wanyu/IteraTeR_full_sent huggingface dataset:
Original:  On the contrary, such problems have been pivotal in obtaining completeness results for PPAD and PPA, for several important problems, such as finding a Nash equilibrium [Daskalakis et al., 2009, Chen et al., 2009b] and  Necklace Splitting with 2 thieves [Filos-Ratsikas and Goldberg, 2019].
Revised:  On the contrary, such problems have been pivotal in obtaining completeness results for PPAD and PPA, such as the PPAD-completeness of finding a Nash equilibrium [Daskalakis et al., 2009, Chen et al., 2009b] and Necklace Splitting with 2 thieves [Filos-Ratsikas and Goldberg, 2019].
Edit intent: meaning-changed
Original:  On the contrary, such problems have been pivotal in obtaining completeness results for PPAD and PPA, for several important problems, such as finding a Nash equilibrium [Daskalakis et al., 2009, Chen et al., 2009b] and  Necklace Splitting with 2 thieves [Filos-Ratsikas and Goldberg, 2019].
Revised:  On the contrary, such problems have been pivotal in obtaining completeness results for PPAD and PPA, for several important problems, such as finding a Nash equilibrium [Daskalakis et al., 2009, Chen et al., 2009b] and the PPA-completeness of Necklace Splitting with 2 thieves [Filos-Ratsikas and Goldberg, 2019].
Edit intent: meaning-changed
Original:  Finally, using a different generalization of Tucker's Lemma (termed Z_p-star-Tucker), which we prove to be PPA-p-complete, we prove that p-thieves Necklace Splitting is in PPA-p   . All of our containment results are obtained through a new combinatorial proof for Z_p-versions of Tucker's lemma that is a natural generalization of the standard combinatorial proof of Tucker's lemma by Freund and Todd [1981].
Revised:  Finally, using a different generalization of Tucker's Lemma (termed Z_p-star-Tucker), which we prove to be PPA-p-complete, we prove that p-thief Necklace Splitting is in PPA-p . All of our containment results are obtained through a new combinatorial proof for Z_p-versions of Tucker's lemma that is a natural generalization of the standard combinatorial proof of Tucker's lemma by Freund and Todd [1981].
Edit intent: fluency
Original:  Finally, using a different generalization of Tucker's Lemma (termed Z_p-star-Tucker), which we prove to be PPA-p-complete, we prove that p-thieves Necklace Splitting is in PPA-p   . All of our containment results are obtained through a new combinatorial proof for Z_p-versions of Tucker's lemma that is a natural generalization of the standard combinatorial proof of Tucker's lemma by Freund and Todd [1981].
Revised:  Finally, using a different generalization of Tucker's Lemma (termed Z_p-star-Tucker), which we prove to be PPA-p-complete, we prove that p-thieves Necklace Splitting is in PPA-p . This latter result gives a new combinatorial proof for the Necklace Splitting theorem, the only proof of this nature other than that of Meunier . All of our containment results are obtained through a new combinatorial proof for Z_p-versions of Tucker's lemma that is a natural generalization of the standard combinatorial proof of Tucker's lemma by Freund and Todd [1981].
Edit intent: meaning-changed
Original:  Finally, using a different generalization of Tucker's Lemma (termed Z_p-star-Tucker), which we prove to be PPA-p-complete, we prove that p-thieves Necklace Splitting is in PPA-p   . All of our containment results are obtained through a new combinatorial proof for Z_p-versions of Tucker's lemma that is a natural generalization of the standard combinatorial proof of Tucker's lemma by Freund and Todd [1981].
Revised:  Finally, using a different generalization of Tucker's Lemma (termed Z_p-star-Tucker), which we prove to be PPA-p-complete, we prove that p-thieves Necklace Splitting is in PPA-p 2014 . All of our containment results are obtained through a new combinatorial proof for Z_p-versions of Tucker's lemma that is a natural generalization of the standard combinatorial proof of Tucker's lemma by Freund and Todd [1981].
Edit intent: fluency
Original:  Addressing the linear Gaussian causal model, we derive the finite-sample variance for both estimators and demonstrate that either estimator can dominate the other by an unbounded constant factor depending on the model parameters . Next, we derive an optimal estimator, which leverages all observed variables to strictly outperform the backdoor and frontdoor estimators  .
Revised:  Addressing the linear Gaussian causal model, we demonstrate that either estimator can dominate the other by an unbounded constant factor depending on the model parameters . Next, we derive an optimal estimator, which leverages all observed variables to strictly outperform the backdoor and frontdoor estimators .
Edit intent: coherence
Original:  Addressing the linear Gaussian causal model, we derive the finite-sample variance for both estimators and demonstrate that either estimator can dominate the other by an unbounded constant factor depending on the model parameters . Next, we derive an optimal estimator, which leverages all observed variables to strictly outperform the backdoor and frontdoor estimators  .
Revised:  Addressing the linear Gaussian causal model, we derive the finite-sample variance for both estimators and demonstrate that either estimator can dominate the other by an unbounded constant factor . Next, we derive an optimal estimator, which leverages all observed variables to strictly outperform the backdoor and frontdoor estimators .
Edit intent: clarity
Original:  Addressing the linear Gaussian causal model, we derive the finite-sample variance for both estimators and demonstrate that either estimator can dominate the other by an unbounded constant factor depending on the model parameters . Next, we derive an optimal estimator, which leverages all observed variables to strictly outperform the backdoor and frontdoor estimators  .
Revised:  Addressing the linear Gaussian causal model, we derive the finite-sample variance for both estimators and demonstrate that either estimator can dominate the other by an unbounded constant factor depending on the model parameters . Next, we derive an optimal estimator, which leverages all observed variables , and bound its finite-sample variance. We show that it strictly outperforms the backdoor and frontdoor estimators .
Edit intent: meaning-changed
Original:  Addressing the linear Gaussian causal model, we derive the finite-sample variance for both estimators and demonstrate that either estimator can dominate the other by an unbounded constant factor depending on the model parameters . Next, we derive an optimal estimator, which leverages all observed variables to strictly outperform the backdoor and frontdoor estimators  .
Revised:  Addressing the linear Gaussian causal model, we derive the finite-sample variance for both estimators and demonstrate that either estimator can dominate the other by an unbounded constant factor depending on the model parameters . Next, we derive an optimal estimator, which leverages all observed variables to strictly outperform the backdoor and frontdoor estimators and that this improvement can be unbounded .
Edit intent: clarity
Original:  We also present a procedure for combining two datasets, with confounders observed in one and mediators in the other . Finally, we evaluate our methods on both simulated data and the IHDP and JTPA datasets.
Revised:  We also present a procedure for combining two datasets, one with observed confounders and another with observed mediators . Finally, we evaluate our methods on both simulated data and the IHDP and JTPA datasets.
Edit intent: clarity
Original:  We stress that none of these ratios are accurately represented by the often misinterpreted case fatality ratio (CFR), the number of deaths to date divided by the total number of  infected cases to date.
Revised:  We stress that none of these ratios are accurately represented by the often misinterpreted case fatality ratio (CFR), the number of deaths to date divided by the total number of confirmed infected cases to date.
Edit intent: clarity
Original:  In order to understand how mites affect population dynamics of honeybees and the healthy status of colony , we propose and study a brood-adultbee-mite model with time lag from brood to adult bee .
Revised:  In order to understand how mites affect population dynamics of honeybees and a colony health , we propose and study a brood-adultbee-mite model with time lag from brood to adult bee .
Edit intent: clarity
Original:  In order to understand how mites affect population dynamics of honeybees and the healthy status of colony , we propose and study a brood-adultbee-mite model with time lag from brood to adult bee .
Revised:  In order to understand how mites affect population dynamics of honeybees and the healthy status of colony , we propose a brood-adult bee-mite model in which the time lag from brood to adult bee .
Edit intent: coherence
Original:  In order to understand how mites affect population dynamics of honeybees and the healthy status of colony , we propose and study a brood-adultbee-mite model with time lag from brood to adult bee .
Revised:  In order to understand how mites affect population dynamics of honeybees and the healthy status of colony , we propose and study a brood-adultbee-mite model with time lag from brood to adult is taken into account .
Edit intent: clarity
Original:  (b) Small brood' s infestation rate could stabilize all populations at the unique interior equilibrium under constant seasonality while driving the mite to die out when changes in seasonality is taken into account;
Revised:  (b) Small brood' infestation rates could stabilize all populations at the unique interior equilibrium under constant seasonality while driving the mite to die out when changes in seasonality is taken into account;
Edit intent: fluency
Original:  (b) Small brood' s infestation rate could stabilize all populations at the unique interior equilibrium under constant seasonality while driving the mite to die out when changes in seasonality is taken into account;
Revised:  (b) Small brood' s infestation rate could stabilize all populations at the unique interior equilibrium under constant seasonality while may drive the mite population to die out when changes in seasonality is taken into account;
Edit intent: clarity
Original:  (b) Small brood' s infestation rate could stabilize all populations at the unique interior equilibrium under constant seasonality while driving the mite to die out when changes in seasonality is taken into account;
Revised:  (b) Small brood' s infestation rate could stabilize all populations at the unique interior equilibrium under constant seasonality while driving the mite to die out when seasonality is taken into account;
Edit intent: clarity
Original:  To better understand the  evolution of COVID-19 in Brazil, we apply a Susceptible-Infectious-Quarantined-Recovered (SIQR) model to the analysis of data from the Brazilian Department of Health . Based on analyical and numerical results, as well on the data, the basic reproduction number is estimated to R_{0}=5.25.
Revised:  To better understand the early evolution of COVID-19 in Brazil, we apply a Susceptible-Infectious-Quarantined-Recovered (SIQR) model to the analysis of data from the Brazilian Department of Health . Based on analyical and numerical results, as well on the data, the basic reproduction number is estimated to R_{0}=5.25.
Edit intent: clarity
Original:  To better understand the  evolution of COVID-19 in Brazil, we apply a Susceptible-Infectious-Quarantined-Recovered (SIQR) model to the analysis of data from the Brazilian Department of Health . Based on analyical and numerical results, as well on the data, the basic reproduction number is estimated to R_{0}=5.25.
Revised:  To better understand the evolution of COVID-19 in Brazil, we apply a Susceptible-Infectious-Quarantined-Recovered (SIQR) model to the analysis of data from the Brazilian Department of Health , obtained from February 26, 2020 through March 25, 2020. Based on analyical and numerical results, as well on the data, the basic reproduction number is estimated to R_{0}=5.25.
Edit intent: meaning-changed
Original:  An implication of this preliminary observation is to suggest a potential route of investigation in the COVID-19 research field making use of an already-established literature on the interplay of local and systemic iron regulation,  respiratory infections and the hepcidin protein.
Revised:  An implication of this preliminary observation is to suggest a potential route of investigation in the coronavirus research field making use of an already-established literature on the interplay of local and systemic iron regulation, respiratory infections and the hepcidin protein.
Edit intent: meaning-changed
Original:  An implication of this preliminary observation is to suggest a potential route of investigation in the COVID-19 research field making use of an already-established literature on the interplay of local and systemic iron regulation,  respiratory infections and the hepcidin protein.
Revised:  An implication of this preliminary observation is to suggest a potential route of investigation in the COVID-19 research field making use of an already-established literature on the interplay of local and systemic iron regulation, cytokine-mediated inflammatory processes, respiratory infections and the hepcidin protein.
Edit intent: meaning-changed
Original:  adaptive clustering, which mimics self-isolation (SI) in local communities, or sharp instant clustering, which looks like frontiers closing (FC) between cities and countries ? Since in reality cross-community connections always survive, we can wonder how efficient is the excitation (illness) propagation through the entire clustered network which has some density of inter-cluster connections. Crucial difference between SI- and FC-networks is as follows: SI-networks are "adaptively grown" under condition of maximization of small cliques in the entire network, while FC-networks are "instantly created" byad hoc imposed borders.
Revised:  adaptive clustering, which mimics self-isolation (SI) in local communities, or sharp instant clustering, which looks like frontiers closing (FC) between cities and countries . SI-networks are "adaptively grown" under condition of maximization of small cliques in the entire network, while FC-networks are "instantly created" byad hoc imposed borders.
Edit intent: coherence
Original:  Crucial difference between SI- and FC-networks is as follows: SI-networks are "adaptively grown" under condition of maximization of small cliques in the entire network, while FC-networks are "instantly created" byad hoc imposed borders.
Revised:  Crucial difference between SI- and FC-networks is as follows: SI-networks are "adaptively grown" under condition of maximization of small cliques in the entire network, while FC-networks are "instantly created" ad hoc imposed borders.
Edit intent: fluency
Original:  Crucial difference between SI- and FC-networks is as follows: SI-networks are "adaptively grown" under condition of maximization of small cliques in the entire network, while FC-networks are "instantly created" byad hoc imposed borders.
Revised:  Crucial difference between SI- and FC-networks is as follows: SI-networks are "adaptively grown" under condition of maximization of small cliques in the entire network, while FC-networks are "instantly created" by imposed borders.
Edit intent: clarity
Original:  Crucial difference between SI- and FC-networks is as follows: SI-networks are "adaptively grown" under condition of maximization of small cliques in the entire network, while FC-networks are "instantly created" byad hoc imposed borders.
Revised:  Crucial difference between SI- and FC-networks is as follows: SI-networks are "adaptively grown" under condition of maximization of small cliques in the entire network, while FC-networks are "instantly created" byad hoc . Running the standard SIR model on clustered SI- and FC-networks, we demonstrate that the adaptive network clustering prohibits the epidemic spreading better than the instant clustering in the network with similar parameters.
Edit intent: meaning-changed
Original:  We found that SI model has scale-free property for degree distribution P(k)\sim k^{\eta} with surprisingly small critical exponent -2<\eta< -1. Running the standard SIR model on clustered SI- and FC-networks, we demonstrate that the adaptive network clustering caused by self-isolation in communities prohibits the epidemic spread better than the clustering due to instant boundaries closing .
Revised:  We found that SI model has scale-free property for degree distribution P(k)\sim k^{\eta} with small critical exponent -2<\eta< -1. Running the standard SIR model on clustered SI- and FC-networks, we demonstrate that the adaptive network clustering caused by self-isolation in communities prohibits the epidemic spread better than the clustering due to instant boundaries closing .
Edit intent: clarity
Original:  We found that SI model has scale-free property for degree distribution P(k)\sim k^{\eta} with surprisingly small critical exponent -2<\eta< -1. Running the standard SIR model on clustered SI- and FC-networks, we demonstrate that the adaptive network clustering caused by self-isolation in communities prohibits the epidemic spread better than the clustering due to instant boundaries closing .
Revised:  We found that SI model has scale-free property for degree distribution P(k)\sim k^{\eta} with surprisingly small critical exponent -2<\eta< -1 and argue that scale-free behavior emerges due to the randomness in the adaptive network clustering caused by self-isolation in communities prohibits the epidemic spread better than the clustering due to instant boundaries closing .
Edit intent: meaning-changed
Original:  We found that SI model has scale-free property for degree distribution P(k)\sim k^{\eta} with surprisingly small critical exponent -2<\eta< -1. Running the standard SIR model on clustered SI- and FC-networks, we demonstrate that the adaptive network clustering caused by self-isolation in communities prohibits the epidemic spread better than the clustering due to instant boundaries closing .
Revised:  We found that SI model has scale-free property for degree distribution P(k)\sim k^{\eta} with surprisingly small critical exponent -2<\eta< -1. Running the standard SIR model on clustered SI- and FC-networks, we demonstrate that the initial degree distributions and is absent for random regular graphs .
Edit intent: meaning-changed
Original:  We collect streaming data using the Twitter API from March 1, 2020 to date and  provide analysis of topic clusters and social sentiments related to important emerging policies such as "#socialdistancing" and "#workfromhome" .
Revised:  We collect streaming data using the Twitter API from March 1, 2020 to date and identify false, misleading and clickbait contents from collected Tweets. We provide analysis of topic clusters and social sentiments related to important emerging policies such as "#socialdistancing" and "#workfromhome" .
Edit intent: meaning-changed
Original:  We collect streaming data using the Twitter API from March 1, 2020 to date and  provide analysis of topic clusters and social sentiments related to important emerging policies such as "#socialdistancing" and "#workfromhome" .
Revised:  We collect streaming data using the Twitter API from March 1, 2020 to date and provide analysis of user accounts and misinformation spread across countries. In addition, we provide analysis of public sentiments on intervention policies such as "#socialdistancing" and "#workfromhome" .
Edit intent: meaning-changed
Original:  We collect streaming data using the Twitter API from March 1, 2020 to date and  provide analysis of topic clusters and social sentiments related to important emerging policies such as "#socialdistancing" and "#workfromhome" . We track emerging hashtags over time, and provide location and time sensitive analysis of sentiments. In addition, we study the challenging problem of misinformation on social media, and provide a detection method to identify false, misleading and clickbait contents from Twitter information cascades .
Revised:  We collect streaming data using the Twitter API from March 1, 2020 to date and provide analysis of topic clusters and social sentiments related to important emerging policies such as "#socialdistancing" and "#workfromhome" , and we track topics, and emerging hashtags and sentiments over countries .
Edit intent: coherence
Original:  To this end, we design a dashboard to track misinformation on popular social media news sharing platform - Twitter. The dashboard allows visibility into the social media discussions around Coronavirus and the quality of information shared on the platform, updated over time. We collect streaming data using the Twitter API from March 1, 2020 to date and identify false, misleading and clickbait contents from collected Tweets.
Revised:  To this end, we collected streaming data related to COVID-19 using the Twitter API from March 1, 2020 to date and identify false, misleading and clickbait contents from collected Tweets.
Edit intent: meaning-changed
Original:  We collect streaming data using the Twitter API from March 1, 2020 to date and identify false, misleading and clickbait contents from collected Tweets. We provide analysis of user accounts and misinformation spread across countries .
Revised:  We collect streaming data using the Twitter API from March 1, 2020. We identified unreliable and misleading contents based on fact-checking sources, and examined the narratives promoted in misinformation tweets, along with the distribution of engagements with these tweets .
Edit intent: meaning-changed
Original:  In addition, we provide analysis of public sentiments on intervention policies such as "#socialdistancing" and "#workfromhome", and we track topics, and emerging hashtags and sentiments over countries. The dashboard maintains an evolving list of misinformation cascades, sentiments and emerging trends over time, accessible online at URL Keywords.
Revised:  In addition, we provide examples of the spreading patterns of prominent misinformation tweets. The analysis is presented and updated on a publically accessible dashboard URL to track the nature of online discourse and misinformation about COVID-19 on Twitter from March 1 - June 5, 2020. The dashboard provides a daily list of identified misinformation tweets, along with topics, sentiments, and emerging trends over time, accessible online at URL Keywords.
Edit intent: meaning-changed
Original:  Machine learning methods provide a platform  for development of deployment-ready classification models robust to the smaller, more heterogeneous datasets typical of healthcare. Identification of promising classifiers is dependent, in part, on hyperparameter optimization (HO) , for which a number of approaches including grid search, random sampling and Bayesian optimization have been shown to be effective.
Revised:  Machine learning methods provide a platform for development of deployment-ready classification models . Prioritization of promising classifiers is dependent, in part, on hyperparameter optimization (HO) , for which a number of approaches including grid search, random sampling and Bayesian optimization have been shown to be effective.
Edit intent: clarity
Original:  Identification of promising classifiers is dependent, in part, on hyperparameter optimization (HO) , for which a number of approaches including grid search, random sampling and Bayesian optimization have been shown to be effective.
Revised:  Identification of promising classifiers is dependent, in part, on hyperparameter optimization (HO) for which a number of approaches including grid search, random sampling and Bayesian optimization have been shown to be effective.
Edit intent: fluency