wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Here, we address the hypothesis that SARS-CoV and SARS-CoV-2 strains able to use angiotensin-converting enzyme 2 (ACE2) proteins have adapted their RBM along the viral evolution to explore specific conformational topology driven by the amino acid residues YGF to infect host cells.
Revised:  Here, we address the hypothesis that SARS-CoV and SARS-CoV-2 strains able to use angiotensin-converting enzyme 2 (ACE2) proteins have adapted their RBM along the viral evolution to explore specific conformational topology driven by the residues YGF to infect host cells.
Edit intent: clarity
Original:  TriCG and TriMR are tantamount to preconditioned block-CG and block-MINRES with two right-hand sides in which the two approximate solutions are summed at each iteration, but require less storage and work per iteration.
Revised:  TriCG and TriMR are tantamount to preconditioned Block-CG and Block-MINRES with two right-hand sides in which the two approximate solutions are summed at each iteration, but require less storage and work per iteration.
Edit intent: fluency
Original:  In all our experiments, TriCG and TriMR terminate earlier than SYMMLQ and MINRES on a residual-based stopping condition with an improvement of up to 50\% in terms of number of iterations  .
Revised:  In all our experiments, TriCG and TriMR terminate earlier than SYMMLQ and MINRES on a residual-based stopping condition with an improvement of up to 50\% in terms of number of iterations . They also terminate more reliably than Block-CG and Block-MINRES. Experiments in quadruple and octuple precision suggest that loss of orthogonality in the basis vectors is significantly less pronounced in TriCG and TriMR than in Block-CG and Block-MINRES .
Edit intent: meaning-changed
Original:  We formulate a bi-level optimization problem to decide not only which of M outer scenarios to simulate and how many times to replicate at each, but also how to pool these replications such that the total simulation effort is minimized while achieving the same estimation error as the standard nested simulation  .
Revised:  We formulate a bi-level optimization problem to decide not only which of M outer scenarios to simulate and how many times to replicate at each, but also how to pool these replications such that the total simulation effort is minimized while achieving a target level of .
Edit intent: meaning-changed
Original:  We formulate a bi-level optimization problem to decide not only which of M outer scenarios to simulate and how many times to replicate at each, but also how to pool these replications such that the total simulation effort is minimized while achieving the same estimation error as the standard nested simulation  .
Revised:  We formulate a bi-level optimization problem to decide not only which of M outer scenarios to simulate and how many times to replicate at each, but also how to pool these replications such that the total simulation effort is minimized while achieving the same estimation error as the standard nested simulation precision .
Edit intent: clarity
Original:  Empirical results show that our experiment design significantly reduces the simulation cost compared to the standard nested simulation as well as a state-of-the-art  design that pools replications via regressions .
Revised:  Empirical results show that our experiment design significantly reduces the simulation cost compared to the standard nested simulation as well as a state-of-the-art regression-based design that pools replications via regressions .
Edit intent: meaning-changed
Original:  Inspired by this idea, we introduce Multi-view Attention-based Pairwise Recurrent Neural Network ( %DIFDELCMD < \projtitle%%%  ) to solve the name disambiguation problem.
Revised:  Inspired by this idea, we introduce Multi-view Attention-based Pairwise Recurrent Neural Network ( %DIFDELCMD < \projtitle%%% MA-PairRNN ) to solve the name disambiguation problem.
Edit intent: meaning-changed
Original:  We divided papers into small blocks based on discriminative author attributes and blocks of the same author will be merged according to pairwise classification results of %DIFDELCMD < \projtitle%%% .%DIFDELCMD < \projtitle %%%  combines heterogeneous graph embedding learning and pairwise similarity learning into a framework.
Revised:  We divided papers into small blocks based on discriminative author attributes and blocks of the same author will be merged according to pairwise classification results of %DIFDELCMD < \projtitle%%% %DIFDELCMD < \projtitle %%% combines heterogeneous graph embedding learning and pairwise similarity learning into a framework.
Edit intent: fluency
Original:  We divided papers into small blocks based on discriminative author attributes and blocks of the same author will be merged according to pairwise classification results of %DIFDELCMD < \projtitle%%% .%DIFDELCMD < \projtitle %%%  combines heterogeneous graph embedding learning and pairwise similarity learning into a framework.
Revised:  We divided papers into small blocks based on discriminative author attributes and blocks of the same author will be merged according to pairwise classification results of %DIFDELCMD < \projtitle%%% .%DIFDELCMD < \projtitle %%% MA-PairRNN. MA-PairRNN combines heterogeneous graph embedding learning and pairwise similarity learning into a framework.
Edit intent: meaning-changed
Original:  In addition to attribute and structure information, %DIFDELCMD < \projtitle %%%  also exploits semantic information by meta-path and generates node representation in an inductive way, which is scalable to large graphs.
Revised:  In addition to attribute and structure information, %DIFDELCMD < \projtitle %%% MA-PairRNN also exploits semantic information by meta-path and generates node representation in an inductive way, which is scalable to large graphs.
Edit intent: meaning-changed
Original:  It was also demonstrated that %DIFDELCMD < \projtitle %%%  can perform well with a small amount of training data and have better generalization ability across different research areas.
Revised:  It was also demonstrated that %DIFDELCMD < \projtitle %%% MA-PairRNN can perform well with a small amount of training data and have better generalization ability across different research areas.
Edit intent: meaning-changed
Original:  Here, three putative binding sites were described through computational means, while URLanic compounds for each location were reached through virtual screening from a massive, PAIN-filtered flavonoid database . Virtual screening integrated several confirmation methods, including validation docking based on 2Dscore, cross-docking assays and positive and negative controls.
Revised:  Here, a PAIN-filtered flavonoid database . Virtual screening integrated several confirmation methods, including validation docking based on 2Dscore, cross-docking assays and positive and negative controls.
Edit intent: clarity
Original:  Here, three putative binding sites were described through computational means, while URLanic compounds for each location were reached through virtual screening from a massive, PAIN-filtered flavonoid database . Virtual screening integrated several confirmation methods, including validation docking based on 2Dscore, cross-docking assays and positive and negative controls.
Revised:  Here, three putative binding sites were described through computational means, while URLanic compounds for each location were reached through virtual screening from a massive, PAIN-filtered flavonoid database was screened against four sites of the protease: a free (normal) conformation of the Substrate Binding Site (NSBS), an induced-fit state of the SBS (ISBS), a Dimerization Site (DS) and a Cryptic Site (CS). The mean binding energies of the top five ligands from each site were -9.52, -11.512, -7.042 and -10.348 kcal/mol for the NSBS, the ISBS, the DS and the CS, respectively. For the DS and CS, these top five compounds were selected as candidates to bind their respective site. In the case of SBS, the top 30 ligands with the lowest binding energies from NSBS and ISBS were contrasted and the ones present in both lists were selected as the final candidates. The final list was: Dorsilurin E (FL3FQUNP0001), Euchrenone a11 (FL2FALNP0014), Kurziflavolactone C (FL2FA9NC0016), Licorice glycoside E (FL2F1AGSN001) and Taxifolin 3'- (6"-phenyl- acetylglucoside) (FL4DACGS0020) for the SBS; Sanggenol O (FL2FALNP0020), CHEMBL2171573, Kanzonol E (FL3F1ANP0001), CHEMBL2171584 and Abyssynoflavanone VI (FL2FACNP0014) for DS and CHEMBL2171598, CHEMBL2171577, Denticulaflavanol (FL5FAANR0001), Kurzichalcolactone (FL1CA9NC0001) and CHEMBL2171578 for CS . Virtual screening integrated several confirmation methods, including validation docking based on 2Dscore, cross-docking assays and positive and negative controls.
Edit intent: meaning-changed
Original:  Here, three putative binding sites were described through computational means, while URLanic compounds for each location were reached through virtual screening from a massive, PAIN-filtered flavonoid database . Virtual screening integrated several confirmation methods, including validation docking based on 2Dscore, cross-docking assays and positive and negative controls.
Revised:  Here, three putative binding sites were described through computational means, while URLanic compounds for each location were reached through virtual screening from a massive, PAIN-filtered flavonoid database . Virtual screening integrated several confirmation methods, including cross-docking assays and positive and negative controls.
Edit intent: meaning-changed
Original: Joint extraction refers to extracting triples, composed of entities and relations, simultaneously from the text with a single model , but the existing methods rarely work well on sentences with overlapping issue, i.e., the same entity is included in multiple triples.
Revised: Joint extraction refers to extracting triples, composed of entities and relations, simultaneously from the text with a single model . However, most existing methods fail to extract all triples accurately and efficiently from sentences with overlapping issue, i.e., the same entity is included in multiple triples.
Edit intent: clarity
Original: In this paper, we present a new topic modelling approach via the theory of optimal transport (OT).
Revised: Recently, Neural Topic Models (NTMs) inspired by variational autoencoders have obtained increasingly research interest due to their promising results on text analysis. However, it is usually hard for existing NTMs to achieve good document representation and coherent/diverse topics at the same time. Moreover, they often degrade their performance severely on short documents. The requirement of reparameterisation could also comprise their training quality and model flexibility. To address these shortcomings, we present a new topic modelling approach via the theory of optimal transport (OT).
Edit intent: meaning-changed
Original:  Specifically, we present a document with two distributions: a distribution over the words (doc-word distribution ) and a distribution over the topics (doc-topic distribution). For one document, the doc-word distribution is the observed, sparse, low-level representation of the content, while the doc-topic distribution is the latent, dense, high-level one of the same content. Learning a topic model can then be viewed as a process of minimising the transportation of the semantic information from one distribution to the other. This new viewpoint leads to a novel OT-based topic modelling framework, which enjoys appealing simplicity, effectiveness, and efficiency.
Revised:  Specifically, we propose to learn the topic distribution of a document by directly minimising its OT distance to the document's word distributions. Importantly, the cost matrix of the OT distance models the weights between topics and words, which is constructed by the distances between topics and words in an embedding space. Our proposed model can be trained efficiently with a differentiable loss.
Edit intent: meaning-changed
Original:  Our system combines state-of-the-art approaches to process and represents textual data in order to capture its underlying semantics . In particular, we use 1) Bidirectional Encoder Representations from Transformers (commonly known as, BERT) to learn a contextualized vector representation of a tweet, and 2) a graph-based architecture to compute semantic correlations between the entities and hashtags in tweets and their corresponding labels .
Revised:  I-AID incorporates three main components: i) a BERT-based encoder to capture the semantics of a tweet and represent as a low-dimensional vector, ii) a graph attention network (GAT) to apprehend correlations between tweets' words/entities and the corresponding information types, and iii) a Relation Network as a learnable distance metric to compute the similarity between tweets and their corresponding labels .
Edit intent: clarity
Original:  In particular, we use 1) Bidirectional Encoder Representations from Transformers (commonly known as, BERT) to learn a contextualized vector representation of a tweet, and 2) a graph-based architecture to compute semantic correlations between the entities and hashtags in tweets and their corresponding labels .
Revised:  In particular, we use 1) Bidirectional Encoder Representations from Transformers (commonly known as, BERT) to learn a contextualized vector representation of a tweet, and 2) a graph-based architecture to compute semantic correlations between the entities and hashtags in tweets and their corresponding information types in a supervised way .
Edit intent: clarity
Original:  We conducted our experiments on a real-world dataset of disaster-related tweets. Our experimental results indicate that our model outperforms state-of-the-art approaches baselines in terms of F1-score by + 11\% .
Revised:  We conducted several experiments on two real publicly-available datasets. Our results indicate that our model outperforms state-of-the-art approaches baselines in terms of F1-score by + 11\% .
Edit intent: meaning-changed
Original:  Our experimental results indicate that our model outperforms state-of-the-art approaches baselines in terms of F1-score by + 11\% .
Revised:  Our experimental results indicate that our model outperforms state-of-the-art approaches baselines in terms of F1-score by + 6\% and +4\% on the TREC-IS dataset and COVID-19 Tweets, respectively .
Edit intent: meaning-changed
Original: Most of the existing literature on the current pandemic focuses on approaches to model the outbreak and spreading of COVID-19. This paper proposes a generalized Markov-Switching approach, the SUIHR model, designed to study border control policies and contact tracing against COVID-19 in a period where countries start to re-open.
Revised: Imported COVID-19 cases, if unchecked, can jeopardize the effort of domestic containment. We aim to find out what sustainable border control options for different entities (e.g., countries, states) exist during the reopening phases, given their own choice of domestic control measures and new technologies such as contact tracing. We propose a SUIHR model, which represents an extension to the discrete time SIR models. The model focuses on studying the spreading of virus predominantly by asymptomatic and pre-symptomatic patients. Imported risk and (1-tier) contact tracing are both built into the model. Under plausible parameter assumptions, we seek sustainable border control policies, in combination with sufficient internal measures, which allow entities to confine the virus without the SUIHR model, designed to study border control policies and contact tracing against COVID-19 in a period where countries start to re-open.
Edit intent: meaning-changed
Original:  This paper proposes a generalized Markov-Switching approach, the SUIHR model, designed to study border control policies and contact tracing against COVID-19 in a period where countries start to re-open.
Revised:  This paper proposes a generalized Markov-Switching approach, the need to revert back to more restrictive life styles or to rely on herd immunity. When the base reproduction number of COVID-19 in a period where countries start to re-open.
Edit intent: meaning-changed
Original:  This paper proposes a generalized Markov-Switching approach, the SUIHR model, designed to study border control policies and contact tracing against COVID-19 in a period where countries start to re-open. We offer the following contributions. First, the SUIHR modelcan include multiple entities, reflecting different government bodies with different containment measures. Second, constraints as, for example, new case targets and medical resource limits can be imposed in a linear programming framework. Third, in contrast to most SIR models, we focus on the spreading of infectious people without symptoms instead of the spreading of people who are already showing symptoms. We find that even if a country has closed its borders completely, domestic contact tracing is not enough to go back to normal life.
Revised:  This paper proposes a generalized Markov-Switching approach, the SUIHR model, designed to study border control policies and contact tracing against COVID-19 exceeds 2.5, even 100\% effective contact tracing alone is not enough to go back to normal life.
Edit intent: meaning-changed
Original:  We find that even if a country has closed its borders completely, domestic contact tracing is not enough to go back to normal life. Countries having successfully controlled the virus can keep it under check as long as imported risk is not growing, meaning they can lift travel restrictions with similar countries .
Revised:  We find that even if a country has closed its borders completely, domestic contact tracing is not enough to contain the spreading. For an entity that has completely eliminated the virus domestically, and resumes "normal", very strict pre-departure screening and test and isolation upon arrival combined with effective contact tracing can only delay another outbreak by 6 months .
Edit intent: meaning-changed
Original:  The approach presented here could be used in other Indo-Aryan or Indic languages to develop a question answering system.abstract 
Revised:  The approach presented here could be used in other Indo-Aryan or Indic languages to develop a question answering system.abstract \% for coarse-grained question classes. Overall, the stacking approach produces the best results for fine-grained classification and achieves 87.79\% of accuracy. The approach presented here could be used in other Indo-Aryan or Indic languages to develop a question answering system.
Edit intent: meaning-changed
Original:  Much effort has been made in the past decades to collect extensive and detailed information about the spatial distribution of tropical rainforests, as demonstrated, e.g., in the impressive 50 ha tropical forest plot on Barro Colorado Island, Panama.
Revised:  Much effort has been made in the past decades to collect extensive and detailed information about the spatial distribution of tropical rainforests, as demonstrated, e.g., in the 50 ha tropical forest plot on Barro Colorado Island, Panama.
Edit intent: clarity
Original: This paper develops a general causal inference method for treatment effects models under selection on unobservables. A large set of covariates that admits an unknown, possibly nonlinear factor structure is exploited to control for the latent confounders.
Revised: This paper develops a general causal inference method for treatment effects models with noisily measured confounders. The key feature is that a large set of covariates that admits an unknown, possibly nonlinear factor structure is exploited to control for the latent confounders.
Edit intent: clarity
Original:  A large set of covariates that admits an unknown, possibly nonlinear factor structure is exploited to control for the latent confounders. The key building block is a local principal subspace approximation procedure that combines K-nearest neighbors matching and principal component analysis.
Revised:  A large set of covariates that admits an unknown, possibly nonlinear factor structure . The main building block is a local principal subspace approximation procedure that combines K-nearest neighbors matching and principal component analysis.
Edit intent: coherence
Original: Boolean networks are frequently used to model biological systems, such as gene regulatory networks. Canalizing functions have been suggested as suitable update rules for genetic networks and have been studied rigorously. Mathematically, the biology-inspired concept of canalization means that a single input can determine the output of a function , irrespective of all the other inputs.
Revised: This paper studies the mathematical properties of collectively canalizing Boolean functions, a class of functions that has arisen from applications in systems biology. Boolean networks are an increasingly popular modeling framework for regulatory networks , irrespective of all the other inputs.
Edit intent: clarity
Original:  Mathematically, the biology-inspired concept of canalization means that a single input can determine the output of a function , irrespective of all the other inputs. Here, we relax this definition and explore the broader class of collectively canalizing Boolean functions as well as the k-set canalizing proportion, which is the proportion of sets of k inputs which canalize a Boolean function. We exhibit how the number and type of these sets influence a function's behavior and define a new measure for the canalizing strength of any Boolean function.
Revised:  Mathematically, the biology-inspired concept of canalization means that a single input can determine the output of a function , and the class of functions studied here captures a key feature of biological network dynamics, namely that a subset of one or more variables, under certain conditions, can dominate the value of a Boolean function, to the exclusion of all others. These functions have rich mathematical properties to be explored. The paper shows how the number and type of these sets influence a function's behavior and define a new measure for the canalizing strength of any Boolean function.
Edit intent: clarity
Original:  We further show how the k-set canalizing proportions connect the concept of canalization and the well-studied concept of the average sensitivity of a Boolean function  .
Revised:  We further show how the k-set canalizing proportions connect the concept of canalization and the well-studied concept of the average sensitivity of a Boolean function . The relationship between Boolean functions and the dynamics of the networks they form is important in a wide range of applications beyond biology, such as computer science, and has been studied with statistical and simulation-based methods. But the rich relationship between structure and dynamics remains largely unexplored, and this paper is intended as a contribution to its mathematical foundation .
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Original:  We show that the new method which is obtained by using both continuous and discontinuous discretisations in space, admits  local and global conservation laws of energyand momentum. We show existence and uniqueness of solution of the discrete equations.
Revised:  We show that the new method which is obtained by using both continuous and discontinuous discretisations in space, admits local and global conservation law of energy. We also show existence and uniqueness of solution of the discrete equations.
Edit intent: clarity
Original:  We specifically propose a needs-driven multi-robot cooperation mechanism represented through a Behavior Tree structure and evaluate the performance of the system in terms of the group utility and energy cost to achieve the rescue mission in a limited time.
Revised:  We propose a needs-driven multi-robot cooperation mechanism represented through a Behavior Tree structure and evaluate the performance of the system in terms of the group utility and energy cost to achieve the rescue mission in a limited time.
Edit intent: coherence