wanyu/IteraTeR_full_sent huggingface dataset:
Original:  This article strives to show the unusual link between functional subsequential finite state transducers and Glushkov's construction. The methods and algorithms presented here were used to implement  compiler of regular expressions  .
Revised:  This article strives to show the unusual link between functional subsequential finite state transducers . Produced automata are very compact, as they contain only one state for each symbol (from input alphabet) of original expression and only one transition for each range of symbols, no matter how large. Such compactified ranges of transitions allow for efficient binary search lookup during automaton evaluation. All the methods and algorithms presented here were used to implement compiler of regular expressions .
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Original: Given a planar point set X, we study the convex shells and the convex layers . We prove that when X consists of points independently and uniformly sampled inside a convex polygon with k vertices , the expected number of vertices on the first t convex shells is O(kt\log{n\left(\right) and the expected number of vertices on the first t convex layers is O\left(kt^{3} \log{\frac{n \right).
Revised: We study the Tukey layers and convex layers of a planar point set, which consists of n points independently and uniformly sampled inside a convex polygon with k vertices , the expected number of vertices on the first t convex shells is O(kt\log{n\left(\right) and the expected number of vertices on the first t convex layers is O\left(kt^{3} \log{\frac{n \right).
Edit intent: meaning-changed
Original:  We prove that when X consists of points independently and uniformly sampled inside a convex polygon with k vertices , the expected number of vertices on the first t convex shells is O(kt\log{n\left(\right) and the expected number of vertices on the first t convex layers is O\left(kt^{3} \log{\frac{n \right).
Revised:  We prove that when X consists of points independently and uniformly sampled from a convex polygon with k vertices , the expected number of vertices on the first t convex shells is O(kt\log{n\left(\right) and the expected number of vertices on the first t convex layers is O\left(kt^{3} \log{\frac{n \right).
Edit intent: clarity
Original:  We prove that when X consists of points independently and uniformly sampled inside a convex polygon with k vertices , the expected number of vertices on the first t convex shells is O(kt\log{n\left(\right) and the expected number of vertices on the first t convex layers is O\left(kt^{3} \log{\frac{n \right).
Revised:  We prove that when X consists of points independently and uniformly sampled inside a convex polygon with k vertices . We show that the expected number of vertices on the first t convex shells is O(kt\log{n\left(\right) and the expected number of vertices on the first t convex layers is O\left(kt^{3} \log{\frac{n \right).
Edit intent: coherence
Original:  We prove that when X consists of points independently and uniformly sampled inside a convex polygon with k vertices , the expected number of vertices on the first t convex shells is O(kt\log{n\left(\right) and the expected number of vertices on the first t convex layers is O\left(kt^{3} \log{\frac{n \right).
Revised:  We prove that when X consists of points independently and uniformly sampled inside a convex polygon with k vertices , the expected number of vertices on the first t Tukey layers is O\left(\right) and the expected number of vertices on the first t convex layers is O\left(kt^{3} \log{\frac{n \right).
Edit intent: meaning-changed
Original:  We prove that when X consists of points independently and uniformly sampled inside a convex polygon with k vertices , the expected number of vertices on the first t convex shells is O(kt\log{n\left(\right) and the expected number of vertices on the first t convex layers is O\left(kt^{3} \log{\frac{n \right).
Revised:  We prove that when X consists of points independently and uniformly sampled inside a convex polygon with k vertices , the expected number of vertices on the first t convex shells is O(kt\log{n\left(kt\log(n/k)\right) and the expected number of vertices on the first t convex layers is O\left(kt^{3} \log{\frac{n \right).
Edit intent: meaning-changed
Original:  We prove that when X consists of points independently and uniformly sampled inside a convex polygon with k vertices , the expected number of vertices on the first t convex shells is O(kt\log{n\left(\right) and the expected number of vertices on the first t convex layers is O\left(kt^{3} \log{\frac{n \right).
Revised:  We prove that when X consists of points independently and uniformly sampled inside a convex polygon with k vertices , the expected number of vertices on the first t convex shells is O(kt\log{n\left(\right) and the expected number of vertices on the first t convex layers is O\left(kt^{3} \log(n/(kt^2)) \right).
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Original: A core problem in cognitive science and machine learning is to understand how humans derive semantic representations from perceptual objects, such as color from an apple, pleasantness from a musical chord, or trustworthiness from a face.
Revised: A core problem in cognitive science and machine learning is to understand how humans derive semantic representations from perceptual objects, such as color from an apple, pleasantness from a musical chord, or seriousness from a face.
Edit intent: clarity
Original:  However, MCMCP's binary choice paradigm generates relatively little information per trial, and its local proposal function makes it slow to explore the parameter space and find the modes of the distribution.
Revised:  However, while MCMCP has strong asymptotic properties, its binary choice paradigm generates relatively little information per trial, and its local proposal function makes it slow to explore the parameter space and find the modes of the distribution.
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Original:  The final experiments combine GSP with a state-of-the-art image synthesis network (StyleGAN) and a recent network interpretability technique (GANSpace), enabling GSP to efficiently explore high-dimensional perceptual spaces , and demonstrating how GSP can be a powerful tool for jointly characterizing semantic representations in humans and machines .
Revised:  The final experiments use GSP to navigate the latent space of a state-of-the-art image synthesis network (StyleGAN) and a recent network interpretability technique (GANSpace), enabling GSP to efficiently explore high-dimensional perceptual spaces , and demonstrating how GSP can be a powerful tool for jointly characterizing semantic representations in humans and machines .
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Original:  The final experiments combine GSP with a state-of-the-art image synthesis network (StyleGAN) and a recent network interpretability technique (GANSpace), enabling GSP to efficiently explore high-dimensional perceptual spaces , and demonstrating how GSP can be a powerful tool for jointly characterizing semantic representations in humans and machines .
Revised:  The final experiments combine GSP with a state-of-the-art image synthesis network (StyleGAN) , a promising approach for applying GSP to high-dimensional perceptual spaces , and demonstrating how GSP can be a powerful tool for jointly characterizing semantic representations in humans and machines .
Edit intent: clarity
Original:  The final experiments combine GSP with a state-of-the-art image synthesis network (StyleGAN) and a recent network interpretability technique (GANSpace), enabling GSP to efficiently explore high-dimensional perceptual spaces , and demonstrating how GSP can be a powerful tool for jointly characterizing semantic representations in humans and machines .
Revised:  The final experiments combine GSP with a state-of-the-art image synthesis network (StyleGAN) and a recent network interpretability technique (GANSpace), enabling GSP to efficiently explore high-dimensional perceptual spaces . We conclude by discussing future cognitive applications and ethical implications .
Edit intent: clarity
Original: We analyze social network data from Twitter to uncover early-warning signals of COVID-19 outbreaks in Europe in the winter season 2020 , before the first public announcements of local sources of infection were made.
Revised: We analyze data from Twitter to uncover early-warning signals of COVID-19 outbreaks in Europe in the winter season 2020 , before the first public announcements of local sources of infection were made.
Edit intent: clarity
Original: We analyze social network data from Twitter to uncover early-warning signals of COVID-19 outbreaks in Europe in the winter season 2020 , before the first public announcements of local sources of infection were made.
Revised: We analyze social network data from Twitter to uncover early-warning signals of COVID-19 outbreaks in Europe in the winter season 2019-2020 , before the first public announcements of local sources of infection were made.
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Original:  We demonstrate that these limitations can be overcome by addressing the generalization challenges in a recently-released dataset, gSCAN, which explicitly measures how well a robotic agent is able to interpret novel ideas grounded in vision, e.g., novel pairings of adjectives and nouns.
Revised:  We demonstrate that these limitations can be overcome by addressing the generalization challenges in a recently-released dataset, gSCAN, which explicitly measures how well an agent is able to interpret novel ideas grounded in vision, e.g., novel pairings of adjectives and nouns.
Edit intent: clarity
Original:  The key principle we employ is compositionality: that the compositional structure of networks should reflect the compositional structure of the problem domain they address, while allowing all other parameters and properties to be learned end-to-end with weak supervision.
Revised:  The key principle we employ is compositionality: that the compositional structure of networks should reflect the compositional structure of the problem domain they address, while allowing other parameters and properties to be learned end-to-end with weak supervision.
Edit intent: fluency
Original:  Crucially, our base network has the same state-of-the-art performance as prior work , 97\% execution accuracy, while at the same time generalizing its knowledge when prior work does not ; for example, achieving 95\% accuracy on novel adjective-noun compositions where previous work has 55\% average accuracy.
Revised:  Crucially, our base network has the same state-of-the-art performance as prior work , 97\% execution accuracy, while at the same time generalizing its knowledge when prior work does not . Our network also provides a level of interpretability that enables users to inspect what each part of networks learns.
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Original:  Benefits of this approach include (i) end-to-end training for detections, (ii) consistency with the fuzzy nature of intake gestures , and (iii) avoidance of hard-coded rules .
Revised:  Benefits of this approach include (i) end-to-end training for detections, (ii) simplified timing requirements for intake gesture labels , and (iii) avoidance of hard-coded rules .
Edit intent: clarity
Original:  Benefits of this approach include (i) end-to-end training for detections, (ii) consistency with the fuzzy nature of intake gestures , and (iii) avoidance of hard-coded rules .
Revised:  Benefits of this approach include (i) end-to-end training for detections, (ii) consistency with the fuzzy nature of intake gestures , and (iii) improved detection performance compared to existing approaches .
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Original:  Across two separate datasets, we quantify these benefits by showing relative F_1 score improvements between 2.0 \% and 6.2\% over the two-stage approach for intake detection and eating vs. drinking recognition tasks, for both video and inertial sensors.
Revised:  Across two separate datasets, we achieve relative F_1 score improvements between 2.0 \% and 6.2\% over the two-stage approach for intake detection and eating vs. drinking recognition tasks, for both video and inertial sensors.
Edit intent: clarity
Original:  Across two separate datasets, we quantify these benefits by showing relative F_1 score improvements between 2.0 \% and 6.2\% over the two-stage approach for intake detection and eating vs. drinking recognition tasks, for both video and inertial sensors.
Revised:  Across two separate datasets, we quantify these benefits by showing relative F_1 score improvements between 1.9 \% and 6.2\% over the two-stage approach for intake detection and eating vs. drinking recognition tasks, for both video and inertial sensors.
Edit intent: meaning-changed
Original:  Across two separate datasets, we quantify these benefits by showing relative F_1 score improvements between 2.0 \% and 6.2\% over the two-stage approach for intake detection and eating vs. drinking recognition tasks, for both video and inertial sensors.
Revised:  Across two separate datasets, we quantify these benefits by showing relative F_1 score improvements between 2.0 \% and 6.2\% over the two-stage approach for intake detection and eating /drinking detection tasks, for both video and inertial sensors.
Edit intent: clarity
Original:  or (ii) low-rank interactions and sparse interactions. We also provide strong evidence that (iii)repulsive costs make MOT intractable by showing that several such problemsof interest are NP-hard to solve--even approximately. These three structures are quite general, and collectively they encompass many (if not most) current MOT applications.
Revised:  or (ii) low-rank and sparse components. These two structures encompass many--if not most--current applications of MOT. In addition to providing the first poly(n,k)-time algorithms for a wide range of MOT problems, our results also provide better algorithms for MOT problems that are already known to be tractable: Our algorithms compute solutions which are exact and sparse. (Previous algorithms can do neither.)
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Original:  For an observable with lattice errors of order \alpha and an integrated autocorrelation time that grows like \mathrm{int}\propto a^{-z}, multilevel Monte Carlo (MLMC) can reduce the cost from O(\text{stat}^{-2}\text{disc}^{-(1+z)/\alpha}) to O(\epsilon_{\text{stat}}^{-2}\vert\log \epsilon_{\text{disc}} \vert^2+\epsilon_{\text{disc}}^{-1/\alpha}) .
Revised:  For observables with lattice errors of order \alpha and an integrated autocorrelation time that grows like \mathrm{int}\propto a^{-z}, multilevel Monte Carlo (MLMC) can reduce the cost from O(\text{stat}^{-2}\text{disc}^{-(1+z)/\alpha}) to O(\epsilon_{\text{stat}}^{-2}\vert\log \epsilon_{\text{disc}} \vert^2+\epsilon_{\text{disc}}^{-1/\alpha}) .
Edit intent: fluency
Original:  For an observable with lattice errors of order \alpha and an integrated autocorrelation time that grows like \mathrm{int}\propto a^{-z}, multilevel Monte Carlo (MLMC) can reduce the cost from O(\text{stat}^{-2}\text{disc}^{-(1+z)/\alpha}) to O(\epsilon_{\text{stat}}^{-2}\vert\log \epsilon_{\text{disc}} \vert^2+\epsilon_{\text{disc}}^{-1/\alpha}) .
Revised:  For an observable with lattice errors of order \alpha and integrated autocorrelation times that grow like \mathrm{int}\propto a^{-z}, multilevel Monte Carlo (MLMC) can reduce the cost from O(\text{stat}^{-2}\text{disc}^{-(1+z)/\alpha}) to O(\epsilon_{\text{stat}}^{-2}\vert\log \epsilon_{\text{disc}} \vert^2+\epsilon_{\text{disc}}^{-1/\alpha}) .
Edit intent: fluency
Original:  For an observable with lattice errors of order \alpha and an integrated autocorrelation time that grows like \mathrm{int}\propto a^{-z}, multilevel Monte Carlo (MLMC) can reduce the cost from O(\text{stat}^{-2}\text{disc}^{-(1+z)/\alpha}) to O(\epsilon_{\text{stat}}^{-2}\vert\log \epsilon_{\text{disc}} \vert^2+\epsilon_{\text{disc}}^{-1/\alpha}) .
Revised:  For an observable with lattice errors of order \alpha and an integrated autocorrelation time that grows like \mathrm{int}\propto a^{-z}, multilevel Monte Carlo (MLMC) reduces the cost from O(\text{stat}^{-2}\text{disc}^{-(1+z)/\alpha}) to O(\epsilon_{\text{stat}}^{-2}\vert\log \epsilon_{\text{disc}} \vert^2+\epsilon_{\text{disc}}^{-1/\alpha}) .
Edit intent: clarity
Original:  For an observable with lattice errors of order \alpha and an integrated autocorrelation time that grows like \mathrm{int}\propto a^{-z}, multilevel Monte Carlo (MLMC) can reduce the cost from O(\text{stat}^{-2}\text{disc}^{-(1+z)/\alpha}) to O(\epsilon_{\text{stat}}^{-2}\vert\log \epsilon_{\text{disc}} \vert^2+\epsilon_{\text{disc}}^{-1/\alpha}) . Even higher performance(\epsilon_{\text{stat}}^{-2}+\epsilon_{\text{disc}}^{-1/\alpha}).
Revised:  For an observable with lattice errors of order \alpha and an integrated autocorrelation time that grows like \mathrm{int}\propto a^{-z}, multilevel Monte Carlo (MLMC) can reduce the cost from O(\text{stat}^{-2}\text{disc}^{-(1+z)/\alpha}) to O(\epsilon_{\text{stat}}^{-2}\vert\log \epsilon_{\text{disc}} \vert^2+\epsilon_{\text{disc}}^{-1/\alpha}) or \mathcal{O(\epsilon_{\text{stat}}^{-2}+\epsilon_{\text{disc}}^{-1/\alpha}).
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Original:  Using all Scopus publications from 1996 to 2020, we analyze bibliometric data from over half a million researchers who have published with a Russian affiliation address at some point in their careers.
Revised:  Using an exhaustive set of over 2.4 million Scopus publications from 1996 to 2020, we analyze bibliometric data from over half a million researchers who have published with a Russian affiliation address at some point in their careers.
Edit intent: meaning-changed
Original:  Using all Scopus publications from 1996 to 2020, we analyze bibliometric data from over half a million researchers who have published with a Russian affiliation address at some point in their careers.
Revised:  Using all Scopus publications from 1996 to 2020, we analyze the affiliation addresses of more than 659,000 researchers who have published with a Russian affiliation address at some point in their careers.
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Original:  Migration of researchers is observed through the changes in their affiliation addresses . For the first time, we analyze origins and destinations of migrant researchers with respect to their fields and performance and compute net migration rates based on incoming and outgoing flows. Our results indicate that while Russia has been a donor country in the late 1990s and early 2000s, it has experienced a relatively symmetric circulation of researchers in more recent years. Using subject categories of publications, we quantify the impact of migration on each field of scholarship. Our analysis shows that Russia has suffered a net loss in almost all disciplines and more so in neuroscience, decision sciences, dentistry, biochemistry, and mathematics. For economics and environmental science, there is a relatively balanced circulation of researchers to and from Russia. Our substantive results reveal new aspects of international mobility in academia and its impact on a national science system which speak directly to policy development. Methodologically, our new approach of handling big data can be adopted as a framework of analysis for studying scholarly migration in other countries.
Revised:  Migration of researchers is observed through the changes in their affiliation addresses which alter the mode country of affiliation over different years. Only 5.2
Edit intent: meaning-changed
Original:  Using an exhaustive set of over 2.4 million Scopus publications from 1996 to 2020 , we analyze the affiliation addresses of more than 659,000 researchers who have published with a Russian affiliation address at some point in their careers.
Revised:  Using an exhaustive set of over 2.4 million Scopus publications , we analyze the affiliation addresses of more than 659,000 researchers who have published with a Russian affiliation address at some point in their careers.
Edit intent: clarity
Original:  Using an exhaustive set of over 2.4 million Scopus publications from 1996 to 2020 , we analyze the affiliation addresses of more than 659,000 researchers who have published with a Russian affiliation address at some point in their careers.
Revised:  Using an exhaustive set of over 2.4 million Scopus publications from 1996 to 2020 , we analyze all researchers who have published with a Russian affiliation address at some point in their careers.
Edit intent: clarity
Original:  Using an exhaustive set of over 2.4 million Scopus publications from 1996 to 2020 , we analyze the affiliation addresses of more than 659,000 researchers who have published with a Russian affiliation address at some point in their careers. Migration of researchers is observed through the changes in their affiliation addresses which alter the mode country of affiliation over different years.
Revised:  Using an exhaustive set of over 2.4 million Scopus publications from 1996 to 2020 , we analyze the affiliation addresses of more than 659,000 researchers who have published with a Russian affiliation address in Scopus-indexed sources in 1996-2020. The migration of researchers is observed through the changes in their affiliation addresses which alter the mode country of affiliation over different years.
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Original: We present a Conjugate Gradient (CG) implementation of the probabilistic numerical solver BayesCG , whose error estimates are a fully integrated design feature, easy to compute, and competitive with the best existing estimators.
Revised: The Bayesian Conjugate Gradient method (BayesCG) is a probabilistic generalization of the Conjugate Gradient method (CG) implementation of the probabilistic numerical solver BayesCG , whose error estimates are a fully integrated design feature, easy to compute, and competitive with the best existing estimators.
Edit intent: meaning-changed
Original: We present a Conjugate Gradient (CG) implementation of the probabilistic numerical solver BayesCG , whose error estimates are a fully integrated design feature, easy to compute, and competitive with the best existing estimators. More specifically, we extend BayesCG to singular prior covariances, derive recursions for the posterior covariances, express the posteriors as projections, and establish that BayesCG retains the minimization properties over Krylov spaces regardless of the singular priors. We introduce a possibly singular Krylov prior covariance, under which the BayesCG posterior means coincide with the CG iterates and the posteriors can be computed efficiently.
Revised: We present a Conjugate Gradient (CG) for solving linear systems with real symmetric positive definite coefficient matrices. We present a CG-based implementation of BayesCG with a structure-exploiting prior distribution. The BayesCG output consists of CG iterates and the posteriors can be computed efficiently.
Edit intent: clarity
Original:  We introduce a possibly singular Krylov prior covariance, under which the BayesCG posterior means coincide with the CG iterates and the posteriors can be computed efficiently. Because of its factored form, the Krylov prior is amenable to low-rank approximation, which produces an efficient BayesCG implementation as a CG method.
Revised:  We introduce a possibly singular Krylov prior covariance, under which the BayesCG posterior means coincide with the CG iterates and posterior covariances that can be propagated to subsequent computations. The covariances are low-rank approximation, which produces an efficient BayesCG implementation as a CG method.
Edit intent: clarity