wanyu/IteraTeR_full_sent huggingface dataset:
Original:  This improves upon the previously best O(n^3 \log^2(n)) oracle complexity for strongly polynomial algorithms given in [Lee, Sidford and Wong, FOCS 2015] and [Dadush, V \'e%DIFDELCMD < }%%% gh and Zambelli, SODA 2018], and an exponential time algorithm with oracle complexity O(n^3 \log(n)) given in the former work , answering two open problems posted therein .
Revised:  This improves upon the previously best O(n^3 \log^2(n)) oracle complexity for strongly polynomial algorithms given in [Lee, Sidford and Wong, FOCS 2015] and [Dadush, \'e%DIFDELCMD < }%%% gh and Zambelli, SODA 2018], and an exponential time algorithm with oracle complexity O(n^3 \log(n)) given in the former work , answering two open problems posted therein .
Edit intent: clarity
Original:  This improves upon the previously best O(n^3 \log^2(n)) oracle complexity for strongly polynomial algorithms given in [Lee, Sidford and Wong, FOCS 2015] and [Dadush, V \'e%DIFDELCMD < }%%% gh and Zambelli, SODA 2018], and an exponential time algorithm with oracle complexity O(n^3 \log(n)) given in the former work , answering two open problems posted therein .
Revised:  This improves upon the previously best O(n^3 \log^2(n)) oracle complexity for strongly polynomial algorithms given in [Lee, Sidford and Wong, FOCS 2015] and [Dadush, V %DIFDELCMD < }%%% gh and Zambelli, SODA 2018], and an exponential time algorithm with oracle complexity O(n^3 \log(n)) given in the former work , answering two open problems posted therein .
Edit intent: clarity
Original:  This improves upon the previously best O(n^3 \log^2(n)) oracle complexity for strongly polynomial algorithms given in [Lee, Sidford and Wong, FOCS 2015] and [Dadush, V \'e%DIFDELCMD < }%%% gh and Zambelli, SODA 2018], and an exponential time algorithm with oracle complexity O(n^3 \log(n)) given in the former work , answering two open problems posted therein .
Revised:  This improves upon the previously best O(n^3 \log^2(n)) oracle complexity for strongly polynomial algorithms given in [Lee, Sidford and Wong, FOCS 2015] and [Dadush, V \'e%DIFDELCMD < }%%% V\'egh and Zambelli, SODA 2018], and an exponential time algorithm with oracle complexity O(n^3 \log(n)) given in the former work , answering two open problems posted therein .
Edit intent: clarity
Original:  This improves upon the previously best O(n^3 \log^2(n)) oracle complexity for strongly polynomial algorithms given in [Lee, Sidford and Wong, FOCS 2015] and [Dadush, V \'e%DIFDELCMD < }%%% gh and Zambelli, SODA 2018], and an exponential time algorithm with oracle complexity O(n^3 \log(n)) given in the former work , answering two open problems posted therein .
Revised:  This improves upon the previously best O(n^3 \log^2(n)) oracle complexity for strongly polynomial algorithms given in [Lee, Sidford and Wong, FOCS 2015] and [Dadush, V \'e%DIFDELCMD < }%%% gh and Zambelli, SODA 2018], and an exponential time algorithm with oracle complexity O(n^3 \log(n)) given in the former work .
Edit intent: meaning-changed
Original:  We show how an approximately shortest vector of certain lattice can be used to reduce the dimension of the problem, and how the oracle complexity of such a procedure is advantageous compared with the method that uses the Frank-Tardos framework .
Revised:  We show how an approximately shortest vector of certain lattice can be used to reduce the dimension of the problem, and how the oracle complexity of such a procedure is advantageous compared with the Gr\"otschel-Lov\'asz-Schrijver approach that uses simultaneous diophantine approximation .
Edit intent: meaning-changed
Original: In this paper, we investigate the effect ] of the prolonged service time at the non-dedicated servers in a pooling systemon the system performance. We consider the two-server loss model with exponential interarrival and service times. We show that if the ratio of the mean service time at the dedicated server and the mean prolonged service time at the non-dedicated server exceeds a certain threshold, pooling would become unfavourable. In particular, the threshold is explicitly provided. Moreover, when the degree of the prolonged service time is pre-specified, we show that the pooling system with prolonged service time at non-dedicated servers is not preferred when the work load in the systemis greater than a certain threshold .
Revised: In this paper, we investigate the effect ] of the prolonged service time at the non-dedicated servers in an asymmetric system. We study the independent system, the full flexibility system and the partial flexibility system when the occupation rate of the system, the degree of the prolonged service time is pre-specified, we show that the pooling system with prolonged service time at non-dedicated servers is not preferred when the work load in the systemis greater than a certain threshold .
Edit intent: meaning-changed
Original:  Moreover, when the degree of the prolonged service time is pre-specified, we show that the pooling system with prolonged service time at non-dedicated servers is not preferred when the work load in the systemis greater than a certain threshold .
Revised:  Moreover, when the degree of the prolonged service time and the degree of the asymmetry are allowed to change. We show that under certain circumstances, the partial flexibility scheme outperforms the full flexibility system and the independent system in such a model. Our results also provide instructions on how to introduce flexibility when the service time at non-dedicated servers is not preferred when the work load in the systemis greater than a certain threshold .
Edit intent: meaning-changed
Original:  Moreover, when the degree of the prolonged service time is pre-specified, we show that the pooling system with prolonged service time at non-dedicated servers is not preferred when the work load in the systemis greater than a certain threshold .
Revised:  Moreover, when the degree of the prolonged service time is pre-specified, we show that the pooling system with prolonged service time at non-dedicated servers is prolonged in an asymmetric system .
Edit intent: meaning-changed
Original:  We  complement these positive results by showing that, for any constant p \geq 4, both problems are W[1]-hard on C_\ell\dots -free graphs, where 4 \leq \ell \leq p, and Token Sliding remains W[1]-hard even on bipartite graphs.
Revised:  We believe both of these results to be of independent interest. We complement these positive results by showing that, for any constant p \geq 4, both problems are W[1]-hard on C_\ell\dots -free graphs, where 4 \leq \ell \leq p, and Token Sliding remains W[1]-hard even on bipartite graphs.
Edit intent: meaning-changed
Original:  We  complement these positive results by showing that, for any constant p \geq 4, both problems are W[1]-hard on C_\ell\dots -free graphs, where 4 \leq \ell \leq p, and Token Sliding remains W[1]-hard even on bipartite graphs.
Revised:  We complement these positive results by showing that, for any constant p \geq 4, both problems are W[1]-hard on \{C_4,\dots -free graphs, where 4 \leq \ell \leq p, and Token Sliding remains W[1]-hard even on bipartite graphs.
Edit intent: meaning-changed
Original:  We  complement these positive results by showing that, for any constant p \geq 4, both problems are W[1]-hard on C_\ell\dots -free graphs, where 4 \leq \ell \leq p, and Token Sliding remains W[1]-hard even on bipartite graphs.
Revised:  We complement these positive results by showing that, for any constant p \geq 4, both problems are W[1]-hard on C_\ell\dots, C_p\ -free graphs, where 4 \leq \ell \leq p, and Token Sliding remains W[1]-hard even on bipartite graphs.
Edit intent: meaning-changed
Original:  We  complement these positive results by showing that, for any constant p \geq 4, both problems are W[1]-hard on C_\ell\dots -free graphs, where 4 \leq \ell \leq p, and Token Sliding remains W[1]-hard even on bipartite graphs.
Revised:  We complement these positive results by showing that, for any constant p \geq 4, both problems are W[1]-hard on C_\ell\dots -free graphs and Token Sliding remains W[1]-hard even on bipartite graphs.
Edit intent: coherence
Original:  En route, we improve the sample complexity bound, recently obtained by Guo et al. ( 2019 ), for the Pandora's Box problem, which is a classical model for sequential consumer search.
Revised:  En route, we improve the sample complexity bound, recently obtained by Guo et al. ( 2020 ), for the Pandora's Box problem, which is a classical model for sequential consumer search.
Edit intent: meaning-changed
Original:  The proposed methodology is initially discussed for a model equation, presenting a thorough convergence study which confirms the spectral accuracy of the stochastic collocation method and the second-order of accuracy of the IMEX finite volume scheme chosen to solve the mathematical model.
Revised:  The proposed methodology is initially validated on a model equation, presenting a thorough convergence study which confirms the spectral accuracy of the stochastic collocation method and the second-order of accuracy of the IMEX finite volume scheme chosen to solve the mathematical model.
Edit intent: clarity
Original:  The proposed methodology is initially discussed for a model equation, presenting a thorough convergence study which confirms the spectral accuracy of the stochastic collocation method and the second-order of accuracy of the IMEX finite volume scheme chosen to solve the mathematical model.
Revised:  The proposed methodology is initially discussed for a model equation, presenting a thorough convergence study which confirms the spectral accuracy of the stochastic collocation method and the second-order accuracy of the IMEX finite volume scheme chosen to solve the mathematical model.
Edit intent: clarity
Original:  Simulations performed considering both the simple elastic and the more realistic viscoelastic constitutive law show that including viscoelasticity in the FSI model consistently improves the reliability of pressure waveforms prediction.
Revised:  Simulations performed considering both the simple elastic and the more realistic viscoelastic constitutive law show that the great uncertainty of the viscosity parameter plays a major role in the prediction of pressure waveforms prediction.
Edit intent: clarity
Original:  Simulations performed considering both the simple elastic and the more realistic viscoelastic constitutive law show that including viscoelasticity in the FSI model consistently improves the reliability of pressure waveforms prediction. Results of the patient-specific tests suggest that the proposed methodology could be a valuable tool for improving cardiovascular diagnostics and the treatment of diseases .
Revised:  Simulations performed considering both the simple elastic and the more realistic viscoelastic constitutive law show that including viscoelasticity in the FSI model consistently improves the reliability of pressure waveforms , enlarging the confidence interval of this variable. In-vivo recorded patient-specific tests suggest that the proposed methodology could be a valuable tool for improving cardiovascular diagnostics and the treatment of diseases .
Edit intent: meaning-changed
Original: Self assembly is a ubiquitous process in synthetic and biological systems, broadly defined as the spontaneous URLanization of multiple subunits (e.g. macromolecules, particles) into ordered multi-unit structures.
Revised: Self-assembly is a ubiquitous process in synthetic and biological systems, broadly defined as the spontaneous URLanization of multiple subunits (e.g. macromolecules, particles) into ordered multi-unit structures.
Edit intent: fluency
Original: Self assembly is a ubiquitous process in synthetic and biological systems, broadly defined as the spontaneous URLanization of multiple subunits (e.g. macromolecules, particles) into ordered multi-unit structures.
Revised: Self assembly is a ubiquitous process in synthetic and biological systems, broadly defined as the URLanization of multiple subunits (e.g. macromolecules, particles) into ordered multi-unit structures.
Edit intent: clarity
Original:  Then,  examples of self-limiting assembly systems will be introduced and classified within this framework based on two broad categories: {\it self-closing assemblies} and {\it open-boundary assemblies}.
Revised:  Then, we introduce examples of self-limiting assembly systems will be introduced and classified within this framework based on two broad categories: {\it self-closing assemblies} and {\it open-boundary assemblies}.
Edit intent: coherence
Original:  These will include well-known cases  -- micellization of amphiphiles and shell/tubule formation of tapered subunits -- as well as less widely known classes of assemblies, such as short-range attractive/long-range repulsive systems and geometrically-frustrated assemblies.
Revised:  These include well-known cases -- micellization of amphiphiles and shell/tubule formation of tapered subunits -- as well as less widely known classes of assemblies, such as short-range attractive/long-range repulsive systems and geometrically-frustrated assemblies.
Edit intent: clarity
Original:  These will include well-known cases  -- micellization of amphiphiles and shell/tubule formation of tapered subunits -- as well as less widely known classes of assemblies, such as short-range attractive/long-range repulsive systems and geometrically-frustrated assemblies.
Revised:  These will include well-known cases in biology and synthetic soft matter -- micellization of amphiphiles and shell/tubule formation of tapered subunits -- as well as less widely known classes of assemblies, such as short-range attractive/long-range repulsive systems and geometrically-frustrated assemblies.
Edit intent: meaning-changed
Original:  In this paper, we present a moment matching method based on the extended Krylov subspace combined with the superposition property that can handle large input models and generate more accurate and efficient reduced-order models for circuit simulation methods.
Revised:  In this paper, we present a moment-matching method which is based on the extended Krylov subspace combined with the superposition property that can handle large input models and generate more accurate and efficient reduced-order models for circuit simulation methods.
Edit intent: coherence
Original:  In this paper, we present a moment matching method based on the extended Krylov subspace combined with the superposition property that can handle large input models and generate more accurate and efficient reduced-order models for circuit simulation methods.
Revised:  In this paper, we present a moment matching method based on the extended Krylov subspace and exploits the superposition property that can handle large input models and generate more accurate and efficient reduced-order models for circuit simulation methods.
Edit intent: clarity
Original:  In this paper, we present a moment matching method based on the extended Krylov subspace combined with the superposition property that can handle large input models and generate more accurate and efficient reduced-order models for circuit simulation methods.
Revised:  In this paper, we present a moment matching method based on the extended Krylov subspace combined with the superposition property in order to deal with many terminals. The proposed method can handle large-scale regular and singular circuits and generate accurate and efficient reduced-order models for circuit simulation methods.
Edit intent: meaning-changed
Original:  In this paper, we present a moment matching method based on the extended Krylov subspace combined with the superposition property that can handle large input models and generate more accurate and efficient reduced-order models for circuit simulation methods. The accuracy and efficiency of the proposed method is verified using industrial power grid benchmarks .
Revised:  In this paper, we present a moment matching method based on the extended Krylov subspace combined with the superposition property that can handle large input models and generate more accurate and efficient reduced-order models for circuit simulation . Experimental results on industrial IBM power grids demonstrate that our method achieves an error reduction up to 83.69\% over a standard Krylov subspace method .
Edit intent: meaning-changed
Original:  We show that, when the context vectors are adversarially chosen in d-dimensional linear contextual bandits, the learner needs \Omega (d \log T/ \log ( d \log T) ) policy switches to achieve the minimax-optimal expected regret, almost matching the O(d \log T) upper bound by Abbasi-Yadkori et al. 2011(\log d, \log \log T) factors} ;
Revised:  We show that, when the context vectors are adversarially chosen in d-dimensional linear contextual bandits, the learner needs O (d \log T/ \log ( d \log T) ) policy switches to achieve the minimax-optimal expected regret, almost matching the O(d \log T) upper bound by Abbasi-Yadkori et al. 2011(\log d, \log \log T) factors} ;
Edit intent: clarity
Original:  We show that, when the context vectors are adversarially chosen in d-dimensional linear contextual bandits, the learner needs \Omega (d \log T/ \log ( d \log T) ) policy switches to achieve the minimax-optimal expected regret, almost matching the O(d \log T) upper bound by Abbasi-Yadkori et al. 2011(\log d, \log \log T) factors} ;
Revised:  We show that, when the context vectors are adversarially chosen in d-dimensional linear contextual bandits, the learner needs \Omega (d \log d \log T) ) policy switches to achieve the minimax-optimal expected regret, almost matching the O(d \log T) upper bound by Abbasi-Yadkori et al. 2011(\log d, \log \log T) factors} ;
Edit intent: clarity
Original:  We show that, when the context vectors are adversarially chosen in d-dimensional linear contextual bandits, the learner needs \Omega (d \log T/ \log ( d \log T) ) policy switches to achieve the minimax-optimal expected regret, almost matching the O(d \log T) upper bound by Abbasi-Yadkori et al. 2011(\log d, \log \log T) factors} ;
Revised:  We show that, when the context vectors are adversarially chosen in d-dimensional linear contextual bandits, the learner needs \Omega (d \log T/ \log ( d \log T) policy switches to achieve the minimax-optimal expected regret, almost matching the O(d \log T) upper bound by Abbasi-Yadkori et al. 2011(\log d, \log \log T) factors} ;
Edit intent: fluency
Original:  We show that, when the context vectors are adversarially chosen in d-dimensional linear contextual bandits, the learner needs \Omega (d \log T/ \log ( d \log T) ) policy switches to achieve the minimax-optimal expected regret, almost matching the O(d \log T) upper bound by Abbasi-Yadkori et al. 2011(\log d, \log \log T) factors} ;
Revised:  We show that, when the context vectors are adversarially chosen in d-dimensional linear contextual bandits, the learner needs \Omega (d \log T/ \log ( d \log T) ) policy switches to achieve the minimax-optimal 2011(\log d, \log \log T) factors} ;
Edit intent: meaning-changed
Original:  We show that, when the context vectors are adversarially chosen in d-dimensional linear contextual bandits, the learner needs \Omega (d \log T/ \log ( d \log T) ) policy switches to achieve the minimax-optimal expected regret, almost matching the O(d \log T) upper bound by Abbasi-Yadkori et al. 2011(\log d, \log \log T) factors} ;
Revised:  We show that, when the context vectors are adversarially chosen in d-dimensional linear contextual bandits, the learner needs \Omega (d \log T/ \log ( d \log T) ) policy switches to achieve the minimax-optimal expected regret, almost matching the O(d \log T) upper bound by Abbasi-Yadkori et al. regret, and this is optimal up to \mathrm{poly(\log d, \log \log T) factors} ;
Edit intent: meaning-changed
Original: Text data augmentation, i.e.  the creation of synthetic textual data from an original text, is challenging as augmentation transformations should take into account language complexity while being relevant to the target Natural Language Processing (NLP) task (e.g.  Machine Translation, Question Answering, Text Classification, etc.).
Revised: Text data augmentation, i.e. , the creation of synthetic textual data from an original text, is challenging as augmentation transformations should take into account language complexity while being relevant to the target Natural Language Processing (NLP) task (e.g. Machine Translation, Question Answering, Text Classification, etc.).
Edit intent: fluency
Original: Text data augmentation, i.e.  the creation of synthetic textual data from an original text, is challenging as augmentation transformations should take into account language complexity while being relevant to the target Natural Language Processing (NLP) task (e.g.  Machine Translation, Question Answering, Text Classification, etc.).
Revised: Text data augmentation, i.e. the creation of new textual data from an original text, is challenging as augmentation transformations should take into account language complexity while being relevant to the target Natural Language Processing (NLP) task (e.g. Machine Translation, Question Answering, Text Classification, etc.).
Edit intent: clarity
Original: Text data augmentation, i.e.  the creation of synthetic textual data from an original text, is challenging as augmentation transformations should take into account language complexity while being relevant to the target Natural Language Processing (NLP) task (e.g.  Machine Translation, Question Answering, Text Classification, etc.).
Revised: Text data augmentation, i.e. the creation of synthetic textual data from an existing text, is challenging as augmentation transformations should take into account language complexity while being relevant to the target Natural Language Processing (NLP) task (e.g. Machine Translation, Question Answering, Text Classification, etc.).
Edit intent: clarity
Original: Text data augmentation, i.e.  the creation of synthetic textual data from an original text, is challenging as augmentation transformations should take into account language complexity while being relevant to the target Natural Language Processing (NLP) task (e.g.  Machine Translation, Question Answering, Text Classification, etc.).
Revised: Text data augmentation, i.e. the creation of synthetic textual data from an original text, is challenging . Indeed, augmentation transformations should take into account language complexity while being relevant to the target Natural Language Processing (NLP) task (e.g. Machine Translation, Question Answering, Text Classification, etc.).
Edit intent: coherence
Original: Text data augmentation, i.e.  the creation of synthetic textual data from an original text, is challenging as augmentation transformations should take into account language complexity while being relevant to the target Natural Language Processing (NLP) task (e.g.  Machine Translation, Question Answering, Text Classification, etc.).
Revised: Text data augmentation, i.e. the creation of synthetic textual data from an original text, is challenging as augmentation transformations should take into account language complexity while being relevant to the target Natural Language Processing (NLP) task (e.g. , Machine Translation, Question Answering, Text Classification, etc.).
Edit intent: fluency