wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In addition, we can incorporate a box constraint that is reasonable for imaging applications . Numerical schemes are based on the alternating direction method of multipliers (ADMM), and we provide the convergence analysis of all the proposed algorithms (constrained/unconstrained and with/without the box constraint ). Experimental results demonstrate the efficiency of our proposed approaches, showing significant improvements over the state-of-the-art methods in the limit-angle CT reconstruction.
Revised:  In addition, we can incorporate a box constraint that is reasonable for imaging applications , and the convergence for the additional box constraint can also be established. Numerical results on both synthetic and experimental datasets demonstrate the efficiency of our proposed approaches, showing significant improvements over the state-of-the-art methods in the limit-angle CT reconstruction.
Edit intent: meaning-changed
Original:  Experimental results demonstrate the efficiency of our proposed approaches, showing significant improvements over the state-of-the-art methods in the limit-angle CT reconstruction. Specifically worth noticing is an exact recovery of the Shepp-Logan phantom from noiseless projection data with 30 scanning angle .
Revised:  Experimental results demonstrate the efficiency of our proposed approaches, showing significant improvements over the state-of-the-art methods in the limited-angle CT reconstruction .
Edit intent: coherence
Original: This paper concerns optimal error estimates for Legendre polynomial expansions of singular functions whose regularities are naturally characterised by a certain fractional Sobolev-type space introduced in 34, Math. Comput., 2019%DIFDELCMD < ]%%% . The regularity is quantified as the Riemann-Liouville (RL) fractional integration (of order 1-s\in (0,1)) of the highest possible integer-order derivative (of order m) of the underlying singular function that is of bounded variation.
Revised: This paper concerns optimal error estimates for Legendre polynomial expansions of singular functions whose regularities are naturally characterised by a certain fractional Sobolev-type space introduced in %DIFDELCMD < ]%%% . The regularity is quantified as the Riemann-Liouville (RL) fractional integration (of order 1-s\in (0,1)) of the highest possible integer-order derivative (of order m) of the underlying singular function that is of bounded variation.
Edit intent: coherence
Original:  Comput., 2019%DIFDELCMD < ]%%% . The regularity is quantified as the Riemann-Liouville (RL) fractional integration (of order 1-s\in (0,1)) of the highest possible integer-order derivative (of order m) of the underlying singular function that is of bounded variation.
Revised:  Comput., 2019%DIFDELCMD < ]%%% Caputo fractional derivatives are of bounded variation.
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Original:  Different from Chebyshev approximation, the usual L^\infty-estimate is non-optimal for functions with interior singularities. However, we show that the optimality can be achieved in a certain weightedL^\infty-sense. We also provide point-wise error estimates that can answer some open questions posed in several recent literature.
Revised:  It bridges and ``interpolates" the usual Taylor formulas with two consecutive integer orders. This enables us to obtain an analogous formula for the Legendre expansion coefficient of this type of singular functions, and further derive the optimal (weighted) L^\infty-estimates and L^2-estimates of the Legendre polynomial approximations. This set of results can enrich the existing theory for p and hp methods for singular problems, and answer some open questions posed in several recent literature.
Edit intent: meaning-changed
Original: Conversational Machine Comprehension (CMC) is a research track in conversational AI which expects the machine to understand an open-domain  text and thereafter engage in a multi-turn conversation to answer questions related to the text.
Revised: Conversational Machine Comprehension (CMC) , a research track in conversational AI which expects the machine to understand an open-domain text and thereafter engage in a multi-turn conversation to answer questions related to the text.
Edit intent: coherence
Original: Conversational Machine Comprehension (CMC) is a research track in conversational AI which expects the machine to understand an open-domain  text and thereafter engage in a multi-turn conversation to answer questions related to the text.
Revised: Conversational Machine Comprehension (CMC) is a research track in conversational AI , expects the machine to understand an open-domain text and thereafter engage in a multi-turn conversation to answer questions related to the text.
Edit intent: coherence
Original: Conversational Machine Comprehension (CMC) is a research track in conversational AI which expects the machine to understand an open-domain  text and thereafter engage in a multi-turn conversation to answer questions related to the text.
Revised: Conversational Machine Comprehension (CMC) is a research track in conversational AI which expects the machine to understand an open-domain natural language text and thereafter engage in a multi-turn conversation to answer questions related to the text.
Edit intent: meaning-changed
Original:  While most of the research in Machine Reading Comprehension (MRC) revolves around single-turn question answering  , multi-turn CMC has recently gained prominence, thanks to the advancement in natural language understanding via neural language models like BERT and the introduction of large-scale conversational datasets like CoQA and QuAC.
Revised:  While most of the research in Machine Reading Comprehension (MRC) revolves around single-turn question answering (QA) , multi-turn CMC has recently gained prominence, thanks to the advancement in natural language understanding via neural language models like BERT and the introduction of large-scale conversational datasets like CoQA and QuAC.
Edit intent: fluency
Original:  While most of the research in Machine Reading Comprehension (MRC) revolves around single-turn question answering  , multi-turn CMC has recently gained prominence, thanks to the advancement in natural language understanding via neural language models like BERT and the introduction of large-scale conversational datasets like CoQA and QuAC.
Revised:  While most of the research in Machine Reading Comprehension (MRC) revolves around single-turn question answering , multi-turn CMC has recently gained prominence, thanks to the advancement in natural language understanding via neural language models such as BERT and the introduction of large-scale conversational datasets like CoQA and QuAC.
Edit intent: fluency
Original:  While most of the research in Machine Reading Comprehension (MRC) revolves around single-turn question answering  , multi-turn CMC has recently gained prominence, thanks to the advancement in natural language understanding via neural language models like BERT and the introduction of large-scale conversational datasets like CoQA and QuAC.
Revised:  While most of the research in Machine Reading Comprehension (MRC) revolves around single-turn question answering , multi-turn CMC has recently gained prominence, thanks to the advancement in natural language understanding via neural language models like BERT and the introduction of large-scale conversational datasets such as CoQA and QuAC.
Edit intent: fluency
Original:  This literature review , therefore, is a first-of-its-kind attempt at providing a holistic overview of CMC , with an emphasis on the common trends across recently published models, specifically in their approach to tackling conversational history.
Revised:  This literature review , therefore, is a first-of-its-kind attempt at providing a holistic overview of CMC with an emphasis on the common trends across recently published models, specifically in their approach to tackling conversational history.
Edit intent: fluency
Original:  mainly serine and methyl-asparate (MeAsp), in a one-dimensional and a two-dimensional environment . Under suitable conditions, we show that if the ratio of the main chemoreceptors of individual cells, namely Tar/Tsr is less than a specific threshold, the bacteria move to the gradient of serine, and if the ratio is greater than the threshold, the group of bacteria move toward the gradient of MeAsp.
Revised:  mainly serine and methyl-aspartate (MeAsp), in a one-dimensional and a two-dimensional environment . Under suitable conditions, we show that if the ratio of the main chemoreceptors of individual cells, namely Tar/Tsr is less than a specific threshold, the bacteria move to the gradient of serine, and if the ratio is greater than the threshold, the group of bacteria move toward the gradient of MeAsp.
Edit intent: fluency
Original:  mainly serine and methyl-asparate (MeAsp), in a one-dimensional and a two-dimensional environment . Under suitable conditions, we show that if the ratio of the main chemoreceptors of individual cells, namely Tar/Tsr is less than a specific threshold, the bacteria move to the gradient of serine, and if the ratio is greater than the threshold, the group of bacteria move toward the gradient of MeAsp.
Revised:  mainly serine and methyl-asparate (MeAsp), in a one-dimensional and a two-dimensional environment , which is inspired by experimental results in Y. Kalinin et al., J. Bacteriol. 192(7):1796-1800, 2010. Under suitable conditions, we show that if the ratio of the main chemoreceptors of individual cells, namely Tar/Tsr is less than a specific threshold, the bacteria move to the gradient of serine, and if the ratio is greater than the threshold, the group of bacteria move toward the gradient of MeAsp.
Edit intent: meaning-changed
Original:  a second order stochastic optimization algorithm which dynamically incorporates the curvature of the loss function via ADAptive estimates of the Hessian . Second order algorithms are among the most powerful optimization algorithms with superior convergence properties as compared to first order methods such as SGD and ADAM . The main disadvantage of traditional second order methods is their heavier per-iteration computation and poor accuracy as compared to first order methods.
Revised:  a second order stochastic optimization algorithm which dynamically incorporates the curvature of the loss function via ADAptive estimates of the HESSIAN . Second order algorithms are among the most powerful optimization algorithms with superior convergence properties as compared to first order methods such as SGD and ADAM . The main disadvantage of traditional second order methods is their heavier per-iteration computation and poor accuracy as compared to first order methods.
Edit intent: fluency
Original:  a second order stochastic optimization algorithm which dynamically incorporates the curvature of the loss function via ADAptive estimates of the Hessian . Second order algorithms are among the most powerful optimization algorithms with superior convergence properties as compared to first order methods such as SGD and ADAM . The main disadvantage of traditional second order methods is their heavier per-iteration computation and poor accuracy as compared to first order methods.
Revised:  a second order stochastic optimization algorithm which dynamically incorporates the curvature of the loss function via ADAptive estimates of the Hessian . Second order algorithms are among the most powerful optimization algorithms with superior convergence properties as compared to first order methods such as SGD and Adam . The main disadvantage of traditional second order methods is their heavier per-iteration computation and poor accuracy as compared to first order methods.
Edit intent: fluency
Original:  a second order stochastic optimization algorithm which dynamically incorporates the curvature of the loss function via ADAptive estimates of the Hessian . Second order algorithms are among the most powerful optimization algorithms with superior convergence properties as compared to first order methods such as SGD and ADAM . The main disadvantage of traditional second order methods is their heavier per-iteration computation and poor accuracy as compared to first order methods.
Revised:  a second order stochastic optimization algorithm which dynamically incorporates the curvature of the loss function via ADAptive estimates of the Hessian . Second order algorithms are among the most powerful optimization algorithms with superior convergence properties as compared to first order methods such as SGD and ADAM . The main disadvantage of traditional second order methods is their heavier per iteration computation and poor accuracy as compared to first order methods.
Edit intent: fluency
Original:  We show that AdaHessian achieves new state-of-the-art results by a large margin as compared to other adaptive optimization methods, including variants of ADAM . In particular, we perform extensive tests on CV, NLP, and recommendation system tasks and find that AdaHessian : (i) achieves 1.80 %DIFDELCMD < \\%%% %DIF < /1.45\\% higher accuracy on ResNets20/32 on Cifar10, and 5.55\\% higher accuracy on ImageNet as compared to ADAM;
Revised:  We show that ADAHESSIAN achieves new state-of-the-art results by a large margin as compared to other adaptive optimization methods, including variants of ADAM . In particular, we perform extensive tests on CV, NLP, and recommendation system tasks and find that AdaHessian : (i) achieves 1.80 %DIFDELCMD < \\%%% %DIF < /1.45\\% higher accuracy on ResNets20/32 on Cifar10, and 5.55\\% higher accuracy on ImageNet as compared to ADAM;
Edit intent: fluency
Original:  We show that AdaHessian achieves new state-of-the-art results by a large margin as compared to other adaptive optimization methods, including variants of ADAM . In particular, we perform extensive tests on CV, NLP, and recommendation system tasks and find that AdaHessian : (i) achieves 1.80 %DIFDELCMD < \\%%% %DIF < /1.45\\% higher accuracy on ResNets20/32 on Cifar10, and 5.55\\% higher accuracy on ImageNet as compared to ADAM;
Revised:  We show that AdaHessian achieves new state-of-the-art results by a large margin as compared to other adaptive optimization methods, including variants of Adam . In particular, we perform extensive tests on CV, NLP, and recommendation system tasks and find that AdaHessian : (i) achieves 1.80 %DIFDELCMD < \\%%% %DIF < /1.45\\% higher accuracy on ResNets20/32 on Cifar10, and 5.55\\% higher accuracy on ImageNet as compared to ADAM;
Edit intent: fluency
Original:  We show that AdaHessian achieves new state-of-the-art results by a large margin as compared to other adaptive optimization methods, including variants of ADAM . In particular, we perform extensive tests on CV, NLP, and recommendation system tasks and find that AdaHessian : (i) achieves 1.80 %DIFDELCMD < \\%%% %DIF < /1.45\\% higher accuracy on ResNets20/32 on Cifar10, and 5.55\\% higher accuracy on ImageNet as compared to ADAM;
Revised:  We show that AdaHessian achieves new state-of-the-art results by a large margin as compared to other adaptive optimization methods, including variants of ADAM . In particular, we perform extensive tests on CV, NLP, and recommendation system tasks and find that ADAHESSIAN : (i) achieves 1.80 %DIFDELCMD < \\%%% %DIF < /1.45\\% higher accuracy on ResNets20/32 on Cifar10, and 5.55\\% higher accuracy on ImageNet as compared to ADAM;
Edit intent: fluency
Original: \end{abstract} 
Revised: \end{abstract} \%/1.45\% higher accuracy on ResNets20/32 on Cifar10, and 5.55\% higher accuracy on ImageNet as compared to Adam; (ii) outperforms AdamW for transformers by 0.13/0.33 BLEU score on IWSLT14/WMT14 and 2.7/1.0 PPL on PTB/Wikitext-103; (iii) outperforms AdamW for SqueezeBert by 0.41 points on GLUE; and (iv) achieves 0.032\% better score than Adagrad for DLRM on the Criteo Ad Kaggle dataset. Importantly, we show that the cost per iteration of ADAHESSIAN is comparable to first order methods, and that it exhibits robustness towards its hyperparameters.
Edit intent: meaning-changed
Original:  We find that executive functioning skills  are important drivers of earnings in the rural sample, whereas higher-order cognitive skills  are more important for determining  earnings in the urban sample.
Revised:  We find that executive functioning skills are important drivers of earnings in the rural sample, whereas higher-order cognitive skills are more important for determining earnings in the urban sample. Although numeracy is tested in both samples, it is only a statistically significant predictor of earnings in the urban sample.
Edit intent: meaning-changed
Original: Stabilized Runge-Kutta (aka Chebyshev) methods are especially efficient for the numerical solution of large systems of stiff  differential equations because they are fully explicit ; hence, they are inherently parallel and easily accommodate nonlinearity .
Revised: Stabilized Runge-Kutta (aka Chebyshev) methods are especially efficient for the numerical solution of large systems of stiff differential equations because they are fully explicit .
Edit intent: coherence
Original:  After applying machine learning models, we achieve an AUC score of nearly 90\% in discriminating conspiracy from science narratives using the constructed features. We also find that conspiracy cascades are more likely to be controlled by a broader set of users than science cascades  .
Revised:  After applying machine learning models, we achieve an AUC score of nearly 90\% in discriminating conspiracy from science narratives . We find that conspiracy cascades are more likely to be controlled by a broader set of users than science cascades .
Edit intent: clarity
Original: This note is to publicly answer to a paper recently accepted to SWAT 2020 \mbox{%DIFAUXCMD Munro2019SpaceEfficientDS that claims to have solved an error in our papers \mbox{%DIFAUXCMD paolaSiam,paolaTCS by proposing a solution with worst performances.
Revised: This note is to publicly answer to a paper recently accepted to SWAT 2020 1 that claims to have solved an error in our papers \mbox{%DIFAUXCMD paolaSiam,paolaTCS by proposing a solution with worst performances.
Edit intent: meaning-changed
Original: This note is to publicly answer to a paper recently accepted to SWAT 2020 \mbox{%DIFAUXCMD Munro2019SpaceEfficientDS that claims to have solved an error in our papers \mbox{%DIFAUXCMD paolaSiam,paolaTCS by proposing a solution with worst performances.
Revised: This note is to publicly answer to a paper recently accepted to SWAT 2020 \mbox{%DIFAUXCMD Munro2019SpaceEfficientDS that claims to have solved an error in our papers 3,2 by proposing a solution with worst performances.
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Original:  In the following section we describe in detail sections 4.2 (Cluster collection) and 5 (Data Structure and Space Complexity) in \mbox{%DIFAUXCMD paolaSiam to show the implementation of the data structure.
Revised:  In the following section we describe in detail sections 4.2 (Cluster collection) and 5 (Data Structure and Space Complexity) in 3 to show the implementation of the data structure.
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Original: Argumentative quality is an important feature of everyday writing in many textual domains, such as online reviews and question-and-answer (Q A) forums. Authors can improve their writing with feedback targeting individual aspects of argument quality (AQ) , even though preceding work has mostly focused on assessing the overall AQ.
Revised: Argumentative quality is an important feature of everyday writing in many textual domains, such as online reviews and question-and-answer (Q Though preceding work in computational argument quality (AQ) , even though preceding work has mostly focused on assessing the overall AQ.
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Original:  Authors can improve their writing with feedback targeting individual aspects of argument quality (AQ) , even though preceding work has mostly focused on assessing the overall AQ. These individual aspects are reflected in theory-based dimensions of argument quality, but automatic assessment in real-world texts is still in its infancy -- a large-scale corpus and computational models are missing.
Revised:  Authors can improve their writing with feedback targeting individual aspects of argument quality (AQ) mostly focuses on assessing overall AQ, researchers agree that writers would benefit from feedback targeting individual dimensions of argumentation theory. However, a large-scale corpus and computational models are missing.
Edit intent: clarity
Original:  In this work, we advance theory-based argument quality research by conducting an extensive analysis covering three diverse domains of online argumentative writing : Q&A forums, debate forums, and review forums.
Revised:  In this work, we advance theory-based argument quality research by conducting an extensive analysis covering three diverse domains of online argumentative writing and presenting GAQCorpus: the first large-scale English multi-domain (community Q&A forums, debate forums, and review forums.
Edit intent: meaning-changed
Original:  In this work, we advance theory-based argument quality research by conducting an extensive analysis covering three diverse domains of online argumentative writing : Q&A forums, debate forums, and review forums. We start with an annotation study with linguistic experts and crowd workers, resulting in the first large-scale English corpus annotated with theory-based argument quality scores, dubbed AQCorpus.
Revised:  In this work, we advance theory-based argument quality research by conducting an extensive analysis covering three diverse domains of online argumentative writing : Q&A forums, debate forums, review forums) corpus annotated with theory-based argument quality scores, dubbed AQCorpus.
Edit intent: coherence
Original:  We start with an annotation study with linguistic experts and crowd workers, resulting in the first large-scale English corpus annotated with theory-based argument quality scores, dubbed AQCorpus. Next, we propose the first computational approaches to theory-based argument quality assessment, which can serve as strong baselines for future work.
Revised:  We start with an annotation study with linguistic experts and crowd workers, resulting in the first large-scale English corpus annotated with theory-based AQ scores. We then propose the first computational approaches to theory-based argument quality assessment, which can serve as strong baselines for future work.
Edit intent: clarity
Original:  Our research yields interesting findings including the feasibility of large-scale theory-based argument quality annotations, the fact that relations between theory-based argument quality dimensions can be exploited to yield performance improvements, and demonstrates the usefulness of theory-based argument quality predictions with respect to the practical AQ assessment view .
Revised:  We demonstrate the feasibility of large-scale theory-based argument quality annotations, the fact that relations between theory-based argument quality dimensions can be exploited to yield performance improvements, and demonstrates the usefulness of theory-based argument quality predictions with respect to the practical AQ assessment view .
Edit intent: coherence
Original:  Our research yields interesting findings including the feasibility of large-scale theory-based argument quality annotations, the fact that relations between theory-based argument quality dimensions can be exploited to yield performance improvements, and demonstrates the usefulness of theory-based argument quality predictions with respect to the practical AQ assessment view .
Revised:  Our research yields interesting findings including the feasibility of large-scale AQ annotation, show that exploiting relations between dimensions yields performance improvements, and demonstrates the usefulness of theory-based argument quality predictions with respect to the practical AQ assessment view .
Edit intent: coherence