wanyu/IteraTeR_v2 huggingface dataset:
Original:  (2) PCDN can converge to the specified accuracy \epsilon within the limited iteration number of T_\epsilon, and the iteration number T_\epsilon decreases along with the increasing of parallelism (bundle size P).
Revised:  (2) PCDN converges to the specified accuracy \epsilon within the limited iteration number of T_\epsilon, and the iteration number T_\epsilon decreases along with the increasing of parallelism (bundle size P).
Edit intent: clarity
Original:  (2) PCDN can converge to the specified accuracy \epsilon within the limited iteration number of T_\epsilon, and the iteration number T_\epsilon decreases along with the increasing of parallelism (bundle size P).
Revised:  (2) PCDN can converge to the specified accuracy \epsilon within the limited iteration number of T_\epsilon, and T_\epsilon decreases along with the increasing of parallelism (bundle size P).
Edit intent: fluency
Original:  (2) PCDN can converge to the specified accuracy \epsilon within the limited iteration number of T_\epsilon, and the iteration number T_\epsilon decreases along with the increasing of parallelism (bundle size P).
Revised:  (2) PCDN can converge to the specified accuracy \epsilon within the limited iteration number of T_\epsilon, and the iteration number T_\epsilon decreases with increasing parallelism (bundle size P).
Edit intent: clarity
Original:  PCDN is applied to large-scale L_1-regularized logistic regression and L_2-loss SVM. Experimental evaluations over five public datasets indicate that PCDN can better exploit parallelism and outperforms state-of-the-art algorithms in speed , without losing test accuracy.
Revised:  PCDN is applied to L_1-regularized logistic regression and L_2-loss SVM. Experimental evaluations over five public datasets indicate that PCDN can better exploit parallelism and outperforms state-of-the-art algorithms in speed , without losing test accuracy.
Edit intent: clarity
Original:  PCDN is applied to large-scale L_1-regularized logistic regression and L_2-loss SVM. Experimental evaluations over five public datasets indicate that PCDN can better exploit parallelism and outperforms state-of-the-art algorithms in speed , without losing test accuracy.
Revised:  PCDN is applied to large-scale L_1-regularized logistic regression and L_2-loss SVM. Experimental evaluations on five benchmark datasets show that the proposed PCDN algorithm exploits parallelism well and outperforms the state-of-the-art algorithms in speed , without losing test accuracy.
Edit intent: clarity
Original:  PCDN is applied to large-scale L_1-regularized logistic regression and L_2-loss SVM. Experimental evaluations over five public datasets indicate that PCDN can better exploit parallelism and outperforms state-of-the-art algorithms in speed , without losing test accuracy.
Revised:  PCDN is applied to large-scale L_1-regularized logistic regression and L_2-loss SVM. Experimental evaluations over five public datasets indicate that PCDN can better exploit parallelism and outperforms state-of-the-art methods in speed without losing accuracy.
Edit intent: clarity
Original:   For concreteness, the proposed PCDN algorithm is applied to L _1-regularized logistic regression and L _2-loss SVM. Experimental evaluations on five benchmark datasets show that the proposed PCDN algorithm exploits parallelism well and outperforms the state-of-the-art methods in speed without losing accuracy.
Revised:  For concreteness, the proposed PCDN algorithm is applied to \ell _1-regularized logistic regression and L _2-loss SVM. Experimental evaluations on five benchmark datasets show that the proposed PCDN algorithm exploits parallelism well and outperforms the state-of-the-art methods in speed without losing accuracy.
Edit intent: fluency
Original:   For concreteness, the proposed PCDN algorithm is applied to L _1-regularized logistic regression and L _2-loss SVM. Experimental evaluations on five benchmark datasets show that the proposed PCDN algorithm exploits parallelism well and outperforms the state-of-the-art methods in speed without losing accuracy.
Revised:  For concreteness, the proposed PCDN algorithm is applied to L _1-regularized logistic regression and \ell _2-loss SVM. Experimental evaluations on five benchmark datasets show that the proposed PCDN algorithm exploits parallelism well and outperforms the state-of-the-art methods in speed without losing accuracy.
Edit intent: fluency
Original:  In the same year, two different algorithms with the same runtime were given by Koutis and Williams  (ICALP 2009), and Bj\"orklund et al. (ESA 2009), via n^{st/2+O(1)}-time algorithms for counting t-tuples of pairwise disjoint sets drawn from a given family of s-sized subsets of an n-element universe.
Revised:  In the same year, two different algorithms with the same runtime were given by Koutis and Williams ~ (ICALP 2009), and Bj\"orklund et al. (ESA 2009), via n^{st/2+O(1)}-time algorithms for counting t-tuples of pairwise disjoint sets drawn from a given family of s-sized subsets of an n-element universe.
Edit intent: fluency
Original: A novel and deterministic algorithm is presented to detect whether two given planar rational curves are related by means of a similarity, which is a central question in Pattern Recognition.
Revised: A novel and deterministic algorithm is presented to detect whether two given rational plane curves are related by means of a similarity, which is a central question in Pattern Recognition.
Edit intent: clarity
Original: For cross-field and over-time comparisons of citation impacts, bibliometricians normalize the observed citation counts with reference to an expected citation value.
Edit intent: clarity
Original:  In this study, we pursue two related objectives: (1) although different percentile-based approaches have been developed, an approach is hitherto missing that satisfies a number of criteria such as scaling of the percentile ranks from 0 (all other papers perform better) to 100 (all other papers perform worse), and solving the problem with tied citation ranks unambiguously.
Revised:  In this study, we pursue two related objectives: (1) although different percentile-based approaches have been developed, an approach is hitherto missing that satisfies a number of criteria such as scaling of the percentile ranks from zero (all other papers perform better) to 100 (all other papers perform worse), and solving the problem with tied citation ranks unambiguously.
Edit intent: fluency
Original:  This study introduces a new approach having these properties, namely P100. (2) We compare the reliability of our new approach empirically with other percentile-based approaches, among which the approaches developed by the SCImago group, the CWTS , and Thomson Reuters (InCites)  using all papers published in 1980 in  Web of Science  .
Revised:  This study introduces a new approach having these properties, namely P100. (2) We compare the reliability of P100 empirically with other percentile-based approaches, among which the approaches developed by the SCImago group, the CWTS , and Thomson Reuters (InCites) using all papers published in 1980 in Web of Science .
Edit intent: clarity
Original:  This study introduces a new approach having these properties, namely P100. (2) We compare the reliability of our new approach empirically with other percentile-based approaches, among which the approaches developed by the SCImago group, the CWTS , and Thomson Reuters (InCites)  using all papers published in 1980 in  Web of Science  .
Revised:  This study introduces a new approach having these properties, namely P100. (2) We compare the reliability of our new approach empirically with other percentile-based approaches, such as the approaches developed by the SCImago group, the CWTS , and Thomson Reuters (InCites) using all papers published in 1980 in Web of Science .
Edit intent: clarity
Original:  This study introduces a new approach having these properties, namely P100. (2) We compare the reliability of our new approach empirically with other percentile-based approaches, among which the approaches developed by the SCImago group, the CWTS , and Thomson Reuters (InCites)  using all papers published in 1980 in  Web of Science  .
Revised:  This study introduces a new approach having these properties, namely P100. (2) We compare the reliability of our new approach empirically with other percentile-based approaches, among which the approaches developed by the SCImago group, the CWTS , and Thomson Reuters (InCites) , using all papers published in 1980 in Web of Science .
Edit intent: fluency
Original:  This study introduces a new approach having these properties, namely P100. (2) We compare the reliability of our new approach empirically with other percentile-based approaches, among which the approaches developed by the SCImago group, the CWTS , and Thomson Reuters (InCites)  using all papers published in 1980 in  Web of Science  .
Revised:  This study introduces a new approach having these properties, namely P100. (2) We compare the reliability of our new approach empirically with other percentile-based approaches, among which the approaches developed by the SCImago group, the CWTS , and Thomson Reuters (InCites) using all papers published in 1980 in Thomson Reuters Web of Science .
Edit intent: fluency
Original:  This study introduces a new approach having these properties, namely P100. (2) We compare the reliability of our new approach empirically with other percentile-based approaches, among which the approaches developed by the SCImago group, the CWTS , and Thomson Reuters (InCites)  using all papers published in 1980 in  Web of Science  .
Revised:  This study introduces a new approach having these properties, namely P100. (2) We compare the reliability of our new approach empirically with other percentile-based approaches, among which the approaches developed by the SCImago group, the CWTS , and Thomson Reuters (InCites) using all papers published in 1980 in Web of Science (WoS) .
Edit intent: clarity
Original:  How accurately are the different approaches able to predict the long-term citation impact in 2010 (in year 31) using citation impact measured in previous time windows (years 1 to 30)?
Revised:  How accurately can the different approaches able to predict the long-term citation impact in 2010 (in year 31) using citation impact measured in previous time windows (years 1 to 30)?
Edit intent: clarity
Original:  How accurately are the different approaches able to predict the long-term citation impact in 2010 (in year 31) using citation impact measured in previous time windows (years 1 to 30)?
Revised:  How accurately are the different approaches predict the long-term citation impact in 2010 (in year 31) using citation impact measured in previous time windows (years 1 to 30)?
Edit intent: clarity
Original:  The comparison of the percentile-based approaches shows that the method used by InCites overestimates citation impact , whereas the SCImago indicator shows higher power in predicting the long-term citation impact on the basis of citation rates in early years.
Revised:  The comparison of the approaches shows that the method used by InCites overestimates citation impact , whereas the SCImago indicator shows higher power in predicting the long-term citation impact on the basis of citation rates in early years.
Edit intent: clarity
Original:  Since the results point out a disadvantage in this predictive ability for P100 against the other approaches, there is still room for further improvements.
Revised:  Since the results show a disadvantage in this predictive ability for P100 against the other approaches, there is still room for further improvements.
Edit intent: clarity
Original:  In this paper, we posit a feedback mechanism for volatility that reproduces many of the non-trivial properties of empirical prices.
Revised:  In this paper, we posit a feedback mechanism for volatility that closely reproduces the non-trivial properties of empirical prices.
Edit intent: clarity
Original: The 2-Handed Assembly Model (2HAM) is a tile-based self-assembly model in which,  beginning from single tiles, arbitrarily large aggregations of static tiles combine in pairs to form structures.
Revised: The 2-Handed Assembly Model (2HAM) is a tile-based self-assembly model in which, typically beginning from single tiles, arbitrarily large aggregations of static tiles combine in pairs to form structures.
Edit intent: coherence
Original:  In this paper, we prove that there exists a 3D tile set in the 2HAM which is intrinsically universal for the class of all 2D STAM + systems at temperature 1 and 2 (where the STAM + does not make use of the STAM's power of glue deactivation and assembly breaking, as the tile components of the 2HAM are static and unable to change or break bonds).
Revised:  In this paper, we prove that there exists a 3D tile set in the 2HAM which is intrinsically universal for the class of all 2D STAM ^+ systems at temperature 1 and 2 (where the STAM + does not make use of the STAM's power of glue deactivation and assembly breaking, as the tile components of the 2HAM are static and unable to change or break bonds).
Edit intent: clarity
Original:  In this paper, we prove that there exists a 3D tile set in the 2HAM which is intrinsically universal for the class of all 2D STAM + systems at temperature 1 and 2 (where the STAM + does not make use of the STAM's power of glue deactivation and assembly breaking, as the tile components of the 2HAM are static and unable to change or break bonds).
Revised:  In this paper, we prove that there exists a 3D tile set in the 2HAM which is intrinsically universal for the class of all 2D STAM + systems at temperature 1 and 2 (where the STAM ^+ does not make use of the STAM's power of glue deactivation and assembly breaking, as the tile components of the 2HAM are static and unable to change or break bonds).
Edit intent: clarity
Original:  This means that there is a single tile set U in the 3D 2HAM which can, for an arbitrarily complex STAM + system S, be configured with a single input configuration which causes U to exactly simulate S at a scale factor dependent upon S. Furthermore, this simulation uses only 2 planes of the third dimension.
Revised:  This means that there is a single tile set U in the 3D 2HAM which can, for an arbitrarily complex STAM ^+ system S, be configured with a single input configuration which causes U to exactly simulate S at a scale factor dependent upon S. Furthermore, this simulation uses only 2 planes of the third dimension.
Edit intent: fluency
Original:  This means that there is a single tile set U in the 3D 2HAM which can, for an arbitrarily complex STAM + system S, be configured with a single input configuration which causes U to exactly simulate S at a scale factor dependent upon S. Furthermore, this simulation uses only 2 planes of the third dimension.
Revised:  This means that there is a single tile set U in the 3D 2HAM which can, for an arbitrarily complex STAM + system S, be configured with a single input configuration which causes U to exactly simulate S at a scale factor dependent upon S. Furthermore, this simulation uses only two planes of the third dimension.
Edit intent: fluency
Original:  Non-proximate sensory foragers had higher search efficiencies across a range of different resource distributions, and were more robust to changes in resource distribution.
Revised:  We identified optimal search patterns and mode-switching criteria on a variety of resource distributions, and were more robust to changes in resource distribution.
Edit intent: clarity
Original:  We also explore how the search strategies predicted by our model can help bridge the gap between random search theory and traditional patch-use foraging theory.
Revised:  The search strategies predicted by our model can help bridge the gap between random search theory and traditional patch-use foraging theory.
Edit intent: coherence
Original:  We then instantiate a completely solvable model illustrating , at different scales, the role of such random architectures in macroscopic activity emerging of such large neuronal networks .
Revised:  We then instantiate a completely solvable model illustrating the role of such random architectures in macroscopic activity emerging of such large neuronal networks .
Edit intent: clarity
Original:  We then instantiate a completely solvable model illustrating , at different scales, the role of such random architectures in macroscopic activity emerging of such large neuronal networks .
Revised:  We then instantiate a completely solvable model illustrating , at different scales, the role of such random architectures in the emerging macroscopic activity .
Edit intent: clarity
Original: A list M-partition of a graph G is associated with a symmetric D-by-D matrix over {0,1,*} . It is a partition of the vertices of G into D parts which are associated with the rows of M.
Revised: Given a symmetric D*D matrix M over {0,1,*} . It is a partition of the vertices of G into D parts which are associated with the rows of M.
Edit intent: clarity
Original: A list M-partition of a graph G is associated with a symmetric D-by-D matrix over {0,1,*} . It is a partition of the vertices of G into D parts which are associated with the rows of M.
Revised: A list M-partition of a graph G is associated with a symmetric D-by-D matrix over {0,1,*} , a list M-partition of a graph G is a partition of the vertices of G into D parts which are associated with the rows of M.
Edit intent: coherence
Original:  The part of each vertex is chosen from a given list in such a way that no edge of G is mapped to a 0 in M and no non-edge of G is mapped to a 1. Many important graph-theoretic structures can be represented as list M-partitions including graph colourings, split graphs and homogeneous sets  .
Revised:  The part of each vertex is chosen from a given list in such a way that no edge of G is mapped to a 0 in M and no non-edge of G is mapped to a 1 in M. Many important graph-theoretic structures can be represented as list M-partitions including graph colourings, split graphs and homogeneous sets .
Edit intent: fluency
Original:  The part of each vertex is chosen from a given list in such a way that no edge of G is mapped to a 0 in M and no non-edge of G is mapped to a 1. Many important graph-theoretic structures can be represented as list M-partitions including graph colourings, split graphs and homogeneous sets  .
Revised:  The part of each vertex is chosen from a given list in such a way that no edge of G is mapped to a 0 in M and no non-edge of G is mapped to a 1. Many important graph-theoretic structures can be represented as list M-partitions including graph colourings, split graphs and homogeneous sets and pair .
Edit intent: coherence
Original:  Thus, there has been quite a bit of work on determining for which matrices  computations involving list M-partitions are tractable.
Revised:  Thus, there has been quite a bit of work on determining for which matrices M computations involving list M-partitions are tractable.
Edit intent: fluency
Original:  Furthermore, we give an explicit characterisation of the dichotomy theorem - counting list M-partitions is tractable (in FP) if and only if the matrix M has a structure called a derectangularising sequence.
Revised:  Furthermore, we give an explicit characterisation of the dichotomy theorem : counting list M-partitions is tractable (in FP) if and only if the matrix M has a structure called a derectangularising sequence.
Edit intent: fluency
Original:  Finally, we show that the meta-problem of determining whether a given matrix has a derectangularising sequence is NP-complete.
Revised:  We show that the meta-problem of determining whether a given matrix has a derectangularising sequence is NP-complete.
Edit intent: coherence
Original: Given a symmetric D*D matrix M over {0,1,*}, a list M-partition of a graph G is a partition of the vertices of G into D parts which are associated with the rows of M.
Revised: Given a symmetric D*D matrix M over {0,1,*}, a list M-partition of a graph G is a partition of G's vertices into D parts which are associated with the rows of M.
Edit intent: clarity
Original: Given a symmetric D*D matrix M over {0,1,*}, a list M-partition of a graph G is a partition of the vertices of G into D parts which are associated with the rows of M.
Revised: Given a symmetric D*D matrix M over {0,1,*}, a list M-partition of a graph G is a partition of the vertices of G into D parts associated with the rows of M.
Edit intent: clarity
Original:  The part of each vertex is chosen from a given list in such a way that no edge of G is mapped to a 0 in M and no non-edge of G is mapped to a 1 in M. Many important graph-theoretic structures can be represented as list M-partitions including graph colourings, split graphs and homogeneous sets and pair.
Revised:  The part of each vertex is chosen from a given list so that no edge of G is mapped to a 0 in M and no non-edge of G is mapped to a 1 in M. Many important graph-theoretic structures can be represented as list M-partitions including graph colourings, split graphs and homogeneous sets and pair.
Edit intent: clarity
Original:  The part of each vertex is chosen from a given list in such a way that no edge of G is mapped to a 0 in M and no non-edge of G is mapped to a 1 in M. Many important graph-theoretic structures can be represented as list M-partitions including graph colourings, split graphs and homogeneous sets and pair.
Revised:  The part of each vertex is chosen from a given list in such a way that no edge of G maps to a 0 in M and no non-edge of G is mapped to a 1 in M. Many important graph-theoretic structures can be represented as list M-partitions including graph colourings, split graphs and homogeneous sets and pair.
Edit intent: fluency
Original:  The part of each vertex is chosen from a given list in such a way that no edge of G is mapped to a 0 in M and no non-edge of G is mapped to a 1 in M. Many important graph-theoretic structures can be represented as list M-partitions including graph colourings, split graphs and homogeneous sets and pair.
Revised:  The part of each vertex is chosen from a given list in such a way that no edge of G is mapped to a 0 in M and no non-edge of G maps to a 1 in M. Many important graph-theoretic structures can be represented as list M-partitions including graph colourings, split graphs and homogeneous sets and pair.
Edit intent: fluency
Original:  The part of each vertex is chosen from a given list in such a way that no edge of G is mapped to a 0 in M and no non-edge of G is mapped to a 1 in M. Many important graph-theoretic structures can be represented as list M-partitions including graph colourings, split graphs and homogeneous sets and pair.
Revised:  The part of each vertex is chosen from a given list in such a way that no edge of G is mapped to a 0 in M and no non-edge of G is mapped to a 1 in M. Many important graph-theoretic structures can be represented as list M-partitions , such as graph colourings, split graphs and homogeneous sets and pair.
Edit intent: fluency
Original:  This paper focuses on the problem of counting list M-partitions, given a graph G and given lists for each vertex of G. We  give an algorithm that solves this problem in polynomial time for every (fixed) matrix M for which the problem is tractable .
Revised:  We focus on counting list M-partitions, given a graph G and given lists for each vertex of G. We give an algorithm that solves this problem in polynomial time for every (fixed) matrix M for which the problem is tractable .
Edit intent: clarity
Original:  This paper focuses on the problem of counting list M-partitions, given a graph G and given lists for each vertex of G. We  give an algorithm that solves this problem in polynomial time for every (fixed) matrix M for which the problem is tractable .
Revised:  This paper focuses on the problem of counting list M-partitions, given a graph G and a list for each vertex of G. We give an algorithm that solves this problem in polynomial time for every (fixed) matrix M for which the problem is tractable .
Edit intent: clarity