wanyu/IteraTeR_v2 huggingface dataset:
Original:  Therefore, a pertinent protein-specific pre-training task is necessary to better capture the information contained within the protein sequences.
Revised:  Therefore, a complementary protein-specific pre-training task is necessary to better capture the information contained within the protein sequences.
Edit intent: clarity
Original:  Therefore, a pertinent protein-specific pre-training task is necessary to better capture the information contained within the protein sequences.
Revised:  Therefore, a pertinent protein-specific task for pre-training is necessary to better capture the information contained within the protein sequences.
Edit intent: clarity
Original:  Therefore, a pertinent protein-specific pre-training task is necessary to better capture the information contained within the protein sequences.
Revised:  Therefore, a pertinent protein-specific pre-training task is necessary to better capture the information contained within unlabeled protein sequences.
Edit intent: clarity
Original:  Furthermore, we present results from our ablation studies and qualitative interpretation analyses to better understand the strengths of PLUS-RNN. Availability: The codes and pre-trained models are available at URL
Revised:  Furthermore, we present results from our ablation studies and interpretation analyses to better understand the strengths of PLUS-RNN. Availability: The codes and pre-trained models are available at URL
Edit intent: clarity
Original: Motivation: Bridging the exponentially growing gap between the number of unlabeled and labeled proteins, a couple of works have adopted semi-supervised learning for protein sequence modeling.
Revised:  Bridging the exponentially growing gap between the number of unlabeled and labeled proteins, a couple of works have adopted semi-supervised learning for protein sequence modeling.
Edit intent: coherence
Original: Motivation: Bridging the exponentially growing gap between the number of unlabeled and labeled proteins, a couple of works have adopted semi-supervised learning for protein sequence modeling.
Revised: Motivation: Bridging the exponentially growing gap between the numbers of unlabeled and labeled proteins, a couple of works have adopted semi-supervised learning for protein sequence modeling.
Edit intent: fluency
Original: Motivation: Bridging the exponentially growing gap between the number of unlabeled and labeled proteins, a couple of works have adopted semi-supervised learning for protein sequence modeling.
Revised: Motivation: Bridging the exponentially growing gap between the number of unlabeled and labeled protein sequences, several studies adopted semi-supervised learning for protein sequence modeling.
Edit intent: clarity
Original:  They pre-train a model with a substantial amount of unlabeled data and transfer the learned representations to various downstream tasks.
Revised:  In these studies, models were pre-trained with a substantial amount of unlabeled data and transfer the learned representations to various downstream tasks.
Edit intent: clarity
Original:  They pre-train a model with a substantial amount of unlabeled data and transfer the learned representations to various downstream tasks.
Revised:  They pre-train a model with a substantial amount of unlabeled data , and the representations were transferred to various downstream tasks.
Edit intent: clarity
Original:  Nonetheless, the current pre-training methods mostly rely on a language modeling task and often show limited performances.
Revised:  Most pre-training methods mostly rely on a language modeling task and often show limited performances.
Edit intent: clarity
Original:  PLUS consists of masked language modeling and a complementary protein-specific pre-training task, namely same family prediction.
Revised:  PLUS consists of masked language modeling and a complementary protein-specific pre-training task, namely same-family prediction.
Edit intent: fluency
Original:  In this work, we mainly use PLUS to pre-train a  recurrent neural network (RNN) and refer to the resulting model as PLUS-RNN. It advances state-of-the-art pre-training methods on six out of seven tasks, i.e., (1) three protein (-pair)-level classification, (2) two protein-level regression, and (3) two amino-acid-level classification tasks.
Revised:  In this work, we use PLUS to pre-train a recurrent neural network (RNN) and refer to the resulting model as PLUS-RNN. It advances state-of-the-art pre-training methods on six out of seven tasks, i.e., (1) three protein (-pair)-level classification, (2) two protein-level regression, and (3) two amino-acid-level classification tasks.
Edit intent: coherence
Original:  In this work, we mainly use PLUS to pre-train a  recurrent neural network (RNN) and refer to the resulting model as PLUS-RNN. It advances state-of-the-art pre-training methods on six out of seven tasks, i.e., (1) three protein (-pair)-level classification, (2) two protein-level regression, and (3) two amino-acid-level classification tasks.
Revised:  In this work, we mainly use PLUS to pre-train a recurrent neural network and refer to the resulting model as PLUS-RNN. It advances state-of-the-art pre-training methods on six out of seven tasks, i.e., (1) three protein (-pair)-level classification, (2) two protein-level regression, and (3) two amino-acid-level classification tasks.
Edit intent: clarity
Original:  In this work, we mainly use PLUS to pre-train a  recurrent neural network (RNN) and refer to the resulting model as PLUS-RNN. It advances state-of-the-art pre-training methods on six out of seven tasks, i.e., (1) three protein (-pair)-level classification, (2) two protein-level regression, and (3) two amino-acid-level classification tasks.
Revised:  In this work, we mainly use PLUS to pre-train a recurrent neural network (RNN) and refer to the resulting model as PLUS-RNN. It advances state-of-the-art pre-training methods on six out of seven widely used protein biology tasks.
Edit intent: clarity
Original:  Furthermore, we present  results from our ablation studies and interpretation analyses to better understand the strengths of PLUS-RNN. Availability: The codes and pre-trained models are available at URL
Revised:  Furthermore, we present the results from our ablation studies and interpretation analyses to better understand the strengths of PLUS-RNN. Availability: The codes and pre-trained models are available at URL
Edit intent: fluency
Original:  Furthermore, we present  results from our ablation studies and interpretation analyses to better understand the strengths of PLUS-RNN. Availability: The codes and pre-trained models are available at URL
Revised:  Furthermore, we present results from our qualitative interpretation analyses to better understand the strengths of PLUS-RNN. Availability: The codes and pre-trained models are available at URL
Edit intent: clarity
Original:  Furthermore, we present  results from our ablation studies and interpretation analyses to better understand the strengths of PLUS-RNN. Availability: The codes and pre-trained models are available at URL
Revised:  Furthermore, we present results from our ablation studies and interpretation analyses to illustrate the strengths of PLUS-RNN. Availability: The codes and pre-trained models are available at URL
Edit intent: clarity
Original: We introduce "instability analysis," a framework for assessing whether the outcome of optimizing a neural network is robust to SGD noise.
Revised: We introduce "instability analysis," which assesses whether a neural network is robust to SGD noise.
Edit intent: clarity
Original:  For example, standard vision models are initially unstable but become stable early in training ;
Revised:  For example, standard vision models become "stable" in this way early in training ;
Edit intent: clarity
Original:  We leverage instability analysis to examine iterative magnitude pruning  (IMP), the procedure underlying the lottery ticket hypothesis .
Revised:  We leverage instability analysis to examine iterative magnitude pruning " (IMP), the procedure underlying the lottery ticket hypothesis .
Edit intent: fluency
Original:  On small vision tasks, IMP finds sparse "matching subnetworks " that can train in isolation from initialization to full accuracy , but it fails to do so in more challenging settings .
Revised:  On small vision tasks, IMP finds sparse "matching subnetworks " that can train in isolation from initialization to full accuracy from initialization .
Edit intent: clarity
Original:  We find that IMP subnetworks are matching only when they are stable .
Revised:  We find that these subnetworks only reach full accuracy when they are stable .
Edit intent: clarity
Original:  We augment IMP to rewind subnetworks to their weights early in training, producing sparse subnetworks of large-scale networks, including Resnet-50 for ImageNet, that train to full accuracy .
Revised:  We augment IMP to rewind subnetworks to their weights early in training, producing sparse subnetworks of large-scale settings ( Resnet-50 for ImageNet, that train to full accuracy .
Edit intent: clarity
Original:  This submission subsumes 1903.01611 ("Stabilizing the Lottery Ticket Hypothesis" and "The Lottery Ticket Hypothesis at Scale") .
Revised:  This submission subsumes 1903.01611 ("Stabilizing the Lottery Ticket Hypothesis" and "The Lottery Ticket Hypothesis at Scale") 
Edit intent: fluency
Original: We introduce "instability analysis," which assesses whether a neural network optimizes to the same, linearly connected minimum under different samples of SGD noise .
Revised: We study whether a neural network optimizes to the same, linearly connected minimum under different samples of SGD noise .
Edit intent: clarity
Original:  From then on, the outcome of optimization is determined to within a linearly connected region.
Revised:  From then on, the outcome of optimization is determined to a linearly connected region.
Edit intent: clarity
Original:  We use instability to study " iterative magnitude pruning " (IMP), the procedure used by work on the lottery ticket hypothesis to identify subnetworks that could have trained  to full accuracy from initialization .
Revised:  We use this technique to study iterative magnitude pruning " (IMP), the procedure used by work on the lottery ticket hypothesis to identify subnetworks that could have trained to full accuracy from initialization .
Edit intent: clarity
Original:  We use instability to study " iterative magnitude pruning " (IMP), the procedure used by work on the lottery ticket hypothesis to identify subnetworks that could have trained  to full accuracy from initialization .
Revised:  We use instability to study " iterative magnitude pruning (IMP), the procedure used by work on the lottery ticket hypothesis to identify subnetworks that could have trained to full accuracy from initialization .
Edit intent: fluency
Original:  We use instability to study " iterative magnitude pruning " (IMP), the procedure used by work on the lottery ticket hypothesis to identify subnetworks that could have trained  to full accuracy from initialization .
Revised:  We use instability to study " iterative magnitude pruning " (IMP), the procedure used by work on the lottery ticket hypothesis to identify subnetworks that could have trained to full accuracy .
Edit intent: clarity
Original:  We find that these subnetworks only reach full accuracy when they are stable  , which either occurs at initialization for small-scale settings (MNIST) or early in training for large-scale settings ( Resnet-50 and Inception-v3 on ImageNet).
Revised:  We find that these subnetworks only reach full accuracy when they are stable , which either occurs at initialization for small-scale settings (MNIST) or early in training for large-scale settings ( ResNet-50 and Inception-v3 on ImageNet).
Edit intent: fluency
Original:  This submission subsumes 1903.01611 ("Stabilizing the Lottery Ticket Hypothesis" and "The Lottery Ticket Hypothesis at Scale")
Edit intent: clarity
Original:  We design a simplex algorithm that , given an initial vertex, reaches an optimal vertex by tracing a path along the edges of P of length at most O(n ^6 klog k ).
Revised:  We design a simplex algorithm that reaches an optimal vertex by tracing a path along the edges of P of length at most O(n ^6 klog k ).
Edit intent: clarity
Original:  We design a simplex algorithm that , given an initial vertex, reaches an optimal vertex by tracing a path along the edges of P of length at most O(n ^6 klog k ).
Revised:  We design a simplex algorithm that , given an initial vertex, reaches an optimal vertex by tracing a path along the edges of P of length in O(n ^6 klog k ).
Edit intent: clarity
Original:  The length of this path is independent on m and is the best possible up to a polynomial function , since it is only polynomially far from the worst case diameter.
Revised:  The length of this path is independent from m and it is the best possible up to a polynomial function , since it is only polynomially far from the worst case diameter.
Edit intent: fluency
Original:  The length of this path is independent on m and is the best possible up to a polynomial function , since it is only polynomially far from the worst case diameter.
Revised:  The length of this path is independent on m and is the best possible up to a polynomial function . In fact, it is only polynomially far from the worst case diameter.
Edit intent: coherence
Original:  The\pm  number of arithmetic operations needed to compute the next vertex in the path is polynomial in n, m and log k.
Revised:  The\pm number of arithmetic operations needed to compute the next vertex in the path is polynomial in n, m and \log k.
Edit intent: fluency
Original:  Unfortunately, the finite element discretization for these problems suffer from significant numerical pollution errors that increase with the wavenumber.
Revised:  Unfortunately, the finite element discretization for these problems suffers from significant numerical pollution errors that increase with the wavenumber.
Edit intent: fluency
Original:  Anal. 49.3 ( 2011 ), pp. 1210-1243 ).
Revised:  Anal. 49.3 ( 2011 ).
Edit intent: clarity
Original:  For standard power basis our algorithms sample at N = \lfloor 4{3} E + 2 \rfloor B points and for Chebyshev basis at N =%DIFDELCMD < \lfloor %DIFDELCMD < %%% \frac{3{2} E + 2 }%DIFDELCMD < \rfloor %%% B points.
Revised:  For standard power basis our algorithms sample at N = \lfloor 4{3} E + 2 \rfloor B points %DIFDELCMD < \lfloor %DIFDELCMD < %%% \frac{3{2} E + 2 }%DIFDELCMD < \rfloor %%% B points.
Edit intent: coherence
Original:  For standard power basis our algorithms sample at N = \lfloor 4{3} E + 2 \rfloor B points and for Chebyshev basis at N =%DIFDELCMD < \lfloor %DIFDELCMD < %%% \frac{3{2} E + 2 }%DIFDELCMD < \rfloor %%% B points.
Revised:  For standard power basis our algorithms sample at N = \lfloor 4{3} E + 2 \rfloor B points and for Chebyshev basis at N =%DIFDELCMD < \lfloor %DIFDELCMD < %%% {2} E + 2 }%DIFDELCMD < \rfloor %%% B points.
Edit intent: coherence
Original:  Those are fewer points than N = 2(E+1)B - 1 in Kaltofen and Pernet , Proc.
Revised:  Those are fewer points than N = 2(E+1)B - 1 given by Kaltofen and Pernet , Proc.
Edit intent: clarity
Original:  ISSAC 2014 for standard power basis and fewer than in \lfloor {2} E + 2 }\rfloor  Arnold and Kaltofen , Proc.
Revised:  ISSAC 2014 in \lfloor {2} E + 2 }\rfloor Arnold and Kaltofen , Proc.
Edit intent: clarity
Original:  ISSAC 2015 for Chebyshev basis, where only the cases B%DIFDELCMD < \le %%% \lfloor {13} + 1 }\rfloor  3 have explicit counts for N.\ge  Our method shows how to correct 2 errors in a block of 4B points for standard basis and how to correct 1 error in a block of 3B points for Chebyshev Basis.
Revised:  ISSAC 2015 %DIFDELCMD < \le %%% \lfloor {13} + 1 }\rfloor 3 have explicit counts for N.\ge Our method shows how to correct 2 errors in a block of 4B points for standard basis and how to correct 1 error in a block of 3B points for Chebyshev Basis.
Edit intent: coherence
Original:  ISSAC 2015 for Chebyshev basis, where only the cases B%DIFDELCMD < \le %%% \lfloor {13} + 1 }\rfloor  3 have explicit counts for N.\ge  Our method shows how to correct 2 errors in a block of 4B points for standard basis and how to correct 1 error in a block of 3B points for Chebyshev Basis.
Revised:  ISSAC 2015 for Chebyshev basis, where only the cases B%DIFDELCMD < \le %%% \lfloor {13} + 1 }\rfloor 3 and E\ge Our method shows how to correct 2 errors in a block of 4B points for standard basis and how to correct 1 error in a block of 3B points for Chebyshev Basis.
Edit intent: clarity
Original:  The proposed method employs two complementary teams of agents: one agile in detecting (hunters) and another dexterous in completing (gatherers) the tasks.
Revised:  The proposed method employs two complementary teams of agents: one agile in detecting (hunters) and another skillful in completing (gatherers) the tasks.
Edit intent: clarity
Original: Functional error estimates are well-estabilished tools for a posteriori error estimation and related adaptive mesh-refinement for the finite element method (FEM).
Revised: Functional error estimates are well-established tools for a posteriori error estimation and related adaptive mesh-refinement for the finite element method (FEM).
Edit intent: clarity
Original:  In particular, our analysis covers Galerkin BEM as well as collocation , which makes our approach of particular interest for people working in engineering .
Revised:  In particular, our analysis covers Galerkin BEM and the collocation method, what makes the approach of particular interest for people working in engineering .
Edit intent: clarity
Original:  In particular, our analysis covers Galerkin BEM as well as collocation , which makes our approach of particular interest for people working in engineering .
Revised:  In particular, our analysis covers Galerkin BEM as well as collocation , which makes our approach of particular interest for scientific computations and engineering applications .
Edit intent: clarity
Original: The lexicographic method is a technique that was introduced by Hell and Huang Journal of Graph Theory, 20(3):361--374, 1995%DIFDELCMD < ] %%% as a way to simplify the problems of recognizing and obtaining representations of comparability graphs, proper circular-arc graphs and proper interval graphs.
Revised:  Journal of Graph Theory, 20(3):361--374, 1995%DIFDELCMD < ] %%% as a way to simplify the problems of recognizing and obtaining representations of comparability graphs, proper circular-arc graphs and proper interval graphs.
Edit intent: coherence
Original: The lexicographic method is a technique that was introduced by Hell and Huang Journal of Graph Theory, 20(3):361--374, 1995%DIFDELCMD < ] %%% as a way to simplify the problems of recognizing and obtaining representations of comparability graphs, proper circular-arc graphs and proper interval graphs.
Revised: The lexicographic method is a technique that was introduced by Hell and Huang %DIFDELCMD < ] %%% as a way to simplify the problems of recognizing and obtaining representations of comparability graphs, proper circular-arc graphs and proper interval graphs.
Edit intent: coherence
Original:  Chv\'atal and Hammer conjectured in 1977 that given a graph G, a suitably constructed auxiliary graph G ' has chromatic number equal to the minimum size of a threshold cover of G. Although Cozzens and Leibowitz showed that this conjecture is false in the general case, Raschle and Simon  [Proceedings of the Twenty-seventh Annual ACM Symposium on Theory of Computing, STOC '95, pages 650--661, 1995]
Revised:  Chv\'atal and Hammer , in 1977 that given a graph G, a suitably constructed auxiliary graph G ' has chromatic number equal to the minimum size of a threshold cover of G. Although Cozzens and Leibowitz showed that this conjecture is false in the general case, Raschle and Simon [Proceedings of the Twenty-seventh Annual ACM Symposium on Theory of Computing, STOC '95, pages 650--661, 1995]
Edit intent: clarity
Original:  Chv\'atal and Hammer conjectured in 1977 that given a graph G, a suitably constructed auxiliary graph G ' has chromatic number equal to the minimum size of a threshold cover of G. Although Cozzens and Leibowitz showed that this conjecture is false in the general case, Raschle and Simon  [Proceedings of the Twenty-seventh Annual ACM Symposium on Theory of Computing, STOC '95, pages 650--661, 1995]
Revised:  Chv\'atal and Hammer conjectured in 1977 that given a graph G, a threshold cover of G. Although Cozzens and Leibowitz showed that this conjecture is false in the general case, Raschle and Simon [Proceedings of the Twenty-seventh Annual ACM Symposium on Theory of Computing, STOC '95, pages 650--661, 1995]
Edit intent: clarity
Original:  Chv\'atal and Hammer conjectured in 1977 that given a graph G, a suitably constructed auxiliary graph G ' has chromatic number equal to the minimum size of a threshold cover of G. Although Cozzens and Leibowitz showed that this conjecture is false in the general case, Raschle and Simon  [Proceedings of the Twenty-seventh Annual ACM Symposium on Theory of Computing, STOC '95, pages 650--661, 1995]
Revised:  Chv\'atal and Hammer conjectured in 1977 that given a graph G, a suitably constructed auxiliary graph G ' has chromatic number equal to the minimum size of a threshold cover of G. Although Cozzens and Leibowitz showed that this conjecture is false in the general case, Raschle and Simon ~ [Proceedings of the Twenty-seventh Annual ACM Symposium on Theory of Computing, STOC '95, pages 650--661, 1995]
Edit intent: fluency
Original: Smart contracts are automated or self-enforcing contracts that can be used to exchange money, property, or anything of value without having to place trust in third parties.
Revised: Smart contracts are automated or self-enforcing contracts that can be used to exchange assets without having to place trust in third parties.
Edit intent: clarity
Original:  Many commercial transactions presently make use of smart contracts due to their potential benefits in enabling parties to engage in secure peer-to-peer transactions independent of external parties.
Revised:  Many commercial transactions use smart contracts due to their potential benefits in enabling parties to engage in secure peer-to-peer transactions independent of external parties.
Edit intent: clarity
Original:  Many commercial transactions presently make use of smart contracts due to their potential benefits in enabling parties to engage in secure peer-to-peer transactions independent of external parties.
Revised:  Many commercial transactions presently make use of smart contracts due to their potential benefits in terms of secure peer-to-peer transactions independent of external parties.
Edit intent: clarity
Original:  Experience shows that many commonly used smart contracts are vulnerable to serious malicious attacks which may enable attackers to steal valuable assets of involved parties.
Revised:  Experience shows that many commonly used smart contracts are vulnerable to serious malicious attacks which may enable attackers to steal valuable assets of involving parties.
Edit intent: fluency
Original:  Our repair method is search-based and  considers the gas usage of the candidate patches via leveraging our novel notation of\emph{gas dominance relationship .
Revised:  Our repair method is search-based and considers the gas usage of the candidate patches by leveraging our novel notation of\emph{gas dominance relationship .
Edit intent: fluency
Original:  Our repair method is search-based and  considers the gas usage of the candidate patches via leveraging our novel notation of\emph{gas dominance relationship .
Revised:  Our repair method is search-based and considers the gas usage of the candidate patches via leveraging our novel \emph{gas dominance relationship .
Edit intent: clarity
Original:  Our repair method is search-based and  considers the gas usage of the candidate patches via leveraging our novel notation of\emph{gas dominance relationship .
Revised:  Our repair method is search-based and considers the gas usage of the candidate patches via leveraging our novel notation of\emph{ .
Edit intent: clarity