wanyu/IteraTeR_full_sent huggingface dataset:
Original:  It then shows, based on this assumption, that there are means to balance back the economy such that all money minted by sybils is eventually retrieved and liquidated, so that - asymptotically - the amount of money minted equals the number of genuine identities , multiplied by the amount of time they were minting. Furthermore, we argue that this approach can serve as an incentive mechanism that deters the creation of sybils and incentivizes sybil hunting.
Revised:  It then shows, based on this assumption, that there are means to balance back the economy such that all money minted by sybils is eventually retrieved and liquidated, so that - asymptotically - the amount of money minted equals the number of genuine identities , multiplied by the time each agent was a member of the community. We then argue that this approach can serve as an incentive mechanism that deters the creation of sybils and incentivizes sybil hunting.
Edit intent: clarity
Original:  We find that \sigma_k/\mu_k \gtrsim 3.7 , where \mu_k is the mean infectiousness and \sigma_k is its standard deviation, which implies pervasive superspreading.
Revised:  We find that \sigma_k/\mu_k \gtrsim 3.2 , where \mu_k is the mean infectiousness and \sigma_k is its standard deviation, which implies pervasive superspreading.
Edit intent: meaning-changed
Original:  This result allows us to estimate that in the early stages of the pandemic in the USA, nearly 88 \\% of new cases were a result of the top 10\\% of most infectious individuals.
Revised:  This result allows us to estimate that in the early stages of the pandemic in the USA, nearly 81 \\% of new cases were a result of the top 10\\% of most infectious individuals.
Edit intent: meaning-changed
Original: A number of epidemics, including the SARS-CoV-1 epidemic of 2002-2004, have been known to exhibit superspreading, in which a small fraction of infected individuals are responsible for the majority of new infections.
Revised: A number of epidemics, including the SARS-CoV-1 epidemic of 2002-2004, have been known to exhibit superspreading, in which a small fraction of infected individuals is responsible for the majority of new infections.
Edit intent: fluency
Original:  We find that \sigma _k /\mu _k \gtrsim 3.2, where \mu _k is the mean infectiousness and \sigma _k is its standard deviation, which implies pervasive superspreading.
Revised:  We find that \sigma _\beta /\mu _k \gtrsim 3.2, where \mu _k is the mean infectiousness and \sigma _k is its standard deviation, which implies pervasive superspreading.
Edit intent: meaning-changed
Original:  We find that \sigma _k /\mu _k \gtrsim 3.2, where \mu _k is the mean infectiousness and \sigma _k is its standard deviation, which implies pervasive superspreading.
Revised:  We find that \sigma _k /\mu _\beta \gtrsim 3.2, where \mu _k is the mean infectiousness and \sigma _k is its standard deviation, which implies pervasive superspreading.
Edit intent: meaning-changed
Original:  We find that \sigma _k /\mu _k \gtrsim 3.2, where \mu _k is the mean infectiousness and \sigma _k is its standard deviation, which implies pervasive superspreading.
Revised:  We find that \sigma _k /\mu _k \gtrsim 3.2, where \mu _\beta is the mean infectiousness and \sigma _k is its standard deviation, which implies pervasive superspreading.
Edit intent: meaning-changed
Original:  We find that \sigma _k /\mu _k \gtrsim 3.2, where \mu _k is the mean infectiousness and \sigma _k is its standard deviation, which implies pervasive superspreading.
Revised:  We find that \sigma _k /\mu _k \gtrsim 3.2, where \mu _k is the mean infectiousness and \sigma _\beta its standard deviation, which implies pervasive superspreading.
Edit intent: meaning-changed
Original:  This result allows us to estimate that in the early stages of the pandemic in the USA, nearly 81 %DIFDELCMD < \\%%% %DIF < of new cases were a result of the top 10\\% of most infectious individuals.abstract 
Revised:  This result allows us to estimate that in the early stages of the pandemic in the USA, over 81 %DIFDELCMD < \\%%% %DIF < of new cases were a result of the top 10\\% of most infectious individuals.abstract 
Edit intent: clarity
Original:  This result allows us to estimate that in the early stages of the pandemic in the USA, nearly 81 %DIFDELCMD < \\%%% %DIF < of new cases were a result of the top 10\\% of most infectious individuals.abstract 
Revised:  This result allows us to estimate that in the early stages of the pandemic in the USA, nearly 81 %DIFDELCMD < \\%%% %DIF < of new cases were a result of the top 10\\% of most infectious individuals.abstract \% of new cases were a result of the top 10\% of most infectious individuals.
Edit intent: meaning-changed
Original:  With the aid of the previously obtained error bounds, we reveal a mean-square convergence rate of order one for the positivity preserving schemes  under more relaxed conditions, compared with existing relevant results  in the literature.
Revised:  With the aid of the previously obtained error bounds, we reveal a mean-square convergence rate of order one of the positivity preserving schemes under more relaxed conditions, compared with existing relevant results in the literature.
Edit intent: clarity
Original:  With the aid of the previously obtained error bounds, we reveal a mean-square convergence rate of order one for the positivity preserving schemes  under more relaxed conditions, compared with existing relevant results  in the literature.
Revised:  With the aid of the previously obtained error bounds, we reveal a mean-square convergence rate of order one for the positivity preserving schemes for the first time under more relaxed conditions, compared with existing relevant results in the literature.
Edit intent: meaning-changed
Original:  With the aid of the previously obtained error bounds, we reveal a mean-square convergence rate of order one for the positivity preserving schemes  under more relaxed conditions, compared with existing relevant results  in the literature.
Revised:  With the aid of the previously obtained error bounds, we reveal a mean-square convergence rate of order one for the positivity preserving schemes under more relaxed conditions, compared with existing relevant results for first order schemes in the literature.
Edit intent: meaning-changed
Original:  The results of studies on IMU and video data involving deep learning models are reported to provide a baseline for future research  .
Revised:  The results of studies on IMU and video data involving deep learning models are reported to provide a baseline for future research . Specifically, the best baseline models achieve performances of F_1 = 0.853 for the discrete dish using video and F_1 = 0.852 for the shared dish using inertial data .
Edit intent: meaning-changed
Original: We introduce an orientation-preserving landmark-based distance for continuous curves, which can be viewed as an alternative to the Fr%DIFDELCMD < \rme%%% chet \Frechet or Dynamic Time Warping distances.
Revised: We introduce an orientation-preserving landmark-based distance for continuous curves, which can be viewed as an alternative to the %DIFDELCMD < \rme%%% chet \Frechet or Dynamic Time Warping distances.
Edit intent: fluency
Original: We introduce an orientation-preserving landmark-based distance for continuous curves, which can be viewed as an alternative to the Fr%DIFDELCMD < \rme%%% chet \Frechet or Dynamic Time Warping distances.
Revised: We introduce an orientation-preserving landmark-based distance for continuous curves, which can be viewed as an alternative to the Fr%DIFDELCMD < \rme%%% \Frechet or Dynamic Time Warping distances.
Edit intent: fluency
Original:  We propose Hierarchical Contrastive Learning (HiCTL)  to (1) learn universal representations for parallel sentences distributed in one or multiple languages and (2) distinguish the semantically-related words from a shared cross-lingual vocabulary for each sentence.
Revised:  Specifically, we propose a Hierarchical Contrastive Learning (HiCTL) to (1) learn universal representations for parallel sentences distributed in one or multiple languages and (2) distinguish the semantically-related words from a shared cross-lingual vocabulary for each sentence.
Edit intent: coherence
Original:  We propose Hierarchical Contrastive Learning (HiCTL)  to (1) learn universal representations for parallel sentences distributed in one or multiple languages and (2) distinguish the semantically-related words from a shared cross-lingual vocabulary for each sentence.
Revised:  We propose Hierarchical Contrastive Learning (HiCTL) method to (1) learn universal representations for parallel sentences distributed in one or multiple languages and (2) distinguish the semantically-related words from a shared cross-lingual vocabulary for each sentence.
Edit intent: clarity
Original:  Experimental results show that the HiCTL obtains an absolute gain of 1.0\% /2.2\% accuracy on GLUE/XNLI as well as achieves substantial improvements  of +1.7-+3.6 BLEU on both the high-resource and low-resource English-to-X translation tasks over strong baselines .
Revised:  Experimental results show that the HiCTL outperforms the state-of-the-art XLM-R by an absolute gain of 1.0\% /2.2\% accuracy on GLUE/XNLI as well as achieves substantial improvements of +1.7-+3.6 BLEU on both the high-resource and low-resource English-to-X translation tasks over strong baselines .
Edit intent: meaning-changed
Original:  Experimental results show that the HiCTL obtains an absolute gain of 1.0\% /2.2\% accuracy on GLUE/XNLI as well as achieves substantial improvements  of +1.7-+3.6 BLEU on both the high-resource and low-resource English-to-X translation tasks over strong baselines .
Revised:  Experimental results show that the HiCTL obtains an absolute gain of 4.2\% accuracy on the XTREME benchmark as well as achieves substantial improvements of +1.7-+3.6 BLEU on both the high-resource and low-resource English-to-X translation tasks over strong baselines .
Edit intent: meaning-changed
Original:  Experimental results show that the HiCTL obtains an absolute gain of 1.0\% /2.2\% accuracy on GLUE/XNLI as well as achieves substantial improvements  of +1.7-+3.6 BLEU on both the high-resource and low-resource English-to-X translation tasks over strong baselines .
Revised:  Experimental results show that the HiCTL obtains an absolute gain of 1.0\% /2.2\% accuracy on GLUE/XNLI as well as achieves substantial improvements on both of +1.7-+3.6 BLEU on both the high-resource and low-resource English-to-X translation tasks over strong baselines .
Edit intent: meaning-changed
Original:  Experimental results show that the HiCTL obtains an absolute gain of 1.0\% /2.2\% accuracy on GLUE/XNLI as well as achieves substantial improvements  of +1.7-+3.6 BLEU on both the high-resource and low-resource English-to-X translation tasks over strong baselines .
Revised:  Experimental results show that the HiCTL obtains an absolute gain of 1.0\% /2.2\% accuracy on GLUE/XNLI as well as achieves substantial improvements of the high-resource and low-resource English-to-X translation tasks over strong baselines .
Edit intent: clarity
Original:  Experimental results show that the HiCTL obtains an absolute gain of 1.0\% /2.2\% accuracy on GLUE/XNLI as well as achieves substantial improvements  of +1.7-+3.6 BLEU on both the high-resource and low-resource English-to-X translation tasks over strong baselines . We will release the source codes as soon as possible .
Revised:  Experimental results show that the HiCTL obtains an absolute gain of 1.0\% /2.2\% accuracy on GLUE/XNLI as well as achieves substantial improvements of +1.7-+3.6 BLEU on both the high-resource and low-resource English-to-X translation tasks over strong baselines .
Edit intent: meaning-changed
Original: We develop a line-search second-order algorithmic framework for optimization problems in noisy environments, i. e., assuming that only noisy values are available for the objective function and its gradient and Hessian. In the general noisy case, almost sure convergence of the methods fitting into the framework is proved when line searches and suitably decaying step lengths are combined.
Revised: We develop a line-search second-order algorithmic framework for minimizing finite sums. We do not make any convexity assumptions, but require the terms of the sum to be continuously differentiable and have Lipschitz-continuous gradients. The methods fitting into the framework is proved when line searches and suitably decaying step lengths are combined.
Edit intent: meaning-changed
Original:  In the general noisy case, almost sure convergence of the methods fitting into the framework is proved when line searches and suitably decaying step lengths are combined. When the objective function is a finite sum, such as in machine learning applications, our framework is specialized as a stochastic L-BFGS method with line search only, with almost sure convergence to the solution. In this case, linear convergence rate of the expected function error is also proved , along with a worst-case \mathcal{O (\log(\varepsilon^{-1})) complexity bound} .  Numerical experiments, including comparisons with state-of-the art first- and second-order stochastic optimization methods, show the efficiency of our approach.
Revised:  In the general noisy case, almost sure convergence of the methods fitting into the framework is proved when line searches and suitably decaying step lengths (\log(\varepsilon^{-1})) complexity bound} . Numerical experiments, including comparisons with state-of-the art first- and second-order stochastic optimization methods, show the efficiency of our approach.
Edit intent: coherence
Original:  In this case, linear convergence rate of the expected function error is also proved , along with a worst-case \mathcal{O (\log(\varepsilon^{-1})) complexity bound} .  Numerical experiments, including comparisons with state-of-the art first- and second-order stochastic optimization methods, show the efficiency of our approach.
Revised:  In this case, linear convergence rate of the expected function error is also proved , along with a worst-case \mathcal{O (\log(\varepsilon^{-1})) complexity bound} . A key issue is a two-step sampling at each iteration, which allows us to control the error present in the line-search procedure. Stationarity of limit points is proved in the almost-sure sense, while almost-sure convergence of the sequence of approximations to the solution holds with the additional hypothesis that the functions are strongly convex. Numerical experiments, including comparisons with state-of-the art first- and second-order stochastic optimization methods, show the efficiency of our approach.
Edit intent: meaning-changed
Original:  In this case, linear convergence rate of the expected function error is also proved , along with a worst-case \mathcal{O (\log(\varepsilon^{-1})) complexity bound} .  Numerical experiments, including comparisons with state-of-the art first- and second-order stochastic optimization methods, show the efficiency of our approach.
Revised:  In this case, linear convergence rate of the expected function error is also proved , along with a worst-case \mathcal{O (\log(\varepsilon^{-1})) complexity bound} . Numerical experiments, including comparisons with state-of-the art stochastic optimization methods, show the efficiency of our approach.
Edit intent: meaning-changed
Original:  Motivated by a non-randomized open-label study investigating the efficacy of hydroxychloroquine in patients with COVID-19, we describe in a unified fashion various alternative approaches to the analysis of non-randomized studies and apply them to the example study exploring the question whether different methods might have led to different conclusions .
Revised:  Motivated by a non-randomized open-label study investigating the efficacy of hydroxychloroquine in patients with COVID-19, we describe in a unified fashion various alternative approaches to the analysis of non-randomized studies .
Edit intent: coherence
Original:  We evaluate the embeddings at  the intrinsic level and extrinsic level, by deploying them on the Transformer in named entity recognition (NER) task and conduct significance tests .
Revised:  We evaluate the embeddings at both the intrinsic and extrinsic levels. The embeddings are deployed with the intrinsic level and extrinsic level, by deploying them on the Transformer in named entity recognition (NER) task and conduct significance tests .
Edit intent: meaning-changed
Original:  We obtain better performance in both languages on the downstream task with far smaller training data, compared to recently released, common crawl versions and character n-grams appear useful for Swedish, a morphologically rich language.
Revised:  We obtain better performance in both languages on the downstream task with far smaller training data, compared to recently released, Common Crawl versions; and character n-grams appear useful for Swedish, a morphologically rich language.
Edit intent: fluency
Original:  The main differences between this modified SIR model and original SIR one are presented and explained in terms of the zeroes of their respective conserved quantities . We recall that both models have been recently used in order to describe the essentials of the dynamics of the COVID-19 pandemic .
Revised:  The main differences between this modified SIR model and original SIR one are presented and explained in terms of the zeroes of their respective conserved quantities .
Edit intent: coherence
Original:  The dynamics of this modified SIR system is analysed and the exact computation of some epidemiologically relevant quantities is performed , since both models have been recently used in order to describe the essentials of the dynamics of the COVID-19 pandemic .
Revised:  The dynamics of this modified SIR system is analyzed and the exact computation of some epidemiologically relevant quantities is performed , since both models have been recently used in order to describe the essentials of the dynamics of the COVID-19 pandemic .
Edit intent: fluency
Original:  The main differences between this modified SIR model and original SIR one are presented and explained in terms of the zeroes of their respective conserved quantities  .
Revised:  The main differences between this modified SIR model and original SIR one are presented and explained in terms of the zeroes of their respective conserved quantities . Moreover, it is shown that the modified SIR model with time-dependent transmission rate can be also solved in closed form for certain realistic transmission rate functions .
Edit intent: meaning-changed
Original: Many social services including online dating, social media, recruitment and online learning, largely rely on%DIFDELCMD < \matching %%% people with the right people". The success of these services and the user experience with them often depends on their ability to match users.Reciprocal Recommender Systems (RRS) arose to facilitate this process by identifying users who are a potential match for each other, based on information provided by them. These systems are inherently more complex than user-item recommendation approaches and unidirectional user recommendationservices, since they need to take into account both users ' preferences towards each other in the recommendation process. This entails not only predicting accurate preference estimates  as classical recommenders do, but also defining adequate fusion processes for aggregating user-to-user preferential information.
Revised: Many social services including online dating, social media, recruitment and online learning, largely rely on%DIFDELCMD < \matching %%% There exist situations of decision-making under information overload in the Internet, where people have an overwhelming number of available options to choose from, e.g. products to buy in an e-commerce site, or restaurants to visit in a large city. Recommender systems arose as a data-driven personalized decision support tool to assist users in these situations: they are able to process user-related data, filtering and recommending items based on the users preferences, needs and/or behaviour. Unlike most conventional recommender approaches where items are inanimate entities recommended to the users and success is solely determined upon the end users reaction to the recommendation(s) received, in a Reciprocal Recommender System (RRS) users become the item being recommended to other users. Hence, both the end user and the user being recommended should accept the 'matching' recommendation to yield a successful RRS performance. The operation of an RRS entails not only predicting accurate preference estimates as classical recommenders do, but also defining adequate fusion processes for aggregating user-to-user preferential information.
Edit intent: meaning-changed
Original:  This paper presents a snapshot analysis of the extant literature to summarize the state-of-the-art RRS research to date, focusing on the fundamental features that differentiate RRSs from other classes of recommender systems .
Revised:  This paper presents a snapshot-style analysis of the extant literature to summarize the state-of-the-art RRS research to date, focusing on the fundamental features that differentiate RRSs from other classes of recommender systems .
Edit intent: clarity
Original:  This paper presents a snapshot analysis of the extant literature to summarize the state-of-the-art RRS research to date, focusing on the fundamental features that differentiate RRSs from other classes of recommender systems .
Revised:  This paper presents a snapshot analysis of the extant literature that summarizes the state-of-the-art RRS research to date, focusing on the fundamental features that differentiate RRSs from other classes of recommender systems .
Edit intent: fluency
Original:  This paper presents a snapshot analysis of the extant literature to summarize the state-of-the-art RRS research to date, focusing on the fundamental features that differentiate RRSs from other classes of recommender systems .
Revised:  This paper presents a snapshot analysis of the extant literature to summarize the state-of-the-art RRS research to date, focusing on the algorithms, fusion processes and fundamental characteristics of RRS, both inherited from conventional user-to-item recommendation models and those inherent to this emerging family of approaches. Representative RRS models are likewise highlighted .
Edit intent: meaning-changed
Original:  The framework is general enough , i.e. parameters in the morphisms take values in arbitrary commutative semi-rings, from which any finitely dimensional system can be dealt with. Our proposal fits well into a compositional model of consciousness and is an important step forward that addresses both the hard problem of consciousness and the combination problem of (proto)-panpsychism .
Revised:  The framework is general enough and fits well into a compositional model of consciousness and is an important step forward that addresses both the hard problem of consciousness and the combination problem of (proto)-panpsychism .
Edit intent: clarity
Original:  Our exact algorithm runs in linear time when K^2 is O(N^C) for C < 1. Our (1 + \epsilon)-approximate algorithm runs in linear time when K\log(c_{ high }/(c_{ low }\epsilon)) is O(N^C) for C < 1, where c_{ high } and c_{ low } are upper and lower bounds on the optimal cost.
Revised:  Our exact algorithm runs in linear time when K^2 is O(N^C) for C < 1. Our (1 + \epsilon)-approximate algorithm runs in linear time when K\log(c_{ \text{high }/(c_{ low }\epsilon)) is O(N^C) for C < 1, where c_{ high } and c_{ low } are upper and lower bounds on the optimal cost.
Edit intent: meaning-changed