wanyu/IteraTeR_full_sent huggingface dataset:
Original:  To address this issue, we develop variable-lag Granger causality and  Transfer Entropy, generalizations of both Granger causality and Transfer Entropy that relax the assumption of the fixed time delay and allows causes to influence effects with arbitrary time delays.
Revised:  To address this issue, we develop variable-lag Granger causality and Transfer Entropy, generalizations of both Granger causality and Transfer Entropy that relax the assumption of the fixed time delay and allow causes to influence effects with arbitrary time delays.
Edit intent: fluency
Original:  We demonstrate our approach on an application for studying coordinated collective behavior and other real-world casual-inference datasets and show that our proposed approaches perform better than several existing methods in both simulated and real-world datasets.
Revised:  We demonstrate our approaches on an application for studying coordinated collective behavior and other real-world casual-inference datasets and show that our proposed approaches perform better than several existing methods in both simulated and real-world datasets.
Edit intent: fluency
Original:  We find that training on adversarially collected samples leads to strong generalisation to non-adversarially collected datasets, yet with progressive deterioration as the model-in-the-loop strength increases. Furthermore we find that stronger models can still learn from datasets collected with substantially weaker models in the loop: When trained on data collected with a BiDAF model in the loop, RoBERTa achieves 36.0 F1 on questions that it cannot answer when trained on SQuAD - only marginally lower than when trained on data collected using RoBERTa itself  .
Revised:  We find that training on adversarially collected samples leads to strong generalisation to non-adversarially collected datasets, yet with progressive performance deterioration with increasingly stronger models-in-the-loop. Furthermore, we find that stronger models can still learn from datasets collected with substantially weaker models in the loop: When trained on data collected with a BiDAF model in the loop, RoBERTa achieves 36.0 F1 on questions that it cannot answer when trained on SQuAD - only marginally lower than when trained on data collected using RoBERTa itself .
Edit intent: clarity
Original:  Furthermore we find that stronger models can still learn from datasets collected with substantially weaker models in the loop: When trained on data collected with a BiDAF model in the loop, RoBERTa achieves 36.0 F1 on questions that it cannot answer when trained on SQuAD - only marginally lower than when trained on data collected using RoBERTa itself  .
Revised:  Furthermore we find that stronger models can still learn from datasets collected with substantially weaker models-in-the-loop. When trained on data collected with a BiDAF model in the loop, RoBERTa achieves 36.0 F1 on questions that it cannot answer when trained on SQuAD - only marginally lower than when trained on data collected using RoBERTa itself .
Edit intent: coherence
Original:  Furthermore we find that stronger models can still learn from datasets collected with substantially weaker models in the loop: When trained on data collected with a BiDAF model in the loop, RoBERTa achieves 36.0 F1 on questions that it cannot answer when trained on SQuAD - only marginally lower than when trained on data collected using RoBERTa itself  .
Revised:  Furthermore we find that stronger models can still learn from datasets collected with substantially weaker models in the loop: When trained on data collected with a BiDAF model in the loop, RoBERTa achieves 39.9 F1 on questions that it cannot answer when trained on SQuAD - only marginally lower than when trained on data collected using RoBERTa itself .
Edit intent: meaning-changed
Original:  Furthermore we find that stronger models can still learn from datasets collected with substantially weaker models in the loop: When trained on data collected with a BiDAF model in the loop, RoBERTa achieves 36.0 F1 on questions that it cannot answer when trained on SQuAD - only marginally lower than when trained on data collected using RoBERTa itself  .
Revised:  Furthermore we find that stronger models can still learn from datasets collected with substantially weaker models in the loop: When trained on data collected with a BiDAF model in the loop, RoBERTa achieves 36.0 F1 on questions that it cannot answer when trained on SQuAD - only marginally lower than when trained on data collected using RoBERTa itself (41.0F1) .
Edit intent: meaning-changed
Original:  This work reviews with a critical and constructive attitude  our current knowledge of how genotypes map onto  phenotypes URLanismal functions, and discusses theoretical and empirical avenues to broaden and improve this comprehension.
Revised:  This work delves into a critical and constructive attitude our current knowledge of how genotypes map onto phenotypes URLanismal functions, and discusses theoretical and empirical avenues to broaden and improve this comprehension.
Edit intent: clarity
Original:  This work reviews with a critical and constructive attitude  our current knowledge of how genotypes map onto  phenotypes URLanismal functions, and discusses theoretical and empirical avenues to broaden and improve this comprehension.
Revised:  This work reviews with a critical and constructive attitude in our current knowledge of how genotypes map onto phenotypes URLanismal functions, and discusses theoretical and empirical avenues to broaden and improve this comprehension.
Edit intent: fluency
Original:  This work reviews with a critical and constructive attitude  our current knowledge of how genotypes map onto  phenotypes URLanismal functions, and discusses theoretical and empirical avenues to broaden and improve this comprehension.
Revised:  This work reviews with a critical and constructive attitude our current knowledge of how genotypes map onto molecular phenotypes URLanismal functions, and discusses theoretical and empirical avenues to broaden and improve this comprehension.
Edit intent: meaning-changed
Original:   Though we are still far from achieving a complete picture of these relationships, our current understanding of simpler questions, such as the structure induced in the space of genotypes by sequences mapped to molecular structures (the so-called genotype-phenotype map) , has revealed important facts that deeply affect the dynamical description of evolutionary processes.
Revised:  We refer to this generally as the problem of the genotype-phenotype map. Though we are still far from achieving a complete picture of these relationships, our current understanding of simpler questions, such as the structure induced in the space of genotypes by sequences mapped to molecular structures (the so-called genotype-phenotype map) , has revealed important facts that deeply affect the dynamical description of evolutionary processes.
Edit intent: meaning-changed
Original:   Though we are still far from achieving a complete picture of these relationships, our current understanding of simpler questions, such as the structure induced in the space of genotypes by sequences mapped to molecular structures (the so-called genotype-phenotype map) , has revealed important facts that deeply affect the dynamical description of evolutionary processes.
Revised:  Though we are still far from achieving a complete picture of these relationships, our current understanding of simpler questions, such as the structure induced in the space of genotypes by sequences mapped to molecular structures , has revealed important facts that deeply affect the dynamical description of evolutionary processes.
Edit intent: clarity
Original:  Empirical evidence supporting the fundamental relevance of features such as phenotypic bias is mounting as well, while the synthesis of conceptual and experimental progress leads to questioning current assumptions on the nature of evolutionary dynamics---cancer progression models or synthetic biology approaches being notable examples.
Revised:  Empirical evidence supporting the fundamental relevance of features such as phenotypic bias is mounting as well, while the synthesis of conceptual and experimental progress leads to questioning current assumptions on the nature of evolutionary dynamics-cancer progression models or synthetic biology approaches being notable examples.
Edit intent: fluency
Original:  In this survey, we provide a comprehensive review on knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research.
Revised:  In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research.
Edit intent: fluency
Original: Well Mixed-Mass Action (WMMA) kinetics is widely used to model various reactions .
Revised: Diffusion-limited association reactions are ubiquitous in nature. They are particularly important for biological reactions, where the reaction rates are often determined by the diffusive transport of the molecules on two-dimensional surfaces, such as the cell membrane. The peculiarities of diffusion on two-dimensional surfaces may lead to nontrivial reaction kinetics, such as concentration dependent rate of association between two molecules. However, traditionally, the kinetics of biomolecular association reactions has been modeled using the law of mass action, which assumes that the rate of reaction is a concentration independent constant .
Edit intent: coherence
Original:  In this Letter we investigate the diffusive transport and diffusion mediated reactions of biomolecules on plasma membranes compartmentalized by the interaction of cortical actin and transmembrane proteins.
Revised:  In this paper, using multiscale molecular simulation, we investigate the diffusive transport and diffusion mediated reactions of biomolecules on plasma membranes compartmentalized by the interaction of cortical actin and transmembrane proteins.
Edit intent: meaning-changed
Original:  In this Letter we investigate the diffusive transport and diffusion mediated reactions of biomolecules on plasma membranes compartmentalized by the interaction of cortical actin and transmembrane proteins. In that context, we investigate the validity of WMMA kinetics for reactions occurring on the plasma membrane. We find that for most biologically relevant conditions WMMA kinetics is violated. We characterize the violation and investigate its origin .
Revised:  In this Letter we investigate the concentration dependence of diffusion-limited association reactions on 2D surfaces. In particular, we quantify the influence of short-ranged pair interactions on the concentration dependence of the reaction rates and codify it in an empirical law. Using this law in a chemical kinetic model, we find that the the steady state behaviors of simple chemical systems are drastically modified by the presence of concentration dependent rates. In particular, we find that it leads to suppression of intrinsic noise in dimerization reaction and destabilizes robust oscillation in Lotka-Volterra predator-prey systems. In fact, we see a transition from robust to fine-tuned behavior in the latter. In addition, we show that concentration dependent reaction rates arise naturally in stochastic predator-prey systems due to intrinsic noise. We comment on the consequences of these results and discuss their implications in the modeling of complex chemical and biological systems. In particular, we comment on the range of validity of the law of mass action, which is a staple in all theoretical modeling of these systems .
Edit intent: meaning-changed
Original: Our study aimed to approach validity issues that arise both in the case of surveys and Facebook data . We conducted a novel parallel data collection method, rarely used before, which combines a face-to-face survey with the collection of personal FB data archives. We show the research potential of using these alternative data sourcestogether. We show that we can overcome some validity issues by cross-validating the two data sources by each other. We also show that if the researcher collaborates with the users instead of the companies and gets access to a part of the users' social media data archive, the problem of the leanness of social media data is eliminated. Our chosen topic is music interest, a key indicator of cultural sociology. To our knowledge , there are no previous studies on the rate at which any population is categorized by Facebook to advertisers as interested in music genres, or on the relationship between self-reported interest, digitally expressed interest and ad-interest categories. Specific genres were detected which show remarkable different pictures when measured these different ways. We studied also dynamic patterns in FB users music interest. We hope our data collection method and the presented validity approaches will initiate a future dialogue in digital data research in social sciences .
Revised: More and more digital data is available for social science analysis. This provides new ways of measuring several concepts. But when we start using new data sources, we have to understand how the new data source could be processed and how it could be analysed effectively. It is especially for Facebook data since there is no established gold standard analysis-framework. However, researchers have in-depth knowledge on how to measure different concepts using survey data. Thus, cross-referencing Facebook data with survey data is a reasonable way to support Facebook data analysis at different decision points. In this paper, we present how music preference could be measured by Facebook data and how survey data could support the selection of main indicators. Based on our results, we provide some general suggestions for Facebook data processing and indicator operationalization .
Edit intent: meaning-changed
Original:  We extract phonotactic data from 128 Pama-Nyungan vocabularies and apply tests for phylogenetic signal, quantifying the degree to which the data reflect phylogenetic history.
Revised:  We extract phonotactic data from 111 Pama-Nyungan vocabularies and apply tests for phylogenetic signal, quantifying the degree to which the data reflect phylogenetic history.
Edit intent: meaning-changed
Original:  Instead of going through a rather arbitrarily chosen pivot language, we propose to use the Wasserstein barycenter as a more informative ''mean'' language: it encapsulates information from all languages and minimizes all pairwise transportation costs.
Revised:  Instead of going through a rather arbitrarily chosen pivot language, we propose to use the Wasserstein barycenter as a more informative "mean" language: it encapsulates information from all languages and minimizes all pairwise transportation costs.
Edit intent: fluency
Original:  We experimentally demonstrate that our distance metric performs better alignment than current baselines outperforming them by 7\% on high-resource language pairs, 15\% on mid-resource language pairs, and 22\% on low-resource language pairs 
Revised:  We experimentally demonstrate that our distance metric performs better alignment than current baselines outperforming them by 7\% on high-resource language pairs, 15\% on mid-resource language pairs, and 22\% on low-resource language pairs .
Edit intent: fluency
Original: This paper reports on the open source project CLAI (Command Line AI), aimed at bringing the power of AI to the command line interface . The platform sets up the CLI as a new environment for AI researchers to conquer by surfacing the command line as a generic environment that researchers can interface to using a simple sense-act API  much like the traditional AI agent architecture.
Revised: This paper reports on the open source project CLAI (Command Line AI), aimed at bringing the power of AI to the command line interface (CLI). The CLAI platform sets up the CLI as a new environment for AI researchers to conquer by surfacing the command line as a generic environment that researchers can interface to using a simple sense-act API much like the traditional AI agent architecture.
Edit intent: clarity
Original:  The platform sets up the CLI as a new environment for AI researchers to conquer by surfacing the command line as a generic environment that researchers can interface to using a simple sense-act API  much like the traditional AI agent architecture.
Revised:  The platform sets up the CLI as a new environment for AI researchers to conquer by surfacing the command line as a generic environment that researchers can interface to using a simple sense-act API , much like the traditional AI agent architecture.
Edit intent: fluency
Original:  In 2017, the Italian Government expanded the number of mandatory immunizations establishing penalties for families of unvaccinated children . During the 2018  elections campaign, immunization policy entered the political debate , with the government accusing oppositions of fuelling vaccine scepticism.
Revised:  In 2017, the Italian Gov't expanded the number of mandatory immunizations establishing penalties for families of unvaccinated children . During the 2018 elections campaign, immunization policy entered the political debate , with the government accusing oppositions of fuelling vaccine scepticism.
Edit intent: fluency
Original:  In 2017, the Italian Government expanded the number of mandatory immunizations establishing penalties for families of unvaccinated children . During the 2018  elections campaign, immunization policy entered the political debate , with the government accusing oppositions of fuelling vaccine scepticism.
Revised:  In 2017, the Italian Government expanded the number of mandatory immunizations introducing penalties to unvaccinated children's families . During the 2018 elections campaign, immunization policy entered the political debate , with the government accusing oppositions of fuelling vaccine scepticism.
Edit intent: clarity
Original:  In 2017, the Italian Government expanded the number of mandatory immunizations establishing penalties for families of unvaccinated children . During the 2018  elections campaign, immunization policy entered the political debate , with the government accusing oppositions of fuelling vaccine scepticism.
Revised:  In 2017, the Italian Government expanded the number of mandatory immunizations establishing penalties for families of unvaccinated children . During the 2018 general elections campaign, immunization policy entered the political debate , with the government accusing oppositions of fuelling vaccine scepticism.
Edit intent: clarity
Original:  In 2017, the Italian Government expanded the number of mandatory immunizations establishing penalties for families of unvaccinated children . During the 2018  elections campaign, immunization policy entered the political debate , with the government accusing oppositions of fuelling vaccine scepticism.
Revised:  In 2017, the Italian Government expanded the number of mandatory immunizations establishing penalties for families of unvaccinated children . During the 2018 elections campaign, immunization policy entered the political debate with the Gov't in charge blaming oppositions for fuelling vaccine scepticism.
Edit intent: clarity
Original:  By a sentiment analysis on tweets posted in Italian during 2018, we aimed at (i) characterising the temporal flow of communication on vaccines, (ii) evaluating the usefulness of Twitter data for estimating vaccination parameters, and (iii) investigating whether the ambiguous political communication might have originated disorientation among the public.
Revised:  Using a sentiment analysis on tweets posted in Italian during 2018, we aimed at (i) characterising the temporal flow of communication on vaccines, (ii) evaluating the usefulness of Twitter data for estimating vaccination parameters, and (iii) investigating whether the ambiguous political communication might have originated disorientation among the public.
Edit intent: fluency
Original:  By a sentiment analysis on tweets posted in Italian during 2018, we aimed at (i) characterising the temporal flow of communication on vaccines, (ii) evaluating the usefulness of Twitter data for estimating vaccination parameters, and (iii) investigating whether the ambiguous political communication might have originated disorientation among the public.
Revised:  By a sentiment analysis on tweets posted in Italian during 2018, we aimed to: (i) characterising the temporal flow of communication on vaccines, (ii) evaluating the usefulness of Twitter data for estimating vaccination parameters, and (iii) investigating whether the ambiguous political communication might have originated disorientation among the public.
Edit intent: coherence
Original:  By a sentiment analysis on tweets posted in Italian during 2018, we aimed at (i) characterising the temporal flow of communication on vaccines, (ii) evaluating the usefulness of Twitter data for estimating vaccination parameters, and (iii) investigating whether the ambiguous political communication might have originated disorientation among the public.
Revised:  By a sentiment analysis on tweets posted in Italian during 2018, we aimed at (i) characterize the temporal flow of communication on vaccines, (ii) evaluating the usefulness of Twitter data for estimating vaccination parameters, and (iii) investigating whether the ambiguous political communication might have originated disorientation among the public.
Edit intent: clarity
Original:  By a sentiment analysis on tweets posted in Italian during 2018, we aimed at (i) characterising the temporal flow of communication on vaccines, (ii) evaluating the usefulness of Twitter data for estimating vaccination parameters, and (iii) investigating whether the ambiguous political communication might have originated disorientation among the public.
Revised:  By a sentiment analysis on tweets posted in Italian during 2018, we aimed at (i) characterising the temporal flow of vaccines communication on Twitter (ii) evaluating the usefulness of Twitter data for estimating vaccination parameters, and (iii) investigating whether the ambiguous political communication might have originated disorientation among the public.
Edit intent: fluency
Original:  By a sentiment analysis on tweets posted in Italian during 2018, we aimed at (i) characterising the temporal flow of communication on vaccines, (ii) evaluating the usefulness of Twitter data for estimating vaccination parameters, and (iii) investigating whether the ambiguous political communication might have originated disorientation among the public.
Revised:  By a sentiment analysis on tweets posted in Italian during 2018, we aimed at (i) characterising the temporal flow of communication on vaccines, (ii) evaluate the polarity of vaccination opinions and usefulness of Twitter data for estimating vaccination parameters, and (iii) investigating whether the ambiguous political communication might have originated disorientation among the public.
Edit intent: meaning-changed
Original:  By a sentiment analysis on tweets posted in Italian during 2018, we aimed at (i) characterising the temporal flow of communication on vaccines, (ii) evaluating the usefulness of Twitter data for estimating vaccination parameters, and (iii) investigating whether the ambiguous political communication might have originated disorientation among the public.
Revised:  By a sentiment analysis on tweets posted in Italian during 2018, we aimed at (i) characterising the temporal flow of communication on vaccines, (ii) evaluating the usefulness of Twitter data to estimate vaccination parameters, and (iii) investigating whether the ambiguous political communication might have originated disorientation among the public.
Edit intent: fluency