wanyu/IteraTeR_v2 huggingface dataset:
Original:  We demonstrate theoretically and empirically  the superiority of our techniques to batch algorithmsthat produce the full result and then sort it.
Revised:  We demonstrate theoretically and empirically the superiority of our techniques over batch algorithms, which produce the full result and then sort it.
Edit intent: fluency
Original:  Interestingly, our technique is not only faster for returning the first few results, but  even when all results are produced.
Revised:  Our technique is not only faster for returning the first few results, but even when all results are produced.
Edit intent: coherence
Original: Motivation: MiRNAs are a kind of small non-coding RNAs that are not translated into proteins, and aberrant expression of miRNAsis associated with human diseases.
Revised: MicroRNAs (miRNAs) play crucial roles in multifarious biological processes associated with human diseases.
Edit intent: clarity
Original:  Since miRNAs have different roles in diseases, the miRNA-disease associations are categorized into multiple types according to their roles.
Revised:  Identifying potential miRNA-disease associations are categorized into multiple types according to their roles.
Edit intent: clarity
Original:  Specifically, we introduce symmetric and strong self-concordance.
Revised:  Specifically, we introduce a symmetric parameter and the notion of strong self-concordance.
Edit intent: clarity
Original:  These properties imply that the corresponding Dikin walk mixes in O(n\nu) steps from a warm start in a convex body in R ^n using a strongly self-concordant barrier with symmetric self-concordance parameter \nu.
Revised:  These properties imply that the corresponding Dikin walk mixes in O(n\nu) steps from a warm start in a convex body in R ^{n using a strongly self-concordant barrier with symmetric self-concordance parameter \nu.
Edit intent: fluency
Original:  As a consequence, we obtain the first walk to mix in O(n^{2}) steps for an arbitrary polytope in R ^n . Strong self-concordance for other barriers leads to an interesting (and unexpected) connection --- for the universal and entropic barriers, it is implied by the KLS conjecture.
Revised:  As a consequence, we obtain the first walk to mix in O(n^{2}) steps for an arbitrary polytope in R ^{n . Strong self-concordance for other barriers leads to an interesting (and unexpected) connection --- for the universal and entropic barriers, it is implied by the KLS conjecture.
Edit intent: fluency
Original:  As a consequence, we obtain the first walk to mix in O(n^{2}) steps for an arbitrary polytope in R ^n . Strong self-concordance for other barriers leads to an interesting (and unexpected) connection --- for the universal and entropic barriers, it is implied by the KLS conjecture.
Revised:  As a consequence, we obtain the first walk to mix in O(n^{2}) steps for an arbitrary polytope in R ^n . Strong self-concordance for other barriers leads to an interesting (and unexpected) connection -- for the universal and entropic barriers, it is implied by the KLS conjecture.
Edit intent: fluency
Original: In certain applications involving the solution of a Bayesian inverse problem, it may not be possible or desirable to evaluate the full posterior, e.g. due to the high computational cost  .
Revised: In certain applications involving the solution of a Bayesian inverse problem, it may not be possible or desirable to evaluate the full posterior, e.g. due to the high computational cost of doing so .
Edit intent: clarity
Original:  By applying SCM to empirical peer report data with a ground truth -- synthetic random peer report data -- we demonstrate that although it can detect true positives (i.e. peer groups that exist), it has an unacceptably high rate of false positives.
Revised:  By applying SCM to empirical peer report data with a ground truth -- synthetic random peer report data -- we demonstrate that SCM has an unacceptably high rate of false positives.
Edit intent: coherence
Original:  By applying SCM to empirical peer report data with a ground truth -- synthetic random peer report data -- we demonstrate that although it can detect true positives (i.e. peer groups that exist), it has an unacceptably high rate of false positives.
Revised:  By applying SCM to empirical peer report data with a ground truth -- synthetic random peer report data -- we demonstrate that although it can detect true positives (i.e. peer groups that exist), it has an unacceptably high risk of false positives.
Edit intent: clarity
Original:  We conclude by recommending that social cognitive mapping should not be used to identify peer groups , and discuss cognitive social structures and bipartite backbone extraction as promising alternatives .
Revised:  Specifically, we show that SCM will identify peer groups , and discuss cognitive social structures and bipartite backbone extraction as promising alternatives .
Edit intent: clarity
Original:  We have analysed different cases of generalised Emden-Fowler equation and for compared our results with existing results in literature.
Revised:  We have analysed different cases of generalised Emden-Fowler equation and compared our results with existing results in literature.
Edit intent: fluency
Original:  We adopt a cartesian cubical type theory proposed by Angiuli, Brunerie, Coquand, Favonia, Harper  and Licata as the implicit foundation, confining our presentation to elementary results such as function extensionality, the derivation of weak connections and path induction, the groupoid structure of types  and the Eckmman-Hilton duality.
Revised:  We adopt a cartesian cubical type theory proposed by Angiuli, Brunerie, Coquand, Favonia, Harper , and Licata as the implicit foundation, confining our presentation to elementary results such as function extensionality, the derivation of weak connections and path induction, the groupoid structure of types and the Eckmman-Hilton duality.
Edit intent: fluency
Original:  We adopt a cartesian cubical type theory proposed by Angiuli, Brunerie, Coquand, Favonia, Harper  and Licata as the implicit foundation, confining our presentation to elementary results such as function extensionality, the derivation of weak connections and path induction, the groupoid structure of types  and the Eckmman-Hilton duality.
Revised:  We adopt a cartesian cubical type theory proposed by Angiuli, Brunerie, Coquand, Favonia, Harper and Licata as the implicit foundation, confining our presentation to elementary results such as function extensionality, the derivation of weak connections and path induction, the groupoid structure of types , and the Eckmman-Hilton duality.
Edit intent: fluency
Original:  The efficacy of this approach depends on the  pressure penalty stabilisation parameter underlying the LDG framework---provided the parameter is suitably chosen, numerical experiment shows that: (i) for steady-state Stokes problems, the convergence rate of the multigrid solver can match that of classical geometric multigrid methods for Poisson problems;
Revised:  The efficacy of this approach depends on the pressure penalty stabilisation parameter -- provided the parameter is suitably chosen, numerical experiment shows that: (i) for steady-state Stokes problems, the convergence rate of the multigrid solver can match that of classical geometric multigrid methods for Poisson problems;
Edit intent: clarity
Original:  An extensive range of two- and three-dimensional test problems demonstrates the solver performance as well as high-order accuracy---these include cases with periodic, Dirichlet, and stress boundary conditions;
Revised:  An extensive range of two- and three-dimensional test problems demonstrates the solver performance as well as high-order accuracy -- these include cases with periodic, Dirichlet, and stress boundary conditions;
Edit intent: fluency
Original: One of the main limitations today when using ray tracing to render sparse voxels octrees (SVOs) is that the octree data structure is inherently static.
Revised: A considerable limitation of employing sparse voxels octrees (SVOs) is that the octree data structure is inherently static.
Edit intent: clarity
Original:  In particular, we consider input uncertainties described by a Karhunen-Loeve expansion and compute statistics of high-dimensional quantities-of-interest, such as the cardiac activation potential.
Revised:  In particular, we consider input uncertainties described by a Karhunen-Loeeve expansion and compute statistics of high-dimensional quantities-of-interest, such as the cardiac activation potential.
Edit intent: fluency
Original:  Our methodology relies on a close integration of multilevel Monte Carlo methods, parallel iterative solvers, and a space-time finite element discretization.
Revised:  Our methodology relies on a close integration of multilevel Monte Carlo methods, parallel iterative solvers, and a space-time discretization.
Edit intent: clarity
Original:  We assess the performance of the proposed framework by showing in detail its application to the nonlinear equations of cardiac electrophysiology.
Revised:  We assess the performance of the proposed framework by showing in detail its application to the solution of nonlinear equations arising from cardiac electrophysiology.
Edit intent: clarity
Original:  Here we show that at large scale, this method is surprisingly effective, leading to positive transfer on all of  35 target languages we tested.
Revised:  Here we show that at large scale, this method is surprisingly effective, leading to positive transfer on all of the 35 target languages we tested.
Edit intent: fluency
Original:  Here we show that at large scale, this method is surprisingly effective  , leading to positive transfer on all of the 35 target languages and two tasks tested.
Revised:  Here we show that at large scale, instance-based transfer learning is surprisingly effective , leading to positive transfer on all of the 35 target languages and two tasks tested.
Edit intent: clarity
Original:  We analyze this improvement and argue that the most natural explanation, namely direct vocabulary overlap between languages, only partially explains the performance gains: in fact, we demonstrate target-language improvement can occur after adding data from an auxiliary language  with no vocabulary in common with the target.
Revised:  We analyze this improvement and argue that the most natural explanation, namely direct vocabulary overlap between languages, only partially explains the performance gains: in fact, we demonstrate target-language improvement can occur after adding data from an auxiliary language even with no vocabulary in common with the target.
Edit intent: clarity
Original:  SS3 was created to naturally deal with ERD problems  since: it supports incremental training and classification over text streams  and it can visually explain its rationale.
Revised:  SS3 was created to deal with ERD problems since: it supports incremental training and classification over text streams and it can visually explain its rationale.
Edit intent: coherence
Original:  SS3 was created to naturally deal with ERD problems  since: it supports incremental training and classification over text streams  and it can visually explain its rationale.
Revised:  SS3 was created to naturally deal with ERD problems since: it supports incremental training and classification over text streams , and it can visually explain its rationale.
Edit intent: fluency
Original:  This  could negatively affect the classification performance and also reduces the descriptiveness of visual explanations.
Revised:  This aspect could negatively affect the classification performance and also reduces the descriptiveness of visual explanations.
Edit intent: clarity
Original:  Unfortunately, when working with text streams, using n-grams is not trivial since the system must learn and recognize which n-grams are important `` on the fly '' . This paper introduces t-SS3, a variation of SS3 which expands the model to dynamically recognize useful patterns over text streams  .
Revised:  Unfortunately, when working with text streams, using n-grams is not trivial since the system must learn and recognize which n-grams are important " on the fly '' . This paper introduces t-SS3, a variation of SS3 which expands the model to dynamically recognize useful patterns over text streams .
Edit intent: fluency
Original:  Unfortunately, when working with text streams, using n-grams is not trivial since the system must learn and recognize which n-grams are important `` on the fly '' . This paper introduces t-SS3, a variation of SS3 which expands the model to dynamically recognize useful patterns over text streams  .
Revised:  Unfortunately, when working with text streams, using n-grams is not trivial since the system must learn and recognize which n-grams are important `` on the fly " . This paper introduces t-SS3, a variation of SS3 which expands the model to dynamically recognize useful patterns over text streams .
Edit intent: fluency
Original:  Unfortunately, when working with text streams, using n-grams is not trivial since the system must learn and recognize which n-grams are important `` on the fly '' . This paper introduces t-SS3, a variation of SS3 which expands the model to dynamically recognize useful patterns over text streams  .
Revised:  Unfortunately, when working with text streams, using n-grams is not trivial since the system must learn and recognize which n-grams are important `` on the fly '' . This paper introduces t-SS3, an extension of SS3 which expands the model to dynamically recognize useful patterns over text streams .
Edit intent: clarity
Original:  Unfortunately, when working with text streams, using n-grams is not trivial since the system must learn and recognize which n-grams are important `` on the fly '' . This paper introduces t-SS3, a variation of SS3 which expands the model to dynamically recognize useful patterns over text streams  .
Revised:  Unfortunately, when working with text streams, using n-grams is not trivial since the system must learn and recognize which n-grams are important `` on the fly '' . This paper introduces t-SS3, a variation of SS3 that allows it to recognize useful patterns over text streams .
Edit intent: clarity
Original:  We evaluated our model on the eRisk 2017 and 2018 tasks on early depression and anorexia detection.
Revised:  We evaluated our model in the eRisk 2017 and 2018 tasks on early depression and anorexia detection.
Edit intent: fluency
Original:  Experimental results show that t-SS3 is able to improve both , existing results and the richness of visual explanations.
Revised:  Experimental results suggest that t-SS3 is able to improve both , existing results and the richness of visual explanations.
Edit intent: clarity
Original:  Experimental results show that t-SS3 is able to improve both , existing results and the richness of visual explanations.
Revised:  Experimental results show that t-SS3 is able to improve both current results and the richness of visual explanations.
Edit intent: clarity
Original: Insurance cash flows become reserve dependent whenever contract conditions are modified during the contract term while maintaining actuarial equivalence .
Revised: Life insurance cash flows become reserve dependent whenever contract conditions are modified during the contract term while maintaining actuarial equivalence .
Edit intent: clarity
Original: Insurance cash flows become reserve dependent whenever contract conditions are modified during the contract term while maintaining actuarial equivalence .
Revised: Insurance cash flows become reserve dependent when contract conditions are modified during the contract term while maintaining actuarial equivalence .
Edit intent: fluency
Original: Insurance cash flows become reserve dependent whenever contract conditions are modified during the contract term while maintaining actuarial equivalence .
Revised: Insurance cash flows become reserve dependent whenever contract conditions are modified during the contract term on condition that actuarial equivalence is maintained .
Edit intent: clarity
Original:  This paper studies multiple contract modifications in a general non-Markovian framework .
Revised:  This paper studies multiple contract modifications .
Edit intent: clarity
Original:  We study a wide range of cases distinguishing for the number of candidates or for the kind of messages spread over the network.
Revised:  We study a wide range of cases distinguishing for the number of candidates or the kind of messages spread over the network.
Edit intent: fluency
Original:  We study a wide range of cases distinguishing for the number of candidates or for the kind of messages spread over the network.
Revised:  We study a wide range of cases distinguishing for the number of candidates or for the kind of information spread over the network.
Edit intent: clarity
Original:  The first layer of the neural network shows a rotationally symmetric pattern justified by the structure of the net itself that turns up to be an approximation of a Laplacian of Gaussian (LoG).
Revised:  The first layer of the neural network shows a rotational symmetric pattern justified by the structure of the net itself that turns up to be an approximation of a Laplacian of Gaussian (LoG).
Edit intent: fluency
Original:  The analogy with respect to the visual system structure is established, emerging directly from the architecture of the neural network.
Revised:  The analogy with the visual system structure is established, emerging directly from the architecture of the neural network.
Edit intent: clarity
Original:  The analogy with respect to the visual system structure is established, emerging directly from the architecture of the neural network.
Revised:  The analogy with respect to the visual system is established, emerging directly from the architecture of the neural network.
Edit intent: clarity
Original: The multilevel Monte Carlo (MLMC) method for continuous time Markov chains, first introduced by Anderson and Higham (  2012), is a highly efficient simulation technique that can be used to estimate various statistical quantities for stochastic reaction networks (SRNs), and in particular for stochastic biological systems.
Revised: The multilevel Monte Carlo (MLMC) method for continuous-time Markov chains, first introduced by Anderson and Higham ( 2012), is a highly efficient simulation technique that can be used to estimate various statistical quantities for stochastic reaction networks (SRNs), and in particular for stochastic biological systems.
Edit intent: fluency
Original: The multilevel Monte Carlo (MLMC) method for continuous time Markov chains, first introduced by Anderson and Higham (  2012), is a highly efficient simulation technique that can be used to estimate various statistical quantities for stochastic reaction networks (SRNs), and in particular for stochastic biological systems.
Revised: The multilevel Monte Carlo (MLMC) method for continuous time Markov chains, first introduced by Anderson and Higham ( 2012), is a highly efficient simulation technique that can be used to estimate various statistical quantities for stochastic reaction networks (SRNs), in particular for stochastic biological systems.
Edit intent: fluency
Original:  Unfortunately, the robustness and performance of the multilevel method can be deteriorated due to the phenomenon of high kurtosis,  observed at the deep levels of MLMC, which leads to inaccurate estimates for the sample variance.
Revised:  Unfortunately, the robustness and performance of the multilevel method can be affected by the high kurtosis, observed at the deep levels of MLMC, which leads to inaccurate estimates for the sample variance.
Edit intent: clarity
Original:  Unfortunately, the robustness and performance of the multilevel method can be deteriorated due to the phenomenon of high kurtosis,  observed at the deep levels of MLMC, which leads to inaccurate estimates for the sample variance.
Revised:  Unfortunately, the robustness and performance of the multilevel method can be deteriorated due to the phenomenon of high kurtosis, a phenomenon observed at the deep levels of MLMC, which leads to inaccurate estimates for the sample variance.
Edit intent: fluency
Original:  Unfortunately, the robustness and performance of the multilevel method can be deteriorated due to the phenomenon of high kurtosis,  observed at the deep levels of MLMC, which leads to inaccurate estimates for the sample variance.
Revised:  Unfortunately, the robustness and performance of the multilevel method can be deteriorated due to the phenomenon of high kurtosis, observed at the deep levels of MLMC, which leads to inaccurate estimates of the sample variance.
Edit intent: clarity
Original:  In this work, we address cases where the high-kurtosis phenomenon is due to catastrophic coupling \textit{ (characteristic of pure jump processes where coupled consecutive paths are identical in most of the simulations, while differences only appear in a very small proportion) , and introduce a pathwise dependent importance sampling technique that improves the robustness and efficiency of the multilevel method.
Revised:  In this work, we address cases where the high-kurtosis phenomenon is due to \textit{ (characteristic of pure jump processes where coupled consecutive paths are identical in most of the simulations, while differences only appear in a very small proportion) , and introduce a pathwise dependent importance sampling technique that improves the robustness and efficiency of the multilevel method.
Edit intent: clarity
Original:  In this work, we address cases where the high-kurtosis phenomenon is due to catastrophic coupling \textit{ (characteristic of pure jump processes where coupled consecutive paths are identical in most of the simulations, while differences only appear in a very small proportion) , and introduce a pathwise dependent importance sampling technique that improves the robustness and efficiency of the multilevel method.
Revised:  In this work, we address cases where the high-kurtosis phenomenon is due to catastrophic coupling \textit{ (characteristic of pure jump processes where coupled consecutive paths are identical in most of the simulations, while differences only appear in a tiny proportion) and introduce a pathwise dependent importance sampling technique that improves the robustness and efficiency of the multilevel method.
Edit intent: clarity
Original:  In this work, we address cases where the high-kurtosis phenomenon is due to catastrophic coupling \textit{ (characteristic of pure jump processes where coupled consecutive paths are identical in most of the simulations, while differences only appear in a very small proportion) , and introduce a pathwise dependent importance sampling technique that improves the robustness and efficiency of the multilevel method.
Revised:  In this work, we address cases where the high-kurtosis phenomenon is due to catastrophic coupling \textit{ (characteristic of pure jump processes where coupled consecutive paths are identical in most of the simulations, while differences only appear in a very small proportion) , and introduce a pathwise-dependent importance sampling (IS) technique that improves the robustness and efficiency of the multilevel method.
Edit intent: clarity
Original:  Our analysis , along with the conducted numerical experiments, demonstrates that our proposed method significantly reduces the kurtosis of the deep levels of MLMC, and also improves the strong convergence rate from \beta=1 for the standard case (without importance sampling ), to \beta=1+\delta, where 0<\delta<1 is a user-selected parameter in our importance sampling algorithm.
Revised:  Our theoretical results , along with the conducted numerical experiments, demonstrates that our proposed method significantly reduces the kurtosis of the deep levels of MLMC, and also improves the strong convergence rate from \beta=1 for the standard case (without importance sampling ), to \beta=1+\delta, where 0<\delta<1 is a user-selected parameter in our importance sampling algorithm.
Edit intent: clarity