wanyu/IteraTeR_v2 huggingface dataset:
Original:  As such  it is broadly applicable in situations where the underlying randomness can efficiently be simulated.
Revised:  As such , it is broadly applicable in situations where the underlying randomness can efficiently be simulated.
Edit intent: fluency
Original: In this paper we study edge-sum distinguishing labeling, a type of labeling recently introduced by Tuza \mbox{ tuza2017graph \textit{ ]  context of labeling games.
Revised: We study edge-sum distinguishing labeling, a type of labeling recently introduced by Tuza \mbox{ tuza2017graph \textit{ ] context of labeling games.
Edit intent: coherence
Original: In this paper we study edge-sum distinguishing labeling, a type of labeling recently introduced by Tuza \mbox{ tuza2017graph \textit{ ]  context of labeling games.
Revised: In this paper we study edge-sum distinguishing labeling, a type of labeling recently introduced by Tuza in \textit{ ] context of labeling games.
Edit intent: coherence
Original: In this paper we study edge-sum distinguishing labeling, a type of labeling recently introduced by Tuza \mbox{ tuza2017graph \textit{ ]  context of labeling games.
Revised: In this paper we study edge-sum distinguishing labeling, a type of labeling recently introduced by Tuza \mbox{ tuza2017graph \textit{ ] in context of labeling games.
Edit intent: fluency
Original:  Also, we show that ESD labeling is closely connected to the well-known notion of the Sidon sequence and harmonious labeling.
Revised:  Also, we observe that ESD labeling is closely connected to the well-known notion of the Sidon sequence and harmonious labeling.
Edit intent: coherence
Original:  Also, we observe that ESD labeling is closely connected to the well-known notion of the Sidon sequence and harmonious labeling.
Revised:  We also observe that ESD labeling is closely connected to the well-known notion of the Sidon sequence and harmonious labeling.
Edit intent: coherence
Original: In their GECCO'12 paper, Doerr and Doerr proved that the k-ary unbiased black-box complexity of OneMax on n bits is O(n/k) for 2\le k\le \log_2 n . We propose an alternative strategy for achieving this unbiased black-box complexity when 3\le k\le\log_2 n. While it is based on the same idea of block-wise optimization, it uses k-ary unbiased operators in a different way.
Revised: In their GECCO'12 paper, Doerr and Doerr proved that the k-ary unbiased black-box complexity of OneMax on n bits is O(n/k) for 2\le k\le O(\log n) . We propose an alternative strategy for achieving this unbiased black-box complexity when 3\le k\le\log_2 n. While it is based on the same idea of block-wise optimization, it uses k-ary unbiased operators in a different way.
Edit intent: clarity
Original:  Flexible network architectures, such as the software-defined networking (SDN) paradigm, will provide to WLANs with new capabilities to deal with users' demands, while achieving greater levels of efficiency and flexibility in those dynamic and complex scenarios.
Revised:  Flexible network architectures, such as the software-defined networking (SDN) paradigm, will provide WLANs with new capabilities to deal with users' demands, while achieving greater levels of efficiency and flexibility in those dynamic and complex scenarios.
Edit intent: fluency
Original:  Flexible network architectures, such as the software-defined networking (SDN) paradigm, will provide to WLANs with new capabilities to deal with users' demands, while achieving greater levels of efficiency and flexibility in those dynamic and complex scenarios.
Revised:  Flexible network architectures, such as the software-defined networking (SDN) paradigm, will provide to WLANs with new capabilities to deal with users' demands, while achieving greater levels of efficiency and flexibility in those complex scenarios.
Edit intent: clarity
Original:  Moreover , the use of machine learning (ML) techniques will improve network resource usage and management by identifying feasible configurations through learning.
Revised:  Moreover , the use of machine learning (ML) techniques may improve network resource usage and management by identifying feasible configurations through learning.
Edit intent: clarity
Original:  Moreover, we validate the suitability of such an approach by developing a use-case in which we show the benefits of using ML , and how those techniques can be combined with SDN .
Revised:  Finally, in order to demonstrate the potential of ML techniques in combination with SDN to improve the network operation, we evaluate different use cases for intelligent-based spatial reuse and dynamic channel bonding operation in WLANs using Multi-Armed Bandits .
Edit intent: clarity
Original: Birkhoff's theorem, which has also been called%DIFDELCMD < {\em %%% the fundamental theorem for finite distributive lattices , states that the elements of any such lattice%DIFDELCMD < \cal %%% L are isomorphic to ] ]  the closed sets of a partial order, say \Pi.
Revised: %DIFDELCMD < {\em %%% the fundamental theorem for finite distributive lattices , states that the elements of any such lattice%DIFDELCMD < \cal %%% L are isomorphic to ] ] the closed sets of a partial order, say \Pi.
Edit intent: coherence
Original: Birkhoff's theorem, which has also been called%DIFDELCMD < {\em %%% the fundamental theorem for finite distributive lattices , states that the elements of any such lattice%DIFDELCMD < \cal %%% L are isomorphic to ] ]  the closed sets of a partial order, say \Pi.
Revised: Birkhoff's theorem, which has also been called%DIFDELCMD < {\em %%% , states that the elements of any such lattice%DIFDELCMD < \cal %%% L are isomorphic to ] ] the closed sets of a partial order, say \Pi.
Edit intent: clarity
Original: Birkhoff's theorem, which has also been called%DIFDELCMD < {\em %%% the fundamental theorem for finite distributive lattices , states that the elements of any such lattice%DIFDELCMD < \cal %%% L are isomorphic to ] ]  the closed sets of a partial order, say \Pi.
Revised: Birkhoff's theorem, which has also been called%DIFDELCMD < {\em %%% the fundamental theorem for finite distributive lattices %DIFDELCMD < \cal %%% L are isomorphic to ] ] the closed sets of a partial order, say \Pi.
Edit intent: clarity
Original:  We generalize this theorem to showing that each sublattice of%DIFDELCMD < \cal %%% L is isomorphic to a distinct partial order that can be obtained from \Pi via the operation of%DIFDELCMD < {\em %%% compression , defined in this paper.
Revised:  We generalize this theorem to showing that each sublattice of%DIFDELCMD < \cal %%% %DIFDELCMD < {\em %%% compression , defined in this paper.
Edit intent: clarity
Original:  We generalize this theorem to showing that each sublattice of%DIFDELCMD < \cal %%% L is isomorphic to a distinct partial order that can be obtained from \Pi via the operation of%DIFDELCMD < {\em %%% compression , defined in this paper.
Revised:  We generalize this theorem to showing that each sublattice of%DIFDELCMD < \cal %%% L is isomorphic to a distinct partial order that can be obtained from \Pi via the operation of%DIFDELCMD < {\em %%% , defined in this paper.
Edit intent: clarity
Original:  Using our }  generalization of Birkhoff's Theorem , we give an efficient algorithm for finding the compression of \Pi that is isomorphic to the lattice of \mathcal{M_A \cap \mathcal{M}_B. - Given a polynomial sized domain D of such errors (of permuting one of the preference lists), we give an efficient algorithm that checks if there is a stable matching for A that is stable for each such resulting instance B.
Revised:  Using our } generalization of Birkhoff's Theorem _A \cap \mathcal{M}_B. - Given a polynomial sized domain D of such errors (of permuting one of the preference lists), we give an efficient algorithm that checks if there is a stable matching for A that is stable for each such resulting instance B.
Edit intent: clarity
Original:  We call this a }  {\em fully robust stable matching } . - If yes, the set of all such matchings forms a sublattice of%DIFDELCMD < \cal %%% L and our algorithm finds its partial order as well.
Revised:  We call this a } {\em fully robust stable matching } %DIFDELCMD < \cal %%% L and our algorithm finds its partial order as well.
Edit intent: coherence
Original: We build on our recent paper MV18a%DIFDELCMD < ]%%% , which initiated the problem of finding stable matchings that are robust to errors in the input and gave a polynomial{\em   time algorithm for a special case in which the domain of errors was polynomially large.
Revised:  MV18a%DIFDELCMD < ]%%% , which initiated the problem of finding stable matchings that are robust to errors in the input and gave a polynomial{\em time algorithm for a special case in which the domain of errors was polynomially large.
Edit intent: coherence
Original:  This proof yields crucial new notions, such as that of a%DIFDELCMD < {\em %%% meta-rotation , and is therefore of independent interest.
Revised:  This proof yields crucial new notions, such as that of a%DIFDELCMD < {\em %%% , and is therefore of independent interest.
Edit intent: clarity
Original: A stable matching instance A is transmitted over a channel which introduces%DIFDELCMD < {\em %%% one error from a super-exponentially large set , T;
Revised: We are given a stable matching instance A is transmitted over a channel which introduces%DIFDELCMD < {\em %%% one error from a super-exponentially large set , T;
Edit intent: clarity
Original: A stable matching instance A is transmitted over a channel which introduces%DIFDELCMD < {\em %%% one error from a super-exponentially large set , T;
Revised: A stable matching instance A %DIFDELCMD < {\em %%% one error from a super-exponentially large set , T;
Edit intent: clarity
Original: A stable matching instance A is transmitted over a channel which introduces%DIFDELCMD < {\em %%% one error from a super-exponentially large set , T;
Revised: A stable matching instance A is transmitted over a channel which introduces%DIFDELCMD < {\em %%% from a super-exponentially large set , T;
Edit intent: clarity
Original:  the error consists of arbitrarily permuting the preference list of any one boy or any one girl.
Revised:  the error consists of applying a specific permutation to the preference list of any one boy or any one girl.
Edit intent: clarity
Original: Polyampholytes (PAs) contain positively and negatively charged monomers that are distributed randomly along the polymer chain.
Revised: Random polyampholytes (PAs) contain positively and negatively charged monomers that are distributed randomly along the polymer chain.
Edit intent: clarity
Original:  In the opposite limit, R_g increases monotonically with increasing \kappa.
Revised:  In the opposite limit, R_g decreases monotonically with increasing \kappa.
Edit intent: fluency
Original: Understanding the relationship between  structural and functional connectivity remains a crucial issue in modern neuroscience.
Revised: Understanding the relationship between structural and functional brain networks remains a crucial issue in modern neuroscience.
Edit intent: clarity
Original:  In this work, we show that equalization of the nodes excitatory input improves the correlation between simulated neural patterns of the model and various brain functional data.
Revised:  In this work, we show that normalization of the node's excitatory input improves the correlation between simulated neural patterns of the model and various brain functional data.
Edit intent: fluency
Original:  In this work, we show that equalization of the nodes excitatory input improves the correlation between simulated neural patterns of the model and various brain functional data.
Revised:  In this work, we show that equalization of the nodes excitatory input improves the correspondence between simulated neural patterns of the model and various brain functional data.
Edit intent: clarity
Original:  We find that the best model performance is achieved when the model control parameter is in its critical value and that equalization minimizes both the variability of the critical points and neuronal activity patterns among subjects.
Revised:  Indeed, we find that the best model performance is achieved when the model control parameter is in its critical value and that equalization minimizes both the variability of the critical points and neuronal activity patterns among subjects.
Edit intent: coherence
Original:  We find that the best model performance is achieved when the model control parameter is in its critical value and that equalization minimizes both the variability of the critical points and neuronal activity patterns among subjects.
Revised:  We find that the best match is achieved when the model control parameter is in its critical value and that equalization minimizes both the variability of the critical points and neuronal activity patterns among subjects.
Edit intent: clarity
Original:  We find that the best model performance is achieved when the model control parameter is in its critical value and that equalization minimizes both the variability of the critical points and neuronal activity patterns among subjects.
Revised:  We find that the best model performance is achieved when the model control parameter is in its critical value and that normalization minimizes both the variability of the critical points and neuronal activity patterns among subjects.
Edit intent: clarity
Original:  Our results open new perspectives in personalized brain modelling with potential applications to investigate the deviation from criticality due to structural lesions (e.g. stroke) or brain disorders.
Revised:  Our results open new perspectives in personalized brain modeling with potential applications to investigate the deviation from criticality due to structural lesions (e.g. stroke) or brain disorders.
Edit intent: fluency
Original: Graph classification is a fundamental but challenging problem due to the non-Euclidean property of graph .
Revised: Graph classification is a fundamental but challenging problem due to the non-Euclidean topology .
Edit intent: clarity
Original:  In this work, we jointly leverage the powerful representation ability of random walk and the essential success of standard convolutional network work (CNN), to propose a random walkbased convolutional network, called walk-steered convolution (WSC).
Revised:  In this work, we propose a walk-steered convolutional (WSC) network to assemble the essential success of standard convolutional network work (CNN), to propose a random walkbased convolutional network, called walk-steered convolution (WSC).
Edit intent: clarity
Original:  Each Gaussian implicitly defines a directions and all of them properly encode the spatial layout of walks after the gradient projecting to the  space of Gaussian parameters.
Revised:  Each Gaussian implicitly defines a directions and all of them properly encode the spatial layout of walks after the gradient projecting to the gradient space of Gaussian parameters.
Edit intent: fluency
Original:  Further, a graph coarsening layer using dynamical clustering is stacked upon the Gaussian encoding to capture high-level semantics of graph . Comprehensive evaluations on several public datasets well demonstrate the superiority of our proposed graph learning method over other state-of-the-arts for graph classification.
Revised:  We further stack graph coarsening upon Gaussian encoding by using dynamic clustering, such that high-level semantics of graph . Comprehensive evaluations on several public datasets well demonstrate the superiority of our proposed graph learning method over other state-of-the-arts for graph classification.
Edit intent: clarity
Original:  Further, a graph coarsening layer using dynamical clustering is stacked upon the Gaussian encoding to capture high-level semantics of graph . Comprehensive evaluations on several public datasets well demonstrate the superiority of our proposed graph learning method over other state-of-the-arts for graph classification.
Revised:  Further, a graph coarsening layer using dynamical clustering is stacked upon the Gaussian encoding to capture high-level semantics of graph . Comprehensive evaluations on several public datasets demonstrate the superiority of our proposed graph learning method over other state-of-the-arts for graph classification.
Edit intent: clarity
Original:  Further, a graph coarsening layer using dynamical clustering is stacked upon the Gaussian encoding to capture high-level semantics of graph . Comprehensive evaluations on several public datasets well demonstrate the superiority of our proposed graph learning method over other state-of-the-arts for graph classification.
Revised:  Further, a graph coarsening layer using dynamical clustering is stacked upon the Gaussian encoding to capture high-level semantics of graph . Comprehensive evaluations on several public datasets well demonstrate the superiority of our proposed WSC method over many state-of-the-arts for graph classification.
Edit intent: clarity
Original:  While early trading was affected by system-specific irregularities, it is found that over the months preceding April 2018 all these statistical indicators approach the features hallmarking maturity.
Revised:  While early trading was affected by system-specific irregularities, it is found that over the months preceding Apr 2018 all these statistical indicators approach the features hallmarking maturity.
Edit intent: fluency
Original:  To answer these questions we train multiple versions of knowledge network-based HG system, Moliere, on varying corpora in order to compare challenges and tradeoffs in terms of result quality and computational requirements.
Revised:  To answer these questions we train multiple versions of knowledge network-based HG system, Moliere, on varying corpora in order to compare challenges and trade offs in terms of result quality and computational requirements.
Edit intent: fluency
Original:  Reproducibility: Our code can be found at github.com/ JSybrandt /moliere, and our data is hosted at bit.ly/2GxghpM  .
Revised:  Reproducibility: Our code and data are available at github.com/ JSybrandt /moliere, and our data is hosted at bit.ly/2GxghpM .
Edit intent: clarity
Original:  Reproducibility: Our code can be found at github.com/ JSybrandt /moliere, and our data is hosted at bit.ly/2GxghpM  .
Revised:  Reproducibility: Our code can be found at github.com/ JSybrandt /moliere, and bit.ly/2GxghpM .
Edit intent: coherence
Original:  Recent work has considered the relationship between f-subgraph centralities, which defined using the matrix exponential  , and the walk structure of a network.
Revised:  Recent work has considered the relationship between subgraph centrality, which is defined using the matrix exponential , and the walk structure of a network.
Edit intent: clarity
Original:  In a walk-regular graph, the number of closed walks of each length must be the same for all nodes, implying uniform f-subgraph centralities for any f (or maximum f-walk entropy ).
Revised:  In a walk-regular graph, the number of closed walks of each length must be the same for all nodes, implying uniform f-subgraph centralities for any f (or maximum f- ).
Edit intent: clarity
Original:  In parameterized measures, we are also interested in which values of the parameter witness this uniformity.
Revised:  In. For parameterized measures, we are also interested in which values of the parameter witness this uniformity.
Edit intent: coherence
Original: A conceptual design for a quantum blockchain is proposed .
Revised: We propose a conceptual design for a quantum blockchain is proposed .
Edit intent: clarity
Original: A conceptual design for a quantum blockchain is proposed .
Revised: A conceptual design for a quantum blockchain .
Edit intent: clarity
Original:  Perhaps more shockingly , our encoding procedure can be interpreted as non-classically influencing the past ;
Revised:  Furthermore , our encoding procedure can be interpreted as non-classically influencing the past ;
Edit intent: coherence
Original:  Perhaps more shockingly , our encoding procedure can be interpreted as non-classically influencing the past ;
Revised:  Perhaps more shockingly , our encoding procedure can be interpreted as nonclassically influencing the past ;
Edit intent: fluency
Original:  We use both automatic and human evaluation approaches for assessing the performance of our recommendation system, concluding that the category--based approach works best, achieving precision and recall at 10 of about 80 %DIFDELCMD < \\%%% %DIF < in the crowdsourcing evaluation.abstract 
Revised:  We use both automatic and human evaluation approaches for assessing the performance of our recommendation system, concluding that the category-based approach works best, achieving precision and recall at 10 of about 80 %DIFDELCMD < \\%%% %DIF < in the crowdsourcing evaluation.abstract 
Edit intent: fluency
Original:  We use both automatic and human evaluation approaches for assessing the performance of our recommendation system, concluding that the category--based approach works best, achieving precision and recall at 10 of about 80 %DIFDELCMD < \\%%% %DIF < in the crowdsourcing evaluation.abstract 
Revised:  We use both automatic and human evaluation approaches for assessing the performance of our recommendation system, concluding that the category--based approach works best, achieving precision 10 of about 80 %DIFDELCMD < \\%%% %DIF < in the crowdsourcing evaluation.abstract 
Edit intent: clarity
Original: This paper proposes a new tensor completion method based on tensor trains and system identification.
Revised: We propose a new tensor completion method based on tensor trains and system identification.
Edit intent: clarity
Original:  The to be completed tensor is modeled as a low-rank tensor train system and the coordinates and corresponding tensor entries are interpreted as system inputs and outputs, respectively .
Revised:  The to be completed tensor is modeled as a low-rank tensor train , where we use the known tensor entries and their coordinates to update the tensor train .
Edit intent: clarity
Original:  A novel tensor train initialization procedure is proposed specifically for image and video completion, which ensures faster convergence of the completion algorithm.
Revised:  A novel tensor train initialization procedure is proposed specifically for image and video completion, which is demonstrated to ensure fast convergence of the completion algorithm.
Edit intent: clarity
Original:  Image and video inpainting experiments verify the superiority of the proposed scheme in terms of both speed , accuracy, and scalability, where a speedup of up to 60X is observed compared to state-of-the-art tensor completion methods at a similar accuracy  .
Revised:  Image and video inpainting experiments verify the superiority of the proposed scheme in terms of both speed and scalability, where a speedup of up to 60X is observed compared to state-of-the-art tensor completion methods at a similar accuracy .
Edit intent: fluency
Original:  With these results, we further show comparison results for the mean AoI  in the M/GI/1 and GI/M/1 queues among those service disciplines.
Revised:  With these results, we further show comparison results for the mean AoI 's in the M/GI/1 and GI/M/1 queues among those service disciplines.
Edit intent: fluency
Original:  With these results, we further show comparison results for the mean AoI  in the M/GI/1 and GI/M/1 queues among those service disciplines.
Revised:  With these results, we further show comparison results for the mean AoI in the M/GI/1 and GI/M/1 queues under those service disciplines.
Edit intent: clarity
Original: In this paper we present a factorization framework for Hermite subdivision operators which satisfy the spectral condition of order d \geq 1.
Revised: In this paper we present a factorization framework for Hermite subdivision operators which satisfy the spectral condition of high order.
Edit intent: clarity