domain of the structure.
Revised: This study proposed a controllable crack propagation (CCP) method by integrating back propagation neural network (BPNN) assisted particle swarm optimization (PSO), reanalysis and extended finite element methods (X-FEM) . It is well known that crack always leads the failure of engineering structure because the crack crossed the critical domain of the structure.
Edit intent: coherence
Original: This study proposed a controllable crack propagation (CCP) method by integrating back propagation neural network (BPNN) assisted particle swarm optimization (PSO), reanalysis and extended finite element methods (X-FEM) . It is well known that the internal crack propagation always leads the failure of engineering structure because the crack crossed the critical domain of the structure.
Revised: This study proposed a controllable crack propagation (CCP) method by integrating back propagation neural network (BPNN) assisted particle swarm optimization (PSO), reanalysis and extended finite element methods (X-FEM) . It is well known that the internal crack propagation always leads the failure by crossing the critical domain in engineering structure.
Edit intent: coherence
Original:  Therefore, the controllable crack propagation method is proposed to control the crack propagation path and make the crack propagate along the specified path, so that the critical domain should not be crossed by the crack and the failure will be avoided.
Revised:  Therefore, the CCP method is proposed to control the crack propagation path and make the crack propagate along the specified path, so that the critical domain should not be crossed by the crack and the failure will be avoided.
Edit intent: clarity
Original:  Therefore, the controllable crack propagation method is proposed to control the crack propagation path and make the crack propagate along the specified path, so that the critical domain should not be crossed by the crack and the failure will be avoided.
Revised:  Therefore, the controllable crack propagation method is proposed to control the crack to propagate along the specified path, so that the critical domain should not be crossed by the crack and the failure will be avoided.
Edit intent: clarity
Original:  Therefore, the controllable crack propagation method is proposed to control the crack propagation path and make the crack propagate along the specified path, so that the critical domain should not be crossed by the crack and the failure will be avoided.
Revised:  Therefore, the controllable crack propagation method is proposed to control the crack propagation path and make the crack propagate along the specified path, which is away from the critical domain should not be crossed by the crack and the failure will be avoided.
Edit intent: coherence
Original:  Considering the optimization iteration is a time consuming process, an efficient reanalysis based  X-FEM  is used to  calculate the crack propagation path, in which a reanalysis solver is used to solve the equilibrium equations efficiently.
Revised:  Considering the optimization iteration is a time consuming process, an efficient reanalysis based X-FEM ) is used to calculate the crack propagation path, in which a reanalysis solver is used to solve the equilibrium equations efficiently.
Edit intent: fluency
Original: We present the submatrix method, a highly parallelizable method for the approximate calculation of inverse p-th roots of large sparse symmetric matrices which are required in different scientific applications such as electronic structure methods .
Revised: We present the submatrix method, a highly parallelizable method for the approximate calculation of inverse p-th roots of large sparse symmetric matrices which are required in different scientific applications .
Edit intent: clarity
Original:  Finally, we present a distributed implementation of the algorithm using MPI and OpenMP and demonstrate its scalability by running it on a high-performance compute cluster comprised of 1024 CPU cores, showing a speedup of 661x compared to single-threaded execution.
Revised:  Finally, we present a distributed implementation of the algorithm using MPI and OpenMP . We demonstrate the scalability of this implementation by running it on a high-performance compute cluster comprised of 1024 CPU cores, showing a speedup of 661x compared to single-threaded execution.
Edit intent: clarity
Original:  Our GAT models have achieved  state-of-the-art results across three established transductive and inductive graph benchmarks: the Cora and Citeseer citation network datasets, as well as a protein-protein interaction dataset (wherein test graphs are entirely unseen during training).
Revised:  Our GAT models have achieved state-of-the-art results across three established transductive and inductive graph benchmarks: the Cora and Citeseer citation network datasets, as well as a protein-protein interaction dataset (wherein test graphs remain unseen during training).
Edit intent: clarity
Original:  The proposed work explores the possibilities of using living neural networks in vitro as the basic computational elements for machine learning applications.
Revised:  The proposed work explores the possibilities of using living neural networks in vitro as basic computational elements for machine learning applications.
Edit intent: fluency
Original:  A  supervised STDP-based learning algorithm is proposed in this work, which considers neuron engineering constrains.
Revised:  A new supervised STDP-based learning algorithm is proposed in this work, which considers neuron engineering constrains.
Edit intent: clarity
Original: We consider the problem of finding the minimizer of a function f: R^d \rightarrow R of the  form \min f(w) = \frac{1 f_i( w ).
Revised: We consider the problem of finding the minimizer of a function f: R^d \rightarrow R of the form \min f(w) = \frac{1 f_i( w ).
Edit intent: fluency
Original:  One typical but promising approach for large-scale data is  stochastic optimization algorithm  . SGDLibrary is a flexible, extensible and efficient pure-Matlab library of a collection of stochastic optimization algorithms.
Revised:  One typical but promising approach for large-scale data is to use a stochastic optimization algorithm . SGDLibrary is a flexible, extensible and efficient pure-Matlab library of a collection of stochastic optimization algorithms.
Edit intent: coherence
Original:  One typical but promising approach for large-scale data is  stochastic optimization algorithm  . SGDLibrary is a flexible, extensible and efficient pure-Matlab library of a collection of stochastic optimization algorithms.
Revised:  One typical but promising approach for large-scale data is stochastic optimization algorithm . SGDLibrary is a readable, flexible and extensible pure-MATLAB library of a collection of stochastic optimization algorithms.
Edit intent: clarity
Original:  The purpose of the library is to provide researchers and implementers a comprehensive evaluation environment of those algorithms on various machine learning problems.
Revised:  The purpose of the library is to provide researchers and implementers a comprehensive evaluation environment for the use of these algorithms on various machine learning problems.
Edit intent: clarity
Original:  The purpose of the library is to provide researchers and implementers a comprehensive evaluation environment of those algorithms on various machine learning problems.
Revised:  The purpose of the library is to provide researchers and implementers a comprehensive evaluation environment of those algorithms on various ML problems.
Edit intent: clarity
Original:  After this first step is fully operational, we could, in practice, move on towards a cooperative network of autonomous vehicles and small satellites   .
Revised:  After this first step is fully operational, we will start to move towards a cooperative network of autonomous vehicles and small satellites .
Edit intent: clarity
Original:  After this first step is fully operational, we could, in practice, move on towards a cooperative network of autonomous vehicles and small satellites   .
Revised:  After this first step is fully operational, we could, in practice, move on towards a cooperative network of autonomous vehicles and small satellites in-situ .
Edit intent: clarity
Original:  We evaluated the proposed method on Pasokh single document dataset using the ROUGE evaluation measure.
Revised:  We evaluated the proposed method on Pasokh single-document data set using the ROUGE evaluation measure.
Edit intent: clarity
Original:  Without using any hand-crafted features, our proposed method achieves competitive performance . ROUGE-3 recall score for system summaries generated with 25\% compression ratio on Pasokh corpus is 0.27 .
Revised:  Without using any hand-crafted features, our proposed method achieves competitive performance .
Edit intent: coherence
Original:  Although the proposed method is language independent, we focus on Persian text summarization in this work.
Edit intent: coherence
Original:  We evaluated the proposed method on Pasokh single-document data set using the ROUGE evaluation measure.
Revised:  We evaluated the proposed method on Pasokh single-document corpus using the ROUGE evaluation measure.
Edit intent: clarity
Original: Synergies among technological opportunities, market perspectives, and geographical endowments can be considered as indicators of systemness.
Edit intent: coherence
Original:  Using information theory, we propose a measure of synergy among size-classes, zip-codes, and  NACE-codes  for 8.5 million American firms .
Revised:  Using information theory, we propose a measure of synergy among size-classes, zip-codes, and NACE-codes ) for 8.5 million American firms .
Edit intent: fluency
Original:  Using information theory, we propose a measure of synergy among size-classes, zip-codes, and  NACE-codes  for 8.5 million American firms .
Revised:  Using information theory, we propose a measure of synergy among size-classes, zip-codes, and NACE-codes for 8.5 million American companies .
Edit intent: fluency
Original:  The synergy at the national level is decomposed at the level of states and Core-Based Statistical Areas (CBSA) as regions.
Revised:  The synergy at the national level is decomposed at the level of states , Core-Based Statistical Areas (CBSA) as regions.
Edit intent: fluency
Original:  Thereafter, we zoom in to the state of California and in even more detail to Silicon Valley.
Revised:  Thereafter, we zoom in to the state of California and in more detail to Silicon Valley.
Edit intent: clarity
Original:  Our results do not support the assumption of a national system of innovations in the U.S.A. Innovation systems appear to operate at the level of the states , the CBSA regions are too small, so that systemness spills across their borders.
Revised:  Our results do not support the assumption of a national system of innovations in the U.S.A. Innovation systems appear to operate at the level of the states ; the CBSA are too small, so that systemness spills across their borders.
Edit intent: clarity
Original:  The East Coast--New Jersey, Boston, and New York--and California are the major players, with Texas a third one in the case of HTKIS.
Revised:  The East Coast -- New Jersey, Boston, and New York--and California are the major players, with Texas a third one in the case of HTKIS.
Edit intent: fluency
Original:  The East Coast--New Jersey, Boston, and New York--and California are the major players, with Texas a third one in the case of HTKIS.
Revised:  The East Coast--New Jersey, Boston, and New York -- and California are the major players, with Texas a third one in the case of HTKIS.
Edit intent: fluency
Original:  At the regional level, Chicago and industrial centers in the Midwest also contribute synergy.
Revised:  Chicago and industrial centers in the Midwest also contribute synergy.
Edit intent: clarity
Original:  Within California, Los Angeles and its environment contribute synergy in the sectors of manufacturing, the San Francisco area in KIS , and Silicon Valley in both, but with synergy mainly generated by manufacturing . Knowledge-intensive services in Silicon Valley spillover to other regions and even globally.
Revised:  Within California, Los Angeles contributes synergy in the sectors of manufacturing, the San Francisco area in KIS , and Silicon Valley in both, but with synergy mainly generated by manufacturing . Knowledge-intensive services in Silicon Valley spillover to other regions and even globally.
Edit intent: clarity
Original:  Within California, Los Angeles and its environment contribute synergy in the sectors of manufacturing, the San Francisco area in KIS , and Silicon Valley in both, but with synergy mainly generated by manufacturing . Knowledge-intensive services in Silicon Valley spillover to other regions and even globally.
Revised:  Within California, Los Angeles and its environment contribute synergy in the sectors of manufacturing, the San Francisco area in KIS . Knowledge-intensive services in Silicon Valley spillover to other regions and even globally.
Edit intent: coherence
Original: One can use the Leiden Rankings for grouping research universities by considering universities which are not  significantly different as a homogeneous set.
Revised: The Leiden Rankings can be used for grouping research universities by considering universities which are not significantly different as a homogeneous set.
Edit intent: clarity
Original: One can use the Leiden Rankings for grouping research universities by considering universities which are not  significantly different as a homogeneous set.
Revised: One can use the Leiden Rankings for grouping research universities by considering universities which are not statistically significantly different as a homogeneous set.
Edit intent: style
Original:  We pursue this classification using both statistical significance and effect sizes of differences among 902 universities in 54 countries , we focus on the UK, Germany, and Brazil as national examples.
Revised:  We pursue this classification using both statistical significance and effect sizes of differences among 902 universities in 54 countries ; we focus on the UK, Germany, and Brazil as national examples.
Edit intent: fluency
Original:  Although the groupings remain largely the same using different statistical significance levels , the resulting classifications are uncorrelated with the ones based on effect sizes (Cramer's V < .3) . Effect sizes for the differences between universities are small (w <.2).
Revised:  Although the groupings remain largely the same using different statistical significance levels , the resulting classifications are uncorrelated with those based on effect sizes (Cramer's V < .3) . Effect sizes for the differences between universities are small (w <.2).
Edit intent: clarity
Original:  Although the groupings remain largely the same using different statistical significance levels , the resulting classifications are uncorrelated with the ones based on effect sizes (Cramer's V < .3) . Effect sizes for the differences between universities are small (w <.2).
Revised:  Although the groupings remain largely the same using different statistical significance levels , the resulting classifications are uncorrelated with the ones based on effect sizes . Effect sizes for the differences between universities are small (w <.2).
Edit intent: coherence
Original: In random graph models, the degree distribution of an %DIFDELCMD < {\em %%% individual node should be distinguished from the (empirical) degree distribution of the %DIFDELCMD < {\em %DIFDELCMD < %%% graph that records the fractions of nodes with given degree.
Revised: In random graph models, the degree distribution of an %DIFDELCMD < {\em %%% individual node should be distinguished from the (empirical) degree distribution of the %DIFDELCMD < {\em %DIFDELCMD < %%% graph that records the fractions of nodes with given degree.
Edit intent: fluency
Original:  In this work, we provide a family of such genuine reinforcement learning tasks , and we construct quantum-enhanced learners which learn super-polynomially  faster than any classical reinforcement learning model  .
Revised:  In this work, we provide a family of such genuine reinforcement learning tasks . We construct quantum-enhanced learners which learn super-polynomially faster than any classical reinforcement learning model .
Edit intent: coherence
Original: Recent work on quantum machine learning has demonstrated that quantum computers can offer dramatic improvements over classical devices for data mining, prediction and classification.
Revised: Quantum computers can offer dramatic improvements over classical devices for data mining, prediction and classification.
Edit intent: clarity
Original: Recent work on quantum machine learning has demonstrated that quantum computers can offer dramatic improvements over classical devices for data mining, prediction and classification.
Revised: Recent work on quantum machine learning has demonstrated that quantum computers can offer dramatic improvements over classical devices for data analysis tasks such as prediction and classification.
Edit intent: clarity
Original:  However, less is known about the advantages using quantum computers may bring in the more general setting of reinforcement learning, where learning is achieved via interaction with a task environment that provides occasional rewards.
Revised:  However, less is known about the advantages that quantum computers may bring in the more general setting of reinforcement learning, where learning is achieved via interaction with a task environment that provides occasional rewards.
Edit intent: clarity
Original:  However, less is known about the advantages using quantum computers may bring in the more general setting of reinforcement learning, where learning is achieved via interaction with a task environment that provides occasional rewards.
Revised:  However, less is known about the advantages using quantum computers may bring in the setting of reinforcement learning, where learning is achieved via interaction with a task environment that provides occasional rewards.
Edit intent: clarity
Original:  The functional mechanisms behind  remain largely unknown.
Revised:  The functional mechanisms behind this phenomenon remain largely unknown.
Edit intent: clarity
Original:  An experimental evidence suggests that some of the memory functions are performed by stratified brain structures  as, e.g., the hippocampus.
Revised:  Experimental evidence suggests that some of the memory functions are performed by stratified brain structures as, e.g., the hippocampus.
Edit intent: clarity
Original:  An experimental evidence suggests that some of the memory functions are performed by stratified brain structures  as, e.g., the hippocampus.
Revised:  An experimental evidence suggests that some of the memory functions are performed by stratified brain structures such as, e.g., the hippocampus.
Edit intent: clarity
Original:  We thus assess the implication of the abundance of neuronal signaling routs converging onto single cells on the information processing.
Revised:  We thus assess the implication of the abundance of neuronal signalling routes converging onto single cells on the information processing.
Edit intent: fluency
Original:  The argument is based on Stochastic Separation Theorems and measure concentration phenomena.
Revised:  The argument is based on Stochastic Separation Theorems and the concentration of measure phenomena.
Edit intent: clarity
Original:  We demonstrate that a simple enough functional neuronal model is capable of explaining: i) the extreme selectivity of single neurons to the information content, ii) simultaneous separation of several uncorrelated stimuli , or informational items , from a large set, and iii) dynamic learning of new items by associating them with already "known" ones.
Revised:  We demonstrate that a simple enough functional neuronal model is capable of explaining: i) the extreme selectivity of single neurons to the information content, ii) simultaneous separation of several uncorrelated stimuli or informational items , from a large set, and iii) dynamic learning of new items by associating them with already "known" ones.
Edit intent: fluency
Original:  We demonstrate that a simple enough functional neuronal model is capable of explaining: i) the extreme selectivity of single neurons to the information content, ii) simultaneous separation of several uncorrelated stimuli , or informational items , from a large set, and iii) dynamic learning of new items by associating them with already "known" ones.
Revised:  We demonstrate that a simple enough functional neuronal model is capable of explaining: i) the extreme selectivity of single neurons to the information content, ii) simultaneous separation of several uncorrelated stimuli , or informational items from a large set, and iii) dynamic learning of new items by associating them with already "known" ones.
Edit intent: fluency