wanyu/IteraTeR_full_sent huggingface dataset:
Original:  For %DIFDELCMD < {\sc %%% Rich Lines and%DIFDELCMD < {\sc %%% Exact Fitting , we present a randomized Monte Carlo algorithm that achieves a lower running time than that of Guibas et al.'s algorithm [ %DIFDELCMD < {\it %%% Computational Geometry 1996], for a wide range of the parameter \lambda.
Revised:  For %DIFDELCMD < {\sc %%% Rich Lines %DIFDELCMD < {\sc %%% Exact Fitting , we present a randomized Monte Carlo algorithm that achieves a lower running time than that of Guibas et al.'s algorithm [ %DIFDELCMD < {\it %%% Computational Geometry 1996], for a wide range of the parameter \lambda.
Edit intent: coherence
Original:  For %DIFDELCMD < {\sc %%% Rich Lines and%DIFDELCMD < {\sc %%% Exact Fitting , we present a randomized Monte Carlo algorithm that achieves a lower running time than that of Guibas et al.'s algorithm [ %DIFDELCMD < {\it %%% Computational Geometry 1996], for a wide range of the parameter \lambda.
Revised:  For %DIFDELCMD < {\sc %%% Rich Lines and%DIFDELCMD < {\sc %%% Rich Lines and Exact Fitting , we present a randomized Monte Carlo algorithm that achieves a lower running time than that of Guibas et al.'s algorithm [ %DIFDELCMD < {\it %%% Computational Geometry 1996], for a wide range of the parameter \lambda.
Edit intent: coherence
Original:  For %DIFDELCMD < {\sc %%% Rich Lines and%DIFDELCMD < {\sc %%% Exact Fitting , we present a randomized Monte Carlo algorithm that achieves a lower running time than that of Guibas et al.'s algorithm [ %DIFDELCMD < {\it %%% Computational Geometry 1996], for a wide range of the parameter \lambda.
Revised:  For %DIFDELCMD < {\sc %%% Rich Lines and%DIFDELCMD < {\sc %%% Exact Fitting , we present a randomized Monte Carlo algorithm that achieves a lower running time than that of Guibas et al.'s algorithm [ %DIFDELCMD < {\it %%% Computational Geometry 1996], for a wide range of the parameter \lambda.
Edit intent: fluency
Original:  We derive lower-bound results showing that, for \lambda =\Omega(\sqrt{n \log n}), the upper bound on the running time of this randomized algorithm matches the lower bound that we derive on the time complexity of %DIFDELCMD < {\sc %%% Rich Lines in the algebraic computation trees model.
Revised:  We derive lower-bound results showing that, for \lambda =\Omega(\sqrt{n \log n}), the upper bound on the running time of this randomized algorithm matches the lower bound that we derive on the time complexity of %DIFDELCMD < {\sc %%% Rich Lines in the algebraic computation trees model.
Edit intent: fluency
Original:  We derive lower-bound results showing that the running time of the randomized algorithm we present comes close to the lower bound we derive on the time complexity of kernelization algorithms for %DIFDELCMD < {\sc %%% Line Cover in the algebraic computation trees model.
Revised:  We derive lower-bound results showing that the running time of the randomized algorithm we present comes close to the lower bound we derive on the time complexity of kernelization algorithms for %DIFDELCMD < {\sc %%% Line Cover in the algebraic computation trees model.
Edit intent: fluency
Original:  Therefore, to obtain more comprehensive entity representations, we propose the C oarse-to-F ineE ntityR epresentation model (CFER ) that adopts a coarse-to-fine strategy involving two phases.
Revised:  Therefore, to obtain more comprehensive entity representations, we propose the oarse-to-F ineE ntityR epresentation model (CFER ) that adopts a coarse-to-fine strategy involving two phases.
Edit intent: fluency
Original:  Therefore, to obtain more comprehensive entity representations, we propose the C oarse-to-F ineE ntityR epresentation model (CFER ) that adopts a coarse-to-fine strategy involving two phases.
Revised:  Therefore, to obtain more comprehensive entity representations, we propose the C F ineE ntityR epresentation model (CFER ) that adopts a coarse-to-fine strategy involving two phases.
Edit intent: fluency
Original:  Therefore, to obtain more comprehensive entity representations, we propose the C oarse-to-F ineE ntityR epresentation model (CFER ) that adopts a coarse-to-fine strategy involving two phases.
Revised:  Therefore, to obtain more comprehensive entity representations, we propose the C oarse-to- ineE ntityR epresentation model (CFER ) that adopts a coarse-to-fine strategy involving two phases.
Edit intent: fluency
Original:  Therefore, to obtain more comprehensive entity representations, we propose the C oarse-to-F ineE ntityR epresentation model (CFER ) that adopts a coarse-to-fine strategy involving two phases.
Revised:  Therefore, to obtain more comprehensive entity representations, we propose the C oarse-to-F E ntityR epresentation model (CFER ) that adopts a coarse-to-fine strategy involving two phases.
Edit intent: fluency
Original:  Therefore, to obtain more comprehensive entity representations, we propose the C oarse-to-F ineE ntityR epresentation model (CFER ) that adopts a coarse-to-fine strategy involving two phases.
Revised:  Therefore, to obtain more comprehensive entity representations, we propose the C oarse-to-F ine ntityR epresentation model (CFER ) that adopts a coarse-to-fine strategy involving two phases.
Edit intent: fluency
Original:  Therefore, to obtain more comprehensive entity representations, we propose the C oarse-to-F ineE ntityR epresentation model (CFER ) that adopts a coarse-to-fine strategy involving two phases.
Revised:  Therefore, to obtain more comprehensive entity representations, we propose the C oarse-to-F ineE R epresentation model (CFER ) that adopts a coarse-to-fine strategy involving two phases.
Edit intent: fluency
Original:  Therefore, to obtain more comprehensive entity representations, we propose the C oarse-to-F ineE ntityR epresentation model (CFER ) that adopts a coarse-to-fine strategy involving two phases.
Revised:  Therefore, to obtain more comprehensive entity representations, we propose the C oarse-to-F ineE ntity epresentation model (CFER ) that adopts a coarse-to-fine strategy involving two phases.
Edit intent: fluency
Original:  Therefore, to obtain more comprehensive entity representations, we propose the C oarse-to-F ineE ntityR epresentation model (CFER ) that adopts a coarse-to-fine strategy involving two phases.
Revised:  Therefore, to obtain more comprehensive entity representations, we propose the C oarse-to-F ineE ntityR CFER ) that adopts a coarse-to-fine strategy involving two phases.
Edit intent: clarity
Original:  Therefore, to obtain more comprehensive entity representations, we propose the C oarse-to-F ineE ntityR epresentation model (CFER ) that adopts a coarse-to-fine strategy involving two phases.
Revised:  Therefore, to obtain more comprehensive entity representations, we propose the C oarse-to-F ineE ntityR epresentation model (Coarse-to-Fine Entity Representation model (CFER ) that adopts a coarse-to-fine strategy involving two phases.
Edit intent: coherence
Original: This work considers the general task of estimating the sum of a bounded function over the edges of a graph that is unknown a priori, where graph vertices and edges are built on-the-fly by an algorithm and the resulting graph is too large to be kept in memory or disk. Prior work proposes Markov Chain Monte Carlo (MCMC) methods that simultaneously sample and generate the graph , eliminating the need for storage .
Revised: This work considers the general task of estimating the sum of a bounded function over the edges of a graph , given neighborhood query access and where access to the entire network is prohibitively expensive. To estimate this sum, prior work proposes Markov Chain Monte Carlo (MCMC) methods that simultaneously sample and generate the graph , eliminating the need for storage .
Edit intent: clarity
Original:  In this paper, we introduce Ripple, an MCMC-based estimator which achieves unprecedented scalability in this task by stratifying the MCMC Markov chain state space  with a new technique that we denote {\em ordered sequential stratified Markov regenerations}.
Revised:  In this paper, we introduce Ripple, an MCMC-based estimator that achieves unprecedented scalability in this task by stratifying the MCMC Markov chain state space with a new technique that we denote {\em ordered sequential stratified Markov regenerations}.
Edit intent: fluency
Original:  In this paper, we introduce Ripple, an MCMC-based estimator which achieves unprecedented scalability in this task by stratifying the MCMC Markov chain state space  with a new technique that we denote {\em ordered sequential stratified Markov regenerations}.
Revised:  In this paper, we introduce Ripple, an MCMC-based estimator which achieves unprecedented scalability by stratifying the MCMC Markov chain state space with a new technique that we denote {\em ordered sequential stratified Markov regenerations}.
Edit intent: clarity
Original:  In this paper, we introduce Ripple, an MCMC-based estimator which achieves unprecedented scalability in this task by stratifying the MCMC Markov chain state space  with a new technique that we denote {\em ordered sequential stratified Markov regenerations}.
Revised:  In this paper, we introduce Ripple, an MCMC-based estimator which achieves unprecedented scalability in this task by stratifying the Markov chain state space with a new technique that we denote {\em ordered sequential stratified Markov regenerations}.
Edit intent: fluency
Original:  In this paper, we introduce Ripple, an MCMC-based estimator which achieves unprecedented scalability in this task by stratifying the MCMC Markov chain state space  with a new technique that we denote {\em ordered sequential stratified Markov regenerations}.
Revised:  In this paper, we introduce Ripple, an MCMC-based estimator which achieves unprecedented scalability in this task by stratifying the MCMC Markov chain state space into ordered strata with a new technique that we denote {\em ordered sequential stratified Markov regenerations}.
Edit intent: clarity
Original:  In this paper, we introduce Ripple, an MCMC-based estimator which achieves unprecedented scalability in this task by stratifying the MCMC Markov chain state space  with a new technique that we denote {\em ordered sequential stratified Markov regenerations}.
Revised:  In this paper, we introduce Ripple, an MCMC-based estimator which achieves unprecedented scalability in this task by stratifying the MCMC Markov chain state space with a new technique that we denote {\em sequential stratified regenerations}.
Edit intent: clarity
Original:  In particular, applying Ripple to the task of estimating connected  induced subgraph counts on large graphs, we empirically demonstrate that Ripple is accurate and is able to estimate counts of up to 12-node subgraphs,  a task at a scale that has been considered unreachable, not only by prior MCMC-based methods , but also by other sampling approaches.
Revised:  We empirically evaluate our method by applying Ripple to the task of estimating connected induced subgraph counts on large graphs, we empirically demonstrate that Ripple is accurate and is able to estimate counts of up to 12-node subgraphs, a task at a scale that has been considered unreachable, not only by prior MCMC-based methods , but also by other sampling approaches.
Edit intent: meaning-changed
Original:  In particular, applying Ripple to the task of estimating connected  induced subgraph counts on large graphs, we empirically demonstrate that Ripple is accurate and is able to estimate counts of up to 12-node subgraphs,  a task at a scale that has been considered unreachable, not only by prior MCMC-based methods , but also by other sampling approaches.
Revised:  In particular, applying Ripple to the task of estimating connected , induced subgraph counts on large graphs, we empirically demonstrate that Ripple is accurate and is able to estimate counts of up to 12-node subgraphs, a task at a scale that has been considered unreachable, not only by prior MCMC-based methods , but also by other sampling approaches.
Edit intent: fluency
Original:  In particular, applying Ripple to the task of estimating connected  induced subgraph counts on large graphs, we empirically demonstrate that Ripple is accurate and is able to estimate counts of up to 12-node subgraphs,  a task at a scale that has been considered unreachable, not only by prior MCMC-based methods , but also by other sampling approaches.
Revised:  In particular, applying Ripple to the task of estimating connected induced subgraph counts given some input graph. Therein, we demonstrate that Ripple is accurate and is able to estimate counts of up to 12-node subgraphs, a task at a scale that has been considered unreachable, not only by prior MCMC-based methods , but also by other sampling approaches.
Edit intent: clarity
Original:  In particular, applying Ripple to the task of estimating connected  induced subgraph counts on large graphs, we empirically demonstrate that Ripple is accurate and is able to estimate counts of up to 12-node subgraphs,  a task at a scale that has been considered unreachable, not only by prior MCMC-based methods , but also by other sampling approaches.
Revised:  In particular, applying Ripple to the task of estimating connected induced subgraph counts on large graphs, we empirically demonstrate that Ripple is accurate and can estimate counts of up to 12-node subgraphs, a task at a scale that has been considered unreachable, not only by prior MCMC-based methods , but also by other sampling approaches.
Edit intent: clarity
Original:  In particular, applying Ripple to the task of estimating connected  induced subgraph counts on large graphs, we empirically demonstrate that Ripple is accurate and is able to estimate counts of up to 12-node subgraphs,  a task at a scale that has been considered unreachable, not only by prior MCMC-based methods , but also by other sampling approaches.
Revised:  In particular, applying Ripple to the task of estimating connected induced subgraph counts on large graphs, we empirically demonstrate that Ripple is accurate and is able to estimate counts of up to 12-node subgraphs, which is a task at a scale that has been considered unreachable, not only by prior MCMC-based methods , but also by other sampling approaches.
Edit intent: coherence
Original:  In particular, applying Ripple to the task of estimating connected  induced subgraph counts on large graphs, we empirically demonstrate that Ripple is accurate and is able to estimate counts of up to 12-node subgraphs,  a task at a scale that has been considered unreachable, not only by prior MCMC-based methods , but also by other sampling approaches.
Revised:  In particular, applying Ripple to the task of estimating connected induced subgraph counts on large graphs, we empirically demonstrate that Ripple is accurate and is able to estimate counts of up to 12-node subgraphs, a task at a scale that has been considered unreachable, not only by prior MCMC-based methods but also by other sampling approaches.
Edit intent: fluency
Original:  For instance, in this target application, we present results where the Markov chain state space is as large as 10^{43}, for which Ripple computes estimates in less than 4 hours  on average.
Revised:  For instance, in this target application, we present results in which the Markov chain state space is as large as 10^{43}, for which Ripple computes estimates in less than 4 hours on average.
Edit intent: fluency
Original:  For instance, in this target application, we present results where the Markov chain state space is as large as 10^{43}, for which Ripple computes estimates in less than 4 hours  on average.
Revised:  For instance, in this target application, we present results where the Markov chain state space is as large as 10^{43}, for which Ripple computes estimates in less than 4 hours , on average.
Edit intent: fluency
Original: Commonsense reasoning requires a model to make presumptions about world events via language understanding. Many methods couple pre-trained language models with knowledge graphs in order to combine the merits in language modeling and entity-based relational learning .
Revised: Commonsense reasoning requires a model to make presumptions about world events . Many prior methods couple language modeling with knowledge graphs in order to combine the merits in language modeling and entity-based relational learning .
Edit intent: clarity
Original:  In this paper , we propose to utilize external entity description to provide contextual information for graph entities. For the CommonsenseQA task, our model first extracts concepts from the question and choice, and then finds a related triple between these concepts. Next, it retrieves the descriptions of these concepts from Wiktionary and feed them as additional input to a pre-trained language model, together with the triple .
Revised:  In this paper , we propose to utilize external entity description to provide contextual information for knowledge understanding. We retrieve descriptions of related concepts from Wiktionary and feed them as additional input to a pre-trained language model, together with the triple .
Edit intent: clarity
Original:  The resulting model can attain much more effective commonsense reasoning capability, achieving state-of-the-art results in the CommonsenseQA dataset with an accuracy of 80.7\% (single model) and 83.3\% (ensemble model) on the official leaderboard .
Revised:  The resulting model achieves state-of-the-art results in the CommonsenseQA dataset with an accuracy of 80.7\% (single model) and 83.3\% (ensemble model) on the official leaderboard .
Edit intent: clarity
Original:  The resulting model can attain much more effective commonsense reasoning capability, achieving state-of-the-art results in the CommonsenseQA dataset with an accuracy of 80.7\% (single model) and 83.3\% (ensemble model) on the official leaderboard .
Revised:  The resulting model can attain much more effective commonsense reasoning capability, achieving state-of-the-art result in the CommonsenseQA dataset with an accuracy of 80.7\% (single model) and 83.3\% (ensemble model) on the official leaderboard .
Edit intent: fluency
Original:  The resulting model can attain much more effective commonsense reasoning capability, achieving state-of-the-art results in the CommonsenseQA dataset with an accuracy of 80.7\% (single model) and 83.3\% (ensemble model) on the official leaderboard .
Revised:  The resulting model can attain much more effective commonsense reasoning capability, achieving state-of-the-art results in the CommonsenseQA dataset and the best result among non-generative models in OpenBookQA .
Edit intent: meaning-changed
Original:  Recently, machine learning techniques based on Deep Neural Networks (DNNs) have begun to emerge as promising tools for function approximation in scientific computingproblems , with impressive results achieved on problems where the dimension of the underlying data or problem domain is large.
Revised:  Recently, machine learning with Deep Neural Networks (DNNs) have begun to emerge as promising tools for function approximation in scientific computingproblems , with impressive results achieved on problems where the dimension of the underlying data or problem domain is large.
Edit intent: clarity
Original:  Recently, machine learning techniques based on Deep Neural Networks (DNNs) have begun to emerge as promising tools for function approximation in scientific computingproblems , with impressive results achieved on problems where the dimension of the underlying data or problem domain is large.
Revised:  Recently, machine learning techniques based on Deep Neural Networks (DNNs) has emerged as a promising tool for scientific computing , with impressive results achieved on problems where the dimension of the underlying data or problem domain is large.
Edit intent: clarity
Original:  Recently, machine learning techniques based on Deep Neural Networks (DNNs) have begun to emerge as promising tools for function approximation in scientific computingproblems , with impressive results achieved on problems where the dimension of the underlying data or problem domain is large.
Revised:  Recently, machine learning techniques based on Deep Neural Networks (DNNs) have begun to emerge as promising tools for function approximation in scientific computingproblems , with impressive results achieved on problems where the dimension of the data or problem domain is large.
Edit intent: clarity
Original:  In this work , we broaden this perspectiveby focusing on approximation of functions that are Hilbert-valued, i.e. they take values in a separable, but typically infinite-dimensional, Hilbert space.
Revised:  This work broadens this perspective, focusing on approximating functions that are Hilbert-valued, i.e. they take values in a separable, but typically infinite-dimensional, Hilbert space.
Edit intent: clarity
Original:  In this work , we broaden this perspectiveby focusing on approximation of functions that are Hilbert-valued, i.e. they take values in a separable, but typically infinite-dimensional, Hilbert space.
Revised:  In this work , we broaden this perspectiveby focusing on approximation of functions that are Hilbert-valued, i.e. take values in a separable, but typically infinite-dimensional, Hilbert space.
Edit intent: coherence
Original:  Therefore, this review aims to integrate previous studies from these fields to understand how to develop virtual environments that can automatically create visual representations of users' emotional states. This manuscript addresses this challenge in three sections: First, theories related to emotion and affect are compared .
Revised:  Therefore, this review aims to integrate previous studies from these fields to understand how to develop virtual environments that can automatically create visual representations of users' affective states. The manuscript addresses this challenge in three sections: First, theories related to emotion and affect are compared .
Edit intent: clarity
Original: Deciphering the workings of a brain (and its alterations due to pathologies and disorders) requires real-time mapping of the activity of all neurons. Recently, this formidable task has been brought into reach by the implementation of light sheet fluorescence (LSF) microscopy on the transparent zebrafish larva, a simple yet representative model of the vertebrate brain. This application is however hindered by the use of visible light for fluorescence excitation , inducing unwanted visual stimulation that can only be partially overcome resorting to complex excitation geometries.
Revised: Deciphering the workings of a brain (and its alterations due to pathologies and disorders) requires real-time whole-brain functional imaging in zebrafish larvae. Conventional one photon LSFM can however induce undesirable visual stimulation due to the use of visible light for fluorescence excitation , inducing unwanted visual stimulation that can only be partially overcome resorting to complex excitation geometries.
Edit intent: clarity
Original:  This application is however hindered by the use of visible light for fluorescence excitation , inducing unwanted visual stimulation that can only be partially overcome resorting to complex excitation geometries. On the other hand, two-photon (2P) LSF microscopy, owing to the use of invisible infra-red illumination, enables volumetric imaging of neuronal activity avoiding undesirable visual stimulation .
Revised:  This application is however hindered by the use of visible excitation light. The use of two-photon (2P) LSF microscopy, owing to the use of invisible infra-red illumination, enables volumetric imaging of neuronal activity avoiding undesirable visual stimulation .
Edit intent: coherence