wanyu/IteraTeR_full_sent huggingface dataset:
Original:  As our core contribution, we introduce the Language Transmission Engine, in which cultural evolution of the language is combined with genetic evolution of the agents' architecture. We show that this co-evolution scenario leads to across-the-board improvements on all considered metrics  .
Revised:  As our core contribution, we empirically show that the optimal situation is to take into account also the learning biases of the language learners and thus let language and agents co-evolve. When we allow the agent population to evolve through architectural evolution, we achieve across the board improvements on all considered metrics .
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Original: A number of discrete time, finite population size models in genetics describing the dynamics of allele frequencies are known to converge (subject to suitable scaling) to a diffusion process  limit, termed the Wright-Fisher diffusion.
Revised: A number of discrete time, finite population size models in genetics describing the dynamics of allele frequencies are known to converge (subject to suitable scaling) to a diffusion process in the infinite population limit, termed the Wright-Fisher diffusion.
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Original:  In this article we  analyse the statistical properties of the Bayesian estimator for the selection coefficient in this model , when both selection and mutation are acting on the population.
Revised:  In this article we show that the diffusion is ergodic uniformly in the selection and mutation parameters, and that the measures induced by the solution to the stochastic differential equation are uniformly locally asymptotically normal. Subsequently these two results are used to analyse the statistical properties of the Bayesian estimator for the selection coefficient in this model , when both selection and mutation are acting on the population.
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Original:  In particular, it is shown that this estimator is uniformly consistent over compact sets , uniformly asymptotically normal, and displays uniform convergence of moments on compact sets .
Revised:  In particular, it is shown that this estimator is uniformly consistent over compact sets , uniformly asymptotically normal, and displays uniform convergence of moments over compact sets, and are asymptotically efficient for a suitable class of loss functions .
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Original:  In this paper, we propose a Game Generative Network (GGN) framework which utilizes the deviation between the real game outcome and the ideal game model to build networks for game processes, which opens a door for understanding more about agents with game behaviors by graph mining approaches.
Revised:  In this paper, we propose a Game Generative Network (GGN) framework that utilizes the deviation between the real game outcome and the ideal game model to build networks for game processes, which opens a door for understanding more about agents with game behaviors by graph mining approaches.
Edit intent: fluency
Original:  In this paper, we propose a Game Generative Network (GGN) framework which utilizes the deviation between the real game outcome and the ideal game model to build networks for game processes, which opens a door for understanding more about agents with game behaviors by graph mining approaches.
Revised:  In this paper, we propose a Game Generative Network (GGN) framework which utilizes the deviation between the real game outcome and the ideal game model to generate networks for game processes, which opens a door for understanding more about agents with game behaviors by graph mining approaches.
Edit intent: clarity
Original:  In this paper, we propose a Game Generative Network (GGN) framework which utilizes the deviation between the real game outcome and the ideal game model to build networks for game processes, which opens a door for understanding more about agents with game behaviors by graph mining approaches.
Revised:  In this paper, we propose a Game Generative Network (GGN) framework which utilizes the deviation between the real game outcome and the ideal game model to build networks for game processes, which opens a door for understanding agents with game behaviors by graph mining approaches.
Edit intent: clarity
Original:  In this paper, we propose a Game Generative Network (GGN) framework which utilizes the deviation between the real game outcome and the ideal game model to build networks for game processes, which opens a door for understanding more about agents with game behaviors by graph mining approaches.
Revised:  In this paper, we propose a Game Generative Network (GGN) framework which utilizes the deviation between the real game outcome and the ideal game model to build networks for game processes, which opens a door for understanding more about agents with game behaviors by network mining approaches.
Edit intent: clarity
Original: The soil bacterium Myxococcus xanthus lives in densely packed groups that form dynamic three-dimensional patterns in response to environmental changes, such as droplet-like fruiting bodies during starvation.
Revised: The soil bacterium Myxococcus xanthus lives in densely packed groups that form dynamic three-dimensional patterns in response to environmental changes, such as droplet-like fruiting bodies during starvation.
Edit intent: fluency
Original:  Our results suggest that cell motility and mechanical cell-cell interactions are sufficient to induce the formation of cell layers at topological defects, thereby seeding fruiting bodies in colonies of M. xanthus .
Revised:  Our results suggest that cell motility and mechanical cell-cell interactions are sufficient to induce the formation of cell layers at topological defects, thereby seeding fruiting bodies in colonies of M. xanthus .
Edit intent: fluency
Original:  Relationships may fail, and given this idosyncratic risk , firms multisource inputs and make costly investments to make relationships with suppliers stronger (less likely to fail). We find that aggregate production is discontinuous in the strength of these relationships.
Revised:  Relationships may fail, and given this idosyncratic risk , firms multisource inputs and make costly investments to make relationships with suppliers stronger . In equilibrium, aggregate production is discontinuous in the strength of these relationships.
Edit intent: clarity
Original:  We give conditions under which the supply network is endogenously fragile , so that arbitrarily small negative shocks to relationship strength lead to a large, discontinuous drop in aggregate output .
Revised:  We give conditions under which the equilibrium network is driven to a fragile configuration, where arbitrarily small aggregate shocks cause discontinuous losses. We use the model to provide a unified account of a number of stylized facts about complex economies .
Edit intent: clarity
Original:  We give conditions under which the equilibrium network is driven to a fragile configuration, where arbitrarily small aggregate shocks cause discontinuous losses. We use the model to provide a unified account of a number of stylized facts about complex economies .
Revised:  We give conditions under which the equilibrium network is driven to a fragile configuration, where arbitrarily small aggregate shocks to relationships causing arbitrarily steep drops in output. The endogenous configuration of supply networks provides a new channel for the powerful amplification of shocks .
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Original:  Due to the risk of such supply relationships being idiosyncratically disrupted, firms multisource inputs and strategically invest to make relationships with suppliers stronger. Aggregate production is robust to idiosyncratic disruptions.
Revised:  Due to the risk of such supply relationships being idiosyncratically disrupted, firms multisource inputs and strategically invest to make relationships stronger, trading off the cost of investment against the benefits of increased robustness. We find that equilibrium aggregate production is robust to idiosyncratic disruptions.
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Original:  This model also resolves two major drawbacks of neutral evolution models: their failure to explain balancing selection without resorting to fitness  and the unrealistic time required for the creation of the observed large types.
Revised:  This model also resolves two major drawbacks of neutral evolution models: their failure to explain balancing selection without resorting to fitness differences and the unrealistic time required for the creation of the observed large types.
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Original: This project describes an approach to analyze public sentiments with social media data and provides an example of the Twitter discourse during the 2019 Chinese National Day.
Revised: As the Internet gradually penetrates into people's daily lives and empowers everyone to demonstrate and exchange opinions and sentiments online, individual citizens are increasingly participating in the agenda-setting of public affairs and the design and implementation of official policies. The current study describes an approach to analyze public sentiments with social media data and provides an example of the Twitter discourse during the 2019 Chinese National Day.
Edit intent: clarity
Original:  The objective is to study the online discourse towards China with NLP algorithms,as well as observe the temporal, spatial and lingual characteristics of the expressed sentiments. Firstly, the Twitter data sets were collected between Sept 30 and Oct 3 through API and part of them were manually labeled to train the SVM. Then, a hybrid method of SVM and dictionary was applied to evaluate the sentiments of the collected tweets.
Revised:  Over 300,000 tweets were collected between Sept 30 and Oct 3 through API and part of them were manually labeled to train the SVM. Then, a hybrid method of SVM and dictionary was applied to evaluate the sentiments of the collected tweets.
Edit intent: coherence
Original:  Firstly, the Twitter data sets were collected between Sept 30 and Oct 3 through API and part of them were manually labeled to train the SVM. Then, a hybrid method of SVM and dictionary was applied to evaluate the sentiments of the collected tweets.
Revised:  Firstly, the Twitter data sets were collected between Sept 30 and Oct 3 , and a hybrid method of SVM and dictionary was applied to evaluate the sentiments of the collected tweets.
Edit intent: coherence
Original:  After that, the tweets sentiments' time fluctuation, spatial distribution and frequently used words were given. Finally, we conclude by highlighting the possible consequences of the overall negative image of China in the English-speaking discourses and indicating future directions .
Revised:  This method avoids complex structures while yielding an average accuracy of over 96\% in most classifiers used in the study. The results indicate alignment between the time of National Day celebration activities and the expressed sentiments revealed in both English and Chinese tweets, although the sentiments of the two languages tend to be in different directions. The sentiment of tweets also diverges from nation to nation, but is generally consistent with the country's official relations with China to varying degrees. The linguistic features of the tweets suggest different concerns for Twitter users who have different sentiments towards China. At last, possible directions for further studies are indicated .
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Original:  Experimental results show ProphetNet achieves the best performance on both abstractive summarization and question generation tasks compared to the models using the same base scale pre-training dataset.
Revised:  Then we conduct experiments on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks for abstractive summarization and question generation tasks compared to the models using the same base scale pre-training dataset.
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Original:  Experimental results show ProphetNet achieves the best performance on both abstractive summarization and question generation tasks compared to the models using the same base scale pre-training dataset. For the large scale dataset pre-training, ProphetNet achieves new state-of-the-art results on Gigaword and comparable results on CNN/DailyMail using only about 1/5 pre-training epochs of the previous model .
Revised:  Experimental results show ProphetNet achieves the best performance on both abstractive summarization and question generation tasks . Experimental results show that ProphetNet achieves new state-of-the-art results on Gigaword and comparable results on CNN/DailyMail using only about 1/5 pre-training epochs of the previous model .
Edit intent: clarity
Original:  For the large scale dataset pre-training, ProphetNet achieves new state-of-the-art results on Gigaword and comparable results on CNN/DailyMail using only about 1/5 pre-training epochs of the previous model .
Revised:  For the large scale dataset pre-training, ProphetNet achieves new state-of-the-art results on all these datasets compared to the models using the same scale pre-training epochs of the previous model .
Edit intent: clarity
Original:  For the large scale dataset pre-training, ProphetNet achieves new state-of-the-art results on Gigaword and comparable results on CNN/DailyMail using only about 1/5 pre-training epochs of the previous model .
Revised:  For the large scale dataset pre-training, ProphetNet achieves new state-of-the-art results on Gigaword and comparable results on CNN/DailyMail using only about 1/5 pre-training corpus .
Edit intent: clarity
Original:  We examine the accuracy of the reconstructed interbank network by comparing the actual data and analyze the characteristics of the interbank network . The comparison confirms that the accuracy of the reconstruction model is acceptably good. For the reconstructed interbank network, we obtain the following characteristics  which are consistent with the previously known stylized facts : the short path length, the small clustering coefficient, the disassortative property, and the core and peripheral structure . Community analysis shows that the number of communities is 2-3 in the normal period, 1 in the economic crisis (2003, 2008-2013).
Revised:  We examine the accuracy of the reconstructed interbank network by calculating important network attributes. We obtain the following characteristics which are consistent with the previously known stylized facts : the short path length, the small clustering coefficient, the disassortative property, and the core and peripheral structure . Community analysis shows that the number of communities is 2-3 in the normal period, 1 in the economic crisis (2003, 2008-2013).
Edit intent: clarity
Original:  For the reconstructed interbank network, we obtain the following characteristics  which are consistent with the previously known stylized facts : the short path length, the small clustering coefficient, the disassortative property, and the core and peripheral structure . Community analysis shows that the number of communities is 2-3 in the normal period, 1 in the economic crisis (2003, 2008-2013).
Revised:  For the reconstructed interbank network, we obtain the following characteristics , which are consistent with the previously known stylized facts : the short path length, the small clustering coefficient, the disassortative property, and the core and peripheral structure . Community analysis shows that the number of communities is 2-3 in the normal period, 1 in the economic crisis (2003, 2008-2013).
Edit intent: fluency
Original:  For the reconstructed interbank network, we obtain the following characteristics  which are consistent with the previously known stylized facts : the short path length, the small clustering coefficient, the disassortative property, and the core and peripheral structure . Community analysis shows that the number of communities is 2-3 in the normal period, 1 in the economic crisis (2003, 2008-2013).
Revised:  For the reconstructed interbank network, we obtain the following characteristics which are consistent with the previously known stylized facts . Although we do not introduce the mechanism to generate the core and peripheral structure . Community analysis shows that the number of communities is 2-3 in the normal period, 1 in the economic crisis (2003, 2008-2013).
Edit intent: meaning-changed
Original:  For the reconstructed interbank network, we obtain the following characteristics  which are consistent with the previously known stylized facts : the short path length, the small clustering coefficient, the disassortative property, and the core and peripheral structure . Community analysis shows that the number of communities is 2-3 in the normal period, 1 in the economic crisis (2003, 2008-2013). The major nodes in each community have been the major commercial banks.
Revised:  For the reconstructed interbank network, we obtain the following characteristics which are consistent with the previously known stylized facts : the short path length, the small clustering coefficient, the disassortative property, and the core and peripheral structure , we impose the constraints to consider the sparsity that is no transactions within the same bank category except for major commercial banks, the core and peripheral structure has spontaneously emerged. We identify major nodes in each community have been the major commercial banks.
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Original:  In a second step, we use these fields to perform  Langevin simulations which account for the desired molecular kinetics . By introducing the concept of 'temperature boosting' of the Langevin simulations, this combination of methods allows simulation of biomolecular processes occurring on multisecond timescales and beyond.
Revised:  In a second step, we use these fields to perform Langevin simulations which account for the desired molecular kinetics occurring on multisecond timescales and beyond.
Edit intent: coherence
Original:  we are able to reproduce the rates from atomistic molecular dynamics simulation and experiments within a factor of 1.5-4 for unbinding times up to the range of milliseconds and of 1.2-10 in the range of seconds . Analysis of the friction profiles reveals that binding and unbinding dynamics are mediated by changes of the surrounding hydration shells in all investigated systems.
Revised:  we are able to reproduce rates from molecular dynamics simulation and experiments within a factor of 1.5-4 for unbinding times up to the range of milliseconds and of 1.2-10 in the range of seconds . Analysis of the friction profiles reveals that binding and unbinding dynamics are mediated by changes of the surrounding hydration shells in all investigated systems.
Edit intent: clarity
Original:  we are able to reproduce the rates from atomistic molecular dynamics simulation and experiments within a factor of 1.5-4 for unbinding times up to the range of milliseconds and of 1.2-10 in the range of seconds . Analysis of the friction profiles reveals that binding and unbinding dynamics are mediated by changes of the surrounding hydration shells in all investigated systems.
Revised:  we are able to reproduce the rates from atomistic molecular dynamics simulation and experiments within a factor of 2-20, and dissociation constants within a factor of 1-4 . Analysis of the friction profiles reveals that binding and unbinding dynamics are mediated by changes of the surrounding hydration shells in all investigated systems.
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Original: In this study, we explore the identification and estimation of the quantile treatment effects (QTE) using panel data. We generalize the change-in-changes (CIC) model proposed by Athey and Imbens (2006) and propose a tractable estimator of the QTE. The CIC model allows for the estimation of the potential outcomes distribution and captures the heterogeneous effects of the treatment on the outcomes .
Revised: In this study, we explore the identification and estimation of the quantile treatment effects (QTE) under the rank invariance and rank stationarity assumptions. Ishihara (2020) explores identification of the nonseparable panel data model under these assumptions and propose a parametric estimation based on the minimum distance method .
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Original:  However, the CIC model has the following two problems: (1) there lacks a tractable estimator in the presence of covariates and (2) the CIC estimator does not work when the treatment is continuous. Our model allows for the presence of covariates and continuous treatment. We propose a two-step estimation method based on a quantile regression and minimum distance method.
Revised:  However, the minimum distance estimation using this process is computationally demanding when the dimensionality of covariates is large. To overcome this problem, we propose a two-step estimation method based on a quantile regression and minimum distance method.
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Original:  Our examples show how a diverse set of computationally-difficult modeling issues across scientific disciplines, from automatically discovering biological mechanisms to accelerating climate simulationsby 15, 000x, can be handled by training UDEs .
Revised:  Our examples show how a diverse set of computationally-difficult modeling issues across scientific disciplines, from automatically discovering biological mechanisms to accelerating the training of physics-informed neural networks and large-eddy simulations, can all be transformed into UDE training problems that are efficiently solved by a single software methodology .
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Original:  Rooted in a long tradition in knowledge representation, all popular KBs only store positive information, while they abstain from taking any stance towards statements not contained in them. In this paper , we make the case for explicitly stating interesting statements which are not true .
Revised:  Rooted in a long tradition in knowledge representation, all popular KBs only store positive information, while they abstain from taking any stance on statements not stored in the KB. This paper makes the case for explicitly stating interesting statements which are not true .
Edit intent: clarity
Original:  Negative statements would be important to overcome current limitations of question answering, yet due to their potential abundance , any effort towards compiling them needs a tight coupling with ranking . We introduce two approaches towards compiling negative statements. (i) In peer-based statistical inferences,we compare entities with highly related entities in order to derive potential negative statements, which we then rank using supervised and unsupervised features.
Revised:  Negative statements would be important to overcome current limitations of question answering, yet due to their potential abundance , any effort to compile them needs to address ranking by saliency. We present a statistical inference method for compiling and ranking negative statements,based on expectations from positive statements of related entities in order to derive potential negative statements, which we then rank using supervised and unsupervised features.
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Original:  (i) In peer-based statistical inferences,we compare entities with highly related entities in order to derive potential negative statements, which we then rank using supervised and unsupervised features. (ii) In query-log-based text extraction, we use a pattern-based approach for harvesting search engine query logs. Experimental resultsshow that both approaches hold promising and complementary potential. Along with this paper, we publish the first datasets on interesting negative information, containing over 1.1M statementsfor 100K popular Wikidata entities .
Revised:  (i) In peer-based statistical inferences,we compare entities with highly related entities in peer groups. Experimental results, with a variety of datasets, show that the method can effectively discover notable negative statements, and extrinsic studies underline their usefulness for entity summarization. Datasets and code are released as resources for further research .
Edit intent: clarity