wanyu/IteraTeR_v2 huggingface dataset:
Original:  The strategies are analyzed using a collection of news from the The Guardian newspaper, labeled by the authors, the 20 newsgroups data set , and the Reuters-21578 collection.
Revised:  The strategies are analyzed using a collection of news from the The Guardian newspaper, labeled by the authors, the 20 Newsgroups and Reuters-21578 collection.
Edit intent: clarity
Original:  The results demonstrate that ,  despite its simplicity, our method is competitive with state-of-the-art methods and has the important advantage of offering flexibility at the moment of adjusting to specific task goals  .
Revised:  The results demonstrate that , despite its simplicity, our method is competitive with state-of-the-art methods and has the important advantage of offering flexibility at the moment of adapting to specific task goals .
Edit intent: clarity
Original: We propose a multi-agent variant of the classical multi-armed bandit problem, in which there are N agents and K arms, and pulling an arm generates a (possibly different) stochastic reward to each agent.
Revised: We propose a multi-agent variant of the classical multi-armed bandit problem, in which there are N agents and K arms, and pulling an arm generates a (possibly different) stochastic reward for each agent.
Edit intent: fluency
Original:  Unlike the classical multi-armed bandit problem, the goal is not to learn the "best arm" , as each agent may perceive a different arm as best for her  .
Revised:  Unlike the classical multi-armed bandit problem, the goal is not to learn the "best arm" ; indeed, each agent may perceive a different arm as best for her .
Edit intent: clarity
Original:  Unlike the classical multi-armed bandit problem, the goal is not to learn the "best arm" , as each agent may perceive a different arm as best for her  .
Revised:  Unlike the classical multi-armed bandit problem, the goal is not to learn the "best arm" , as each agent may perceive a different arm to be the best for her .
Edit intent: fluency
Original:  Unlike the classical multi-armed bandit problem, the goal is not to learn the "best arm" , as each agent may perceive a different arm as best for her  .
Revised:  Unlike the classical multi-armed bandit problem, the goal is not to learn the "best arm" , as each agent may perceive a different arm as best for her personally .
Edit intent: clarity
Original:  Instead, we seek to learn a fair distribution over  arms.
Revised:  Instead, we seek to learn a fair distribution over the arms.
Edit intent: fluency
Original:  We design multi-agent variants of three classic multi-armed bandit algorithms , and show that they achieve sublinear regret,  now measured in terms of the  Nash social welfare.
Revised:  We design multi-agent variants of three classic multi-armed bandit algorithms and show that they achieve sublinear regret, now measured in terms of the Nash social welfare.
Edit intent: fluency
Original:  We design multi-agent variants of three classic multi-armed bandit algorithms , and show that they achieve sublinear regret,  now measured in terms of the  Nash social welfare.
Revised:  We design multi-agent variants of three classic multi-armed bandit algorithms , and show that they achieve sublinear regret, which is now measured in terms of the Nash social welfare.
Edit intent: coherence
Original:  For subject-by-subject alignment, using FG-W resulted in  alignment accuracy of 55 \pm 3 \% , whereas using CNN resulted in classification accuracy of 45%DIFDELCMD < \pm %DIFDELCMD < %%% 1 \%.
Revised:  For subject-by-subject alignment, using FG-W resulted in an alignment accuracy of 55 \pm 3 \% , whereas using CNN resulted in classification accuracy of 45%DIFDELCMD < \pm %DIFDELCMD < %%% 1 \%.
Edit intent: fluency
Original:  For subject-by-subject alignment, using FG-W resulted in  alignment accuracy of 55 \pm 3 \% , whereas using CNN resulted in classification accuracy of 45%DIFDELCMD < \pm %DIFDELCMD < %%% 1 \%.
Revised:  For subject-by-subject alignment, using FG-W resulted in alignment accuracy of 55 \pm %DIFDELCMD < \pm %DIFDELCMD < %%% 1 \%.
Edit intent: clarity
Original:  Where in both cases 25 \%  represents chance.
Revised:  Where in both cases 25 \% accuracy represents chance.
Edit intent: clarity
Original:  Conclusions: Domain adaptation is potential for session-by-session and subject-by-subject alignment  using fNIRS data.
Revised:  Conclusions: Domain adaptation has potential for session-by-session and subject-by-subject alignment using fNIRS data.
Edit intent: fluency
Original:  The proposed framework, designed for dealing  high dimensional random inputs, dovetails a polynomial dimensional decomposition (PDD) of multivariate stochastic response functions and deterministic topology derivatives.
Revised:  The proposed framework, designed for dealing with high dimensional random inputs, dovetails a polynomial dimensional decomposition (PDD) of multivariate stochastic response functions and deterministic topology derivatives.
Edit intent: fluency
Original: We describe an integration of program synthesis intoUclid5 , a formal modelling and verification tool.
Revised: We describe an integration of program synthesis into Uclid5 , a formal modelling and verification tool.
Edit intent: fluency
Original:  To the best of our knowledge, the new version ofUclid5is the only tool that supports program synthesis with bounded model checking, k-induction, sequential program verification, and hyperproperty verification.
Revised:  To the best of our knowledge, the new version of Uclid5 is the only tool that supports program synthesis with bounded model checking, k-induction, sequential program verification, and hyperproperty verification.
Edit intent: fluency
Original:  We use the integration to generate 25 pro-gram synthesis benchmarks with simple, known solutions that are out of reach of current synthesis engines, and we release the benchmarks to the community.
Revised:  We use the integration to generate 25 program synthesis benchmarks with simple, known solutions that are out of reach of current synthesis engines, and we release the benchmarks to the community.
Edit intent: clarity
Original:  Although a number of monoclonal antibodies against SARS-CoV-2  have been identified, we still lack an understanding of the overall landscape of B-cell receptor (BCR) repertoires in COVID-19 patients.
Revised:  Although a number of SARS-CoV-2 have been identified, we still lack an understanding of the overall landscape of B-cell receptor (BCR) repertoires in COVID-19 patients.
Edit intent: clarity
Original:  Here, we used high-throughput sequencing of BCR repertoires collected over multiple time points during an infection to characterize statistical and dynamical signatures of the B-cell response to SARS-CoV-2 in 19 patients with different disease severities.
Revised:  Here, we used high-throughput sequencing of BCR repertoires collected over multiple time points during infection to characterize statistical and dynamical signatures of the B-cell response to SARS-CoV-2 in 19 patients with different disease severities.
Edit intent: clarity
Original:  Here, we used high-throughput sequencing of BCR repertoires collected over multiple time points during an infection to characterize statistical and dynamical signatures of the B-cell response to SARS-CoV-2 in 19 patients with different disease severities.
Revised:  Here, we used high-throughput sequencing of BCR repertoires collected over multiple time points during an infection to characterize signatures of B-cell response to SARS-CoV-2 in 19 patients with different disease severities.
Edit intent: clarity
Original:  Based on principled statistical approaches, we determined differential sequence features of BCRs associated with different disease severity.
Revised:  Based on principled statistical approaches, we determined differential features of BCRs associated with different disease severity.
Edit intent: clarity
Original:  We identified 34 significantly expanded rare clonal lineages shared among patients as candidates for a specific response to SARS-CoV-2.
Revised:  We identified 34 significantly expanded rare clonal lineages shared among patients as candidates for specific responses to SARS-CoV-2.
Edit intent: fluency
Original:  Overall, our results provide important insights for development of rational therapies and vaccines against COVID-19.
Revised:  Our results provide important insights for development of rational therapies and vaccines against COVID-19.
Edit intent: coherence
Original:  We present the first comprehensive analysis of how the  data pipeline affects the training of the widely used Deep Neural Networks (DNNs).
Revised:  We present the first comprehensive analysis of how the data pipeline affects the training time of widely-used Deep Neural Networks (DNNs).
Edit intent: clarity
Original:  We find that in many cases, DNN training time is dominated by data stall time: time spent waiting for data to be fetched from storage and pre-processed.
Revised:  We find that in many cases, DNN training time is dominated by data stall time: time spent waiting for data to be fetched and pre-processed.
Edit intent: clarity
Original:  Our experiments on a range of DNN tasks, models, datasets, and hardware configurations show that CoorDL accelerates hyperparameter search by upto 5.7x, single-server training by upto 2x, and multi-server training by upto 15x compared to the state-of-the-art  data loading library DALI on PyTorch .
Revised:  Our experiments on a range of DNN tasks, models, datasets, and hardware configurations show that when PyTorch uses CoorDL instead of the state-of-the-art data loading library DALI on PyTorch .
Edit intent: clarity
Original:  While prior research has explored many different ways of reducing DNN training time spanning different layers of the system stack , the impact of input data pipeline, i.e., fetching raw data items from storage and performing data pre-processing in memory, has been relatively unexplored.
Revised:  While prior research has explored many different ways of reducing DNN training time , the impact of input data pipeline, i.e., fetching raw data items from storage and performing data pre-processing in memory, has been relatively unexplored.
Edit intent: clarity
Original:  We find that in many cases, DNN training time is dominated by data stall time: time spent waiting for data to be fetched and pre-processed . (2) We build a tool, DS-Analyzer to precisely measure data stalls using a differential technique, and perform predictive what-if analysis on data stalls.
Revised:  We find that in many cases, DNN training time is dominated by data stall time: time spent waiting for data to be fetched and preprocessed . (2) We build a tool, DS-Analyzer to precisely measure data stalls using a differential technique, and perform predictive what-if analysis on data stalls.
Edit intent: fluency
Original:  Our experiments on a range of DNN tasks, models, datasets, and hardware configurations show that when PyTorch uses CoorDL instead of the state-of-the-art DALI data loading library, DNN training time is reduced significantly (by as much as 5x on a single server).
Revised:  Our experiments on a range of DNN tasks, models, datasets, and hardware configs show that when PyTorch uses CoorDL instead of the state-of-the-art DALI data loading library, DNN training time is reduced significantly (by as much as 5x on a single server).
Edit intent: fluency
Original:  Today's most generally applicable algorithms for estimating n-volumes and integrals are based on Markov Chain Monte Carlo (MCMC) methods ;
Revised:  The most generally applicable algorithms for estimating n-volumes and integrals are based on Markov Chain Monte Carlo (MCMC) methods ;
Edit intent: fluency
Original:  Today's most generally applicable algorithms for estimating n-volumes and integrals are based on Markov Chain Monte Carlo (MCMC) methods ;
Revised:  Today's most generally applicable algorithms for estimating n-volumes and integrals are based on Markov Chain Monte Carlo (MCMC) methods , and
Edit intent: fluency
Original:  We find that , across the aggregate dataset, median DoT and DoH response times are as much as 7 ms and 23.2 ms slower than conventional DNS . We study the effects of latency, bandwidth, and heterogeneity between Internet service providers on DNS performance and find that latency had the most significant effect on response times, particularly for DoH .
Revised:  We found that clients do not have to trade DNS performance for privacy. For certain resolvers, DoT was able to perform faster than DNS in median response times, particularly for DoH .
Edit intent: coherence
Original:  We find that , across the aggregate dataset, median DoT and DoH response times are as much as 7 ms and 23.2 ms slower than conventional DNS . We study the effects of latency, bandwidth, and heterogeneity between Internet service providers on DNS performance and find that latency had the most significant effect on response times, particularly for DoH .
Revised:  We find that , across the aggregate dataset, median DoT and DoH response times are as much as 7 ms and 23.2 ms slower than conventional DNS . We study the effects of latency, bandwidth, and heterogeneity between Internet service providers on DNS performance and find that latency had the most significant effect on response times, even as latency increased .
Edit intent: clarity
Original:  We also find that there can be significant variation in DNS performance between resolvers, with median query response times differing by as much as 23.7 ms .
Revised:  We also found significant variation in DNS performance between resolvers, with median query response times differing by as much as 23.7 ms .
Edit intent: clarity
Original:  This paper generalises a recently developed functional tensor-train (FTT) approximation of the inverse Rosenblatt transport 14 to a wide class of high-dimensional nonnegative functions, such as unnormalised probability density functions.
Revised:  This paper generalises the functional tensor-train (FTT) approximation of the inverse Rosenblatt transport 14 to a wide class of high-dimensional nonnegative functions, such as unnormalised probability density functions.
Edit intent: clarity
Original:  This paper generalises a recently developed functional tensor-train (FTT) approximation of the inverse Rosenblatt transport 14 to a wide class of high-dimensional nonnegative functions, such as unnormalised probability density functions.
Revised:  This paper generalises a recently developed functional tensor-train approximation of the inverse Rosenblatt transport 14 to a wide class of high-dimensional nonnegative functions, such as unnormalised probability density functions.
Edit intent: clarity
Original:  This paper generalises a recently developed functional tensor-train (FTT) approximation of the inverse Rosenblatt transport 14 to a wide class of high-dimensional nonnegative functions, such as unnormalised probability density functions.
Revised:  This paper generalises a recently developed functional tensor-train (FTT) approximation of the inverse Rosenblatt transport 14 to a wide class of high-dimensional non-negative functions, such as unnormalised probability density functions.
Edit intent: fluency
Original:  More crucially, we integrate the proposed monotonicity-preserving FTT transport into a nested variable transformation framework inspired by  deep neural networks.
Revised:  More crucially, we integrate the proposed order-preserving functional tensor-train transport into a nested variable transformation framework inspired by deep neural networks.
Edit intent: clarity
Original:  We demonstrate the efficacy of the proposed approach on a range of applications in statistical learning and uncertainty quantification, including parameter estimation for dynamical systems and inverse problems constrained by partial differential equations.
Revised:  We demonstrate the efficiency of the proposed approach on a range of applications in statistical learning and uncertainty quantification, including parameter estimation for dynamical systems and inverse problems constrained by partial differential equations.
Edit intent: clarity
Original:  Finally, we show that in the extension of the Collatz process to numbers with infinite binary expansions (2-adic integers) [Lagarias, 1985], our automaton constructs Collatz cycles and encodes the cyclic Collatz conjecture as a natural reachability problem.
Revised:  Finally, we show that in the extension of the Collatz process to numbers with infinite binary expansions (2-adic integers) [Lagarias, 1985], our automaton encodes the cyclic Collatz conjecture as a natural reachability problem.
Edit intent: clarity
Original:  To this date,  little is known about what sources Wikipedia relies on, in part because extracting citations and identifying cited sources is challenging.
Revised:  To this date, relatively little is known about what sources Wikipedia relies on, in part because extracting citations and identifying cited sources is challenging.
Edit intent: clarity
Original:  We were thus able to extract 4.0M citations to scholarly publications with known identifiers -- including DOI, PMC, PMID, and ISBN -- and further labeled an extra 261K citations with DOIs from Crossref.
Revised:  We were thus able to extract 4.0M citations to scholarly publications with known identifiers -- including DOI, PMC, PMID, and ISBN -- and further equip an extra 261K citations with DOIs from Crossref.
Edit intent: clarity
Original:  We release all our code to allow the community to extend upon our work and update the dataset in the future.
Revised:  We release our code to allow the community to extend upon our work and update the dataset in the future.
Edit intent: clarity
Original:  Previously, the only known solution for this problem with a sub-linear competitive ratio was randomized Alon et al. , TALG 2006].
Revised:  Previously, the only known solution for this problem with a sub-linear competitive ratio was randomized Alon et al. TALG 2006].
Edit intent: fluency
Original:  Our approach is based on solving a different fractional relaxation than that of Alon et al. , where we combine dual fitting and multiplicative weight updates approaches .
Revised:  Our approach is based on solving a different fractional relaxation than that of Alon et al. , where we combine dual fitting and multiplicative weight updates approach .
Edit intent: fluency
Original:  By maintaining certain  monotonicity properties of the created fractional solution, we are able to handle the dependencies between facilities and connections in a rounding routine.
Revised:  By maintaining certain additional monotonicity properties of the created fractional solution, we are able to handle the dependencies between facilities and connections in a rounding routine.
Edit intent: clarity
Original:  By maintaining certain  monotonicity properties of the created fractional solution, we are able to handle the dependencies between facilities and connections in a rounding routine.
Revised:  By maintaining certain monotonicity properties of the created fractional solution, we can handle the dependencies between facilities and connections in a rounding routine.
Edit intent: clarity
Original:  [FOCS 2011] implies the first deterministic algorithm for the online node-weighted Steiner tree problem.
Revised:  [FOCS 2011] yields the first deterministic algorithm for the online node-weighted Steiner tree problem.
Edit intent: clarity
Original:  We generalize the axiomatic characterization of Esteban and Ray (1994) and show that only a particular instance within this class can be used justifiably to measure polarization of networks.
Revised:  We generalize the axiomatic characterization of Esteban and Ray (1994) and show that only a particular instance within this class can be used justifiably to measure polarization in networks.
Edit intent: clarity
Original:  We present an algorithm called SUPER ^{\ast , inspired by the A^{\ast algorithm, for this goal.
Revised:  We present an algorithm called ^{\ast , inspired by the A^{\ast algorithm, for this goal.
Edit intent: clarity
Original:  Moreover, under a community model for the similarities, we prove that SUPER ^{\ast is near-optimal whereas the popular baselines are considerably suboptimal.
Revised:  Moreover, under a community model for the similarities, we prove that ^{\ast is near-optimal whereas the popular baselines are considerably suboptimal.
Edit intent: coherence
Original:  In experiments on real data from ICLR 2018 and synthetic data, we find that SUPER ^{\ast considerably outperforms baselines deployed in existing systems, consistently reducing the number of papers with fewer than requisite bids by 50-75\% or more, and is also robust to various real world complexities.
Revised:  In experiments on real data from ICLR 2018 and synthetic data, we find that ^{\ast considerably outperforms baselines deployed in existing systems, consistently reducing the number of papers with fewer than requisite bids by 50-75\% or more, and is also robust to various real world complexities.
Edit intent: clarity
Original:  The UGA algorithm-spectral sparsifier with positive coefficients with sparsity }\le\lceil{\epsilon^2}}\rceil can output a \frac{(1+\epsilon)^2{(1-\epsilon)^2}} {1-O(\epsilon)}} -spectral sparsifier with \lceiln{\epsilon^2}\rceil edges in O(m+n^2/\epsilon^2) time  with m edges and n vertices for a general random graph satisfying a mild sufficient condition .
Revised:  The UGA algorithm-spectral sparsifier with positive coefficients with sparsity }\le\lceil\frac{n{\epsilon^2}}\rceil can output a \frac{(1+\epsilon)^2{(1-\epsilon)^2}} {1-O(\epsilon)}} -spectral sparsifier with \lceiln{\epsilon^2}\rceil edges in O(m+n^2/\epsilon^2) time with m edges and n vertices for a general random graph satisfying a mild sufficient condition .
Edit intent: clarity
Original:  The UGA algorithm-spectral sparsifier with positive coefficients with sparsity }\le\lceil{\epsilon^2}}\rceil can output a \frac{(1+\epsilon)^2{(1-\epsilon)^2}} {1-O(\epsilon)}} -spectral sparsifier with \lceiln{\epsilon^2}\rceil edges in O(m+n^2/\epsilon^2) time  with m edges and n vertices for a general random graph satisfying a mild sufficient condition .
Revised:  The UGA algorithm-spectral sparsifier with positive coefficients with sparsity }\le\lceil{\epsilon^2}}\rceil can output a \frac{(1+\epsilon)^2{(1-\epsilon)^2}} {1-O(\epsilon)}} -spectral sparsifier with \lceiln{\epsilon^2}\rceil edges in O(m+n^2/\epsilon^2) time from a graph with m edges and n vertices for a general random graph satisfying a mild sufficient condition .
Edit intent: clarity
Original:  The UGA algorithm-spectral sparsifier with positive coefficients with sparsity }\le\lceil{\epsilon^2}}\rceil can output a \frac{(1+\epsilon)^2{(1-\epsilon)^2}} {1-O(\epsilon)}} -spectral sparsifier with \lceiln{\epsilon^2}\rceil edges in O(m+n^2/\epsilon^2) time  with m edges and n vertices for a general random graph satisfying a mild sufficient condition .
Revised:  The UGA algorithm-spectral sparsifier with positive coefficients with sparsity }\le\lceil{\epsilon^2}}\rceil can output a \frac{(1+\epsilon)^2{(1-\epsilon)^2}} {1-O(\epsilon)}} -spectral sparsifier with \lceiln{\epsilon^2}\rceil edges in O(m+n^2/\epsilon^2) time with m edges and n vertices under some mild assumptions .
Edit intent: clarity
Original:  In his book `The Art of Computer Programming Vol. 4A' Knuth raised a stronger form of this conjecture (Problem 56 in  Section 2.1.3 , Chapter 7 ), which requires that the sequence of positions with which the first bit is swapped in each step of such an ordering has 2n+1 blocks of the same length, and each block is obtained by adding s=1 (modulo 2n+1) to the previous block.
Revised:  In his book `The Art of Computer Programming Vol. 4A' Knuth raised a stronger form of this conjecture (Problem 56 in Chapter 7, Section 2.1.3 , Chapter 7 ), which requires that the sequence of positions with which the first bit is swapped in each step of such an ordering has 2n+1 blocks of the same length, and each block is obtained by adding s=1 (modulo 2n+1) to the previous block.
Edit intent: fluency