wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Our decremental hopset algorithm allows us to obtain the following improved decremental algorithms for maintaining shortest paths. -} (1+\epsilon)-approximate single-source shortest paths with amortized update time of 2^{\tilde{O}(\sqrt{\log n})} , when 0< \epsilon<1 is a constant and |E|= n2^{\tilde{\Omega(\sqrt{\log n})}} . This improves  over the best known  update time of 2^{\tilde{O}(\log^{3/4} n)} by [ Henzinger, Krinninger and Nanongkai, FOCS'14].
Revised:  Our decremental hopset algorithm allows us to obtain the following improved decremental algorithms for maintaining shortest paths. -} (1+\epsilon)-approximate single-source shortest paths with amortized update time of 2^{\tilde{O}(\sqrt{\log n})} , when 0< \epsilon<1 is a constant and |E|= n2^{\tilde{\Omega(\sqrt{\log n})}} . This improves over the best known update time of 2^{\tilde{O}(\log^{3/4} n)} by [ HKN, FOCS'14].
Edit intent: meaning-changed
Original:  Furthermore, for any integer k \geq 1 we give an algorithmfor maintaining\tilde{O}, and |E|= n^{1+\Omega(1)}.
Revised:  Furthermore, for any integer k \geq 1 +\epsilon)-approximate shortest paths from a set of s sources in\tilde{O}, and |E|= n^{1+\Omega(1)}.
Edit intent: meaning-changed
Original:  Furthermore, for any integer k \geq 1 we give an algorithmfor maintaining\tilde{O}, and |E|= n^{1+\Omega(1)}.
Revised:  Furthermore, for any integer k \geq 1 we give an algorithmfor maintaining\tilde{O}(s) amortized update time, assuming that s= n^{\Omega(1), and |E|= n^{1+\Omega(1)}.
Edit intent: meaning-changed
Original:  In this regime, we give the first decremental algorithm, whose running time matches, up to polylogarithmic factors, the best known static algorithm. -} (2k-1)(1+\epsilon)-approximate all-pairs-shortest-paths , in \tilde{O}( mn^{1/k update time and O(k) query time \footnote{Throughout this paper we use the notation%DIFDELCMD < \tilde{O}%%% (f(n)) to hide factors of O(\text{polylog (f(n))).
Revised:  In this regime, we give the first decremental algorithm, whose running time matches, up to polylogarithmic factors, the best known static algorithm. -} (2k-1)(1+\epsilon)-approximate all-pairs shortest paths (for any constant k \geq 2) , in \tilde{O}( mn^{1/k update time and O(k) query time \footnote{Throughout this paper we use the notation%DIFDELCMD < \tilde{O}%%% (f(n)) to hide factors of O(\text{polylog (f(n))).
Edit intent: meaning-changed
Original:  In this regime, we give the first decremental algorithm, whose running time matches, up to polylogarithmic factors, the best known static algorithm. -} (2k-1)(1+\epsilon)-approximate all-pairs-shortest-paths , in \tilde{O}( mn^{1/k update time and O(k) query time \footnote{Throughout this paper we use the notation%DIFDELCMD < \tilde{O}%%% (f(n)) to hide factors of O(\text{polylog (f(n))).
Revised:  In this regime, we give the first decremental algorithm, whose running time matches, up to polylogarithmic factors, the best known static algorithm. -} (2k-1)(1+\epsilon)-approximate all-pairs-shortest-paths , in \tilde{O}( n^{1/k update time and O(k) query time \footnote{Throughout this paper we use the notation%DIFDELCMD < \tilde{O}%%% (f(n)) to hide factors of O(\text{polylog (f(n))).
Edit intent: clarity
Original:  In this regime, we give the first decremental algorithm, whose running time matches, up to polylogarithmic factors, the best known static algorithm. -} (2k-1)(1+\epsilon)-approximate all-pairs-shortest-paths , in \tilde{O}( mn^{1/k update time and O(k) query time \footnote{Throughout this paper we use the notation%DIFDELCMD < \tilde{O}%%% (f(n)) to hide factors of O(\text{polylog (f(n))).
Revised:  In this regime, we give the first decremental algorithm, whose running time matches, up to polylogarithmic factors, the best known static algorithm. -} (2k-1)(1+\epsilon)-approximate all-pairs-shortest-paths , in \tilde{O}( mn^{1/k update time and O(k) query time \footnote{%DIFDELCMD < \tilde{O}%%% (f(n)) to hide factors of O(\text{polylog (f(n))).
Edit intent: clarity
Original:  In this regime, we give the first decremental algorithm, whose running time matches, up to polylogarithmic factors, the best known static algorithm. -} (2k-1)(1+\epsilon)-approximate all-pairs-shortest-paths , in \tilde{O}( mn^{1/k update time and O(k) query time \footnote{Throughout this paper we use the notation%DIFDELCMD < \tilde{O}%%% (f(n)) to hide factors of O(\text{polylog (f(n))).
Revised:  In this regime, we give the first decremental algorithm, whose running time matches, up to polylogarithmic factors, the best known static algorithm. -} (2k-1)(1+\epsilon)-approximate all-pairs-shortest-paths , in \tilde{O}( mn^{1/k update time and O(k) query time \footnote{Throughout this paper we use the notation%DIFDELCMD < \tilde{O}%%% (f(n))).
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Original: }}%DIFAUXCMD \addtocounter{footnote}{-1}%DIFAUXCMD . This improves over the result of\tilde{O})\cdot(1/\epsilon)^{O(\sqrt{\log n})} } [Chechik, FOCS'18] in a twofold way.
Revised: }}%DIFAUXCMD \addtocounter{footnote}{-1}%DIFAUXCMD . This improves over the best-known amortized update time of\tilde{O})\cdot(1/\epsilon)^{O(\sqrt{\log n})} } [Chechik, FOCS'18] in a twofold way.
Edit intent: meaning-changed
Original: }}%DIFAUXCMD \addtocounter{footnote}{-1}%DIFAUXCMD . This improves over the result of\tilde{O})\cdot(1/\epsilon)^{O(\sqrt{\log n})} } [Chechik, FOCS'18] in a twofold way.
Revised: }}%DIFAUXCMD \addtocounter{footnote}{-1}%DIFAUXCMD . This improves over the result of\tilde{O}(n^{1/k)\cdot(1/\epsilon)^{O(\sqrt{\log n})} } [Chechik, FOCS'18] in a twofold way.
Edit intent: meaning-changed
Original: }}%DIFAUXCMD \addtocounter{footnote}{-1}%DIFAUXCMD . This improves over the result of\tilde{O})\cdot(1/\epsilon)^{O(\sqrt{\log n})} } [Chechik, FOCS'18] in a twofold way. Namely, we improve the total update time bound by removing an n^{o(1) reduce the query time from O(\log \log (nW)) to  O(k) . Our results are based on a new decremental hopset construction that may be of independent interest .
Revised: }}%DIFAUXCMD \addtocounter{footnote}{-1}%DIFAUXCMD . This improves over the result of\tilde{O})\cdot(1/\epsilon)^{O(\sqrt{\log n})} } [Chechik, FOCS'18] . Moreover, we reduce the query time from O(\log \log (nW)) to O(k) . Our results are based on a new decremental hopset construction that may be of independent interest .
Edit intent: meaning-changed
Original:  Namely, we improve the total update time bound by removing an n^{o(1) reduce the query time from O(\log \log (nW)) to  O(k) . Our results are based on a new decremental hopset construction that may be of independent interest .
Revised:  Namely, we improve the total update time bound by removing an n^{o(1) reduce the query time from O(\log \log (nW)) to a constant O(k) . Our results are based on a new decremental hopset construction that may be of independent interest .
Edit intent: clarity
Original:  Namely, we improve the total update time bound by removing an n^{o(1) reduce the query time from O(\log \log (nW)) to  O(k) . Our results are based on a new decremental hopset construction that may be of independent interest .
Revised:  Namely, we improve the total update time bound by removing an n^{o(1) reduce the query time from O(\log \log (nW)) to O(k) , and hence eliminate the dependence on n and the aspect ratio W .
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Original:  We further propose MIDAS-F, to solve the problem by which anomalies are incorporated into the algorithm's internal states, creating a ' poisoning' effect which can allow future anomalies to slip through undetected.
Revised:  We further propose MIDAS-F, to solve the problem by which anomalies are incorporated into the algorithm's internal states, creating a ` poisoning' effect which can allow future anomalies to slip through undetected.
Edit intent: fluency
Original:  We further propose MIDAS-F, to solve the problem by which anomalies are incorporated into the algorithm's internal states, creating a ' poisoning' effect which can allow future anomalies to slip through undetected.
Revised:  We further propose MIDAS-F, to solve the problem by which anomalies are incorporated into the algorithm's internal states, creating a ' poisoning' effect that can allow future anomalies to slip through undetected.
Edit intent: fluency
Original:  Recent dense retrieval methods learn latent representations to tackle the lexical mismatch problem, while being more computationally expensive and sometimes insufficient for exact matching as they embed the entire text sequence into a single vector with limited capacity.
Revised:  Recent dense retrieval methods learn latent representations to tackle the lexical mismatch problem, while being more computationally expensive and insufficient for exact matching as they embed the entire text sequence into a single vector with limited capacity.
Edit intent: coherence
Original:  Recent dense retrieval methods learn latent representations to tackle the lexical mismatch problem, while being more computationally expensive and sometimes insufficient for exact matching as they embed the entire text sequence into a single vector with limited capacity.
Revised:  Recent dense retrieval methods learn latent representations to tackle the lexical mismatch problem, while being more computationally expensive and sometimes insufficient for exact matching as they embed the text sequence into a single vector with limited capacity.
Edit intent: clarity
Original:  We demonstrate on open-domain question answering (QA) that the generated contexts significantly enrich the semantics of the queries and thus GAR with sparse representations (BM25) achieves comparable or better performance than the current state-of-the-art dense method DPR karpukhin2020dense.
Revised:  We demonstrate on open-domain question answering that the generated contexts significantly enrich the semantics of the queries and thus GAR with sparse representations (BM25) achieves comparable or better performance than the current state-of-the-art dense method DPR karpukhin2020dense.
Edit intent: clarity
Original:  We demonstrate on open-domain question answering (QA) that the generated contexts significantly enrich the semantics of the queries and thus GAR with sparse representations (BM25) achieves comparable or better performance than the current state-of-the-art dense method DPR karpukhin2020dense.
Revised:  We demonstrate on open-domain question answering (QA) that the generated contexts significantly enrich the semantics of the queries and thus GAR with sparse representations (BM25) achieves comparable or better performance than the state-of-the-art dense method DPR karpukhin2020dense.
Edit intent: clarity
Original:  We demonstrate on open-domain question answering (QA) that the generated contexts significantly enrich the semantics of the queries and thus GAR with sparse representations (BM25) achieves comparable or better performance than the current state-of-the-art dense method DPR karpukhin2020dense.
Revised:  We demonstrate on open-domain question answering (QA) that the generated contexts significantly enrich the semantics of the queries and thus GAR with sparse representations (BM25) achieves comparable or better performance than the current state-of-the-art dense methods such as DPR karpukhin2020dense.
Edit intent: clarity
Original:  Moreover, GAR achieves the state-of-the-art performance of extractive QA on the Natural Questions and TriviaQA datasets  when equipped with an extractive reader  .
Revised:  Moreover, as sparse and dense representations are often complementary, GAR can be easily combined with DPR to achieve even better performance. Furthermore, GAR achieves the state-of-the-art performance of extractive QA on the Natural Questions and TriviaQA datasets when equipped with an extractive reader .
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Original:  Moreover, GAR achieves the state-of-the-art performance of extractive QA on the Natural Questions and TriviaQA datasets  when equipped with an extractive reader  .
Revised:  Moreover, GAR achieves the state-of-the-art performance of extractive QA on the Natural Questions and TriviaQA datasets when equipped with an extractive reader , and consistently outperforms other retrieval methods when the same generative reader is used .
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Original:  In this paper, we present MUTANT , a training paradigm that exposes the model to perceptually similar, yet semantically distinct mutations of the input, to improve OOD generalization, such as the VQA-CP challenge.
Revised:  In this paper, we present MUTANT , a training paradigm that exposes the model to perceptually similar, yet semantically distinct mutations of the input, to improve OOD generalization, such as the VQA-CP challenge.
Edit intent: fluency
Original: We present fastHan, an open-source toolkit for four basic tasks in Chinese natural language processing: Chinese word segmentation  , Part-of-Speech  tagging, named entity recognition  , and dependency parsing.
Revised: We present fastHan, an open-source toolkit for four basic tasks in Chinese natural language processing: Chinese word segmentation (CWS) , Part-of-Speech tagging, named entity recognition , and dependency parsing.
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Original: We present fastHan, an open-source toolkit for four basic tasks in Chinese natural language processing: Chinese word segmentation  , Part-of-Speech  tagging, named entity recognition  , and dependency parsing.
Revised: We present fastHan, an open-source toolkit for four basic tasks in Chinese natural language processing: Chinese word segmentation , Part-of-Speech (POS) tagging, named entity recognition , and dependency parsing.
Edit intent: meaning-changed
Original: We present fastHan, an open-source toolkit for four basic tasks in Chinese natural language processing: Chinese word segmentation  , Part-of-Speech  tagging, named entity recognition  , and dependency parsing.
Revised: We present fastHan, an open-source toolkit for four basic tasks in Chinese natural language processing: Chinese word segmentation , Part-of-Speech tagging, named entity recognition (NER) , and dependency parsing.
Edit intent: meaning-changed
Original:  The joint-model is trained and evaluated in 13 corpora of four tasks, yielding near state-of-the-art (SOTA) performance in the dependency parsing task and SOTA performance in the other three tasks .
Revised:  The joint-model is trained and evaluated on 13 corpora of four tasks, yielding near state-of-the-art (SOTA) performance in the dependency parsing task and SOTA performance in the other three tasks .
Edit intent: fluency
Original:  The joint-model is trained and evaluated in 13 corpora of four tasks, yielding near state-of-the-art (SOTA) performance in the dependency parsing task and SOTA performance in the other three tasks .
Revised:  The joint-model is trained and evaluated in 13 corpora of four tasks, yielding near state-of-the-art (SOTA) performance in dependency parsing and NER, achieving SOTA performance in the other three tasks .
Edit intent: meaning-changed
Original:  The joint-model is trained and evaluated in 13 corpora of four tasks, yielding near state-of-the-art (SOTA) performance in the dependency parsing task and SOTA performance in the other three tasks .
Revised:  The joint-model is trained and evaluated in 13 corpora of four tasks, yielding near state-of-the-art (SOTA) performance in the dependency parsing task and SOTA performance in CWS and POS. Besides, fastHan's transferability is also strong, performing much better than popular segmentation tools on a non-training corpus. To better meet the need of practical application, we allow users to use their own labeled data to further fine-tune fastHan .
Edit intent: meaning-changed
Original:  To achieve this, we proposed two  new features, which can encode the rehabilitation information from both paralysed/non-paralysed sides while suppressing the high-level noises such as irrelevant daily activities.
Revised:  To achieve this, we proposed two types of new features, which can encode the rehabilitation information from both paralysed/non-paralysed sides while suppressing the high-level noises such as irrelevant daily activities.
Edit intent: clarity
Original:  We further developed the longitudinal mixed-effects model with Gaussian process prior (LMGP), which can model the random effects caused by different subjects and time slots (during the 8 weeks).
Revised:  Based on the proposed features, we further developed the longitudinal mixed-effects model with Gaussian process prior (LMGP), which can model the random effects caused by different subjects and time slots (during the 8 weeks).
Edit intent: meaning-changed
Original:  Although many approximate/exact algorithms have been developed to solve this  problem on numerous properties, a solution obtained by these algorithms is often unsuitable for real-world applications due to the difficulty of building  mathematical models on real-world problems.
Revised:  Although many approximate/exact algorithms have been developed to solve this kind of problem on numerous properties, a solution obtained by these algorithms is often unsuitable for real-world applications due to the difficulty of building mathematical models on real-world problems.
Edit intent: clarity
Original:  Although many approximate/exact algorithms have been developed to solve this  problem on numerous properties, a solution obtained by these algorithms is often unsuitable for real-world applications due to the difficulty of building  mathematical models on real-world problems.
Revised:  Although many approximate/exact algorithms have been developed to solve this problem on numerous properties, a solution obtained by these algorithms is often unsuitable for real-world applications due to the difficulty of building accurate mathematical models on real-world problems.
Edit intent: clarity
Original:  To this end, given \to  an integer k, we devise algorithms that approximately enumerate all minimal subsets of U with cardinality at most k satisfying \Pi for various monotone properties \Pi, where "approximate enumeration" means that algorithms  may output some minimal subsets satisfying \Pi whose cardinality exceeds k and is at most ck for some constant c \ge 1.
Revised:  To this end, given a weight function w: U\to an integer k, we devise algorithms that approximately enumerate all minimal subsets of U with cardinality at most k satisfying \Pi for various monotone properties \Pi, where "approximate enumeration" means that algorithms may output some minimal subsets satisfying \Pi whose cardinality exceeds k and is at most ck for some constant c \ge 1.
Edit intent: meaning-changed
Original:  To this end, given \to  an integer k, we devise algorithms that approximately enumerate all minimal subsets of U with cardinality at most k satisfying \Pi for various monotone properties \Pi, where "approximate enumeration" means that algorithms  may output some minimal subsets satisfying \Pi whose cardinality exceeds k and is at most ck for some constant c \ge 1.
Revised:  To this end, given \to \mathbb N and an integer k, we devise algorithms that approximately enumerate all minimal subsets of U with cardinality at most k satisfying \Pi for various monotone properties \Pi, where "approximate enumeration" means that algorithms may output some minimal subsets satisfying \Pi whose cardinality exceeds k and is at most ck for some constant c \ge 1.
Edit intent: meaning-changed
Original:  To this end, given \to  an integer k, we devise algorithms that approximately enumerate all minimal subsets of U with cardinality at most k satisfying \Pi for various monotone properties \Pi, where "approximate enumeration" means that algorithms  may output some minimal subsets satisfying \Pi whose cardinality exceeds k and is at most ck for some constant c \ge 1.
Revised:  To this end, given \to an integer k, we devise algorithms that approximately enumerate all minimal subsets of U with weight at most k satisfying \Pi for various monotone properties \Pi, where "approximate enumeration" means that algorithms may output some minimal subsets satisfying \Pi whose cardinality exceeds k and is at most ck for some constant c \ge 1.
Edit intent: fluency
Original:  To this end, given \to  an integer k, we devise algorithms that approximately enumerate all minimal subsets of U with cardinality at most k satisfying \Pi for various monotone properties \Pi, where "approximate enumeration" means that algorithms  may output some minimal subsets satisfying \Pi whose cardinality exceeds k and is at most ck for some constant c \ge 1.
Revised:  To this end, given \to an integer k, we devise algorithms that approximately enumerate all minimal subsets of U with cardinality at most k satisfying \Pi for various monotone properties \Pi, where "approximate enumeration" means that algorithms output all minimal subsets satisfying \Pi whose weight at most k and may output some minimal subsets satisfying \Pi whose cardinality exceeds k and is at most ck for some constant c \ge 1.
Edit intent: meaning-changed
Original:  To this end, given \to  an integer k, we devise algorithms that approximately enumerate all minimal subsets of U with cardinality at most k satisfying \Pi for various monotone properties \Pi, where "approximate enumeration" means that algorithms  may output some minimal subsets satisfying \Pi whose cardinality exceeds k and is at most ck for some constant c \ge 1.
Revised:  To this end, given \to an integer k, we devise algorithms that approximately enumerate all minimal subsets of U with cardinality at most k satisfying \Pi for various monotone properties \Pi, where "approximate enumeration" means that algorithms may output some minimal subsets satisfying \Pi whose weight exceeds k but is at most ck for some constant c \ge 1.
Edit intent: clarity
Original:  These algorithms allow us to efficiently enumerate minimal vertex covers, minimal dominating sets in bounded degree graphs, minimal feedback vertex sets, minimal hitting sets in bounded rank hypergraphs, etc.,  with constant approximation factors.
Revised:  These algorithms allow us to efficiently enumerate minimal vertex covers, minimal dominating sets in bounded degree graphs, minimal feedback vertex sets, minimal hitting sets in bounded rank hypergraphs, etc., of weight at most k with constant approximation factors.
Edit intent: meaning-changed
Original: Statistical field theory captures collective non-equilibrium dynamics of neuronal networks , but it does not address the inverse problem of searching the connectivity to implement a desired dynamics. We here show for an analytically solvable network model that the effective action in statistical field theory is identical to the rate function in large deviation theory;
Revised: We here unify the field theoretical approach to neuronal networks with large deviation theory. For a prototypical random recurrent network model with continuous-valued units, we show that the effective action in statistical field theory is identical to the rate function in large deviation theory;
Edit intent: meaning-changed
Original:  We here show for an analytically solvable network model that the effective action in statistical field theory is identical to the rate function in large deviation theory; using field theoretical methods we derive this rate function . It takes the form of a Kullback-Leibler divergence and enables data-driven inference of model parameters and Bayesian prediction of time series  .
Revised:  We here show for an analytically solvable network model that the effective action in statistical field theory is identical to the rate function and derive the latter using field theory. This rate function takes the form of a Kullback-Leibler divergence and enables data-driven inference of model parameters and Bayesian prediction of time series .
Edit intent: clarity