wanyu/IteraTeR_v2 huggingface dataset:
Original:  The implications are varied and can affect fundamental debates, like whether the brain is working near a critical point, and lead to applications including advanced tools to simplify the digital reconstruction and simulation of the brain.
Revised:  The implications are varied and can be substantial for fundamental debates, like whether the brain is working near a critical point, and lead to applications including advanced tools to simplify the digital reconstruction and simulation of the brain.
Edit intent: clarity
Original:  The implications are varied and can affect fundamental debates, like whether the brain is working near a critical point, and lead to applications including advanced tools to simplify the digital reconstruction and simulation of the brain.
Revised:  The implications are varied and can affect fundamental debates, like whether the brain is working near a critical point, and for applications including advanced tools to simplify the digital reconstruction and simulation of the brain.
Edit intent: coherence
Original:  However, the brain possesses a rich architecture organized in multiple scales linked to one another.
Revised:  However, the brain possesses a rich architecture organized over multiple scales linked to one another.
Edit intent: fluency
Original:  We explored the multiscale organization of the human connectome using datasets of healthy subjects reconstructed at five different resolutions.
Revised:  We explored the multiscale organization of human connectomes using datasets of healthy subjects reconstructed at five different resolutions.
Edit intent: fluency
Original:  We found that the structure of the human brain remains self-similar when the resolution length is progressively decreased by hierarchical coarse-graining of the anatomical regions.
Revised:  We found that the structure of the human brain remains self-similar when the resolution of observation is progressively decreased by hierarchical coarse-graining of the anatomical regions.
Edit intent: clarity
Original:  Our results prove that simplicity is one of the organizing principles of the large-scale self-similar architecture of human structural brain networks, with the same connectivity law ruling short and long range connections and operating between brain regions at different length scales.
Revised:  Our results suggest that simple organizing principles underlie the multiscale architecture of human structural brain networks, with the same connectivity law ruling short and long range connections and operating between brain regions at different length scales.
Edit intent: clarity
Original:  Our results prove that simplicity is one of the organizing principles of the large-scale self-similar architecture of human structural brain networks, with the same connectivity law ruling short and long range connections and operating between brain regions at different length scales.
Revised:  Our results prove that simplicity is one of the organizing principles of the large-scale self-similar architecture of human structural brain networks, where the same connectivity law ruling short and long range connections and operating between brain regions at different length scales.
Edit intent: coherence
Original:  The implications are varied and can be substantial for fundamental debates, like whether the brain is working near a critical point, and for applications including advanced tools to simplify the digital reconstruction and simulation of the brain.
Revised:  The implications are varied and can be substantial for fundamental debates, such as whether the brain is working near a critical point, and for applications including advanced tools to simplify the digital reconstruction and simulation of the brain.
Edit intent: fluency
Original:  The implications are varied and can be substantial for fundamental debates, like whether the brain is working near a critical point, and for applications including advanced tools to simplify the digital reconstruction and simulation of the brain.
Revised:  The implications are varied and can be substantial for fundamental debates, like whether the brain is working near a critical point, as well as for applications including advanced tools to simplify the digital reconstruction and simulation of the brain.
Edit intent: coherence
Original: The Markov chain Monte Carlo (MCMC) methods are the primary tools for sampling from graphical models, e.g. Markov random fields ( MRF).
Revised: Probabilistic graphical models, e.g. Markov random fields ( MRF).
Edit intent: clarity
Original: The Markov chain Monte Carlo (MCMC) methods are the primary tools for sampling from graphical models, e.g. Markov random fields ( MRF).
Revised: The Markov chain Monte Carlo (MCMC) methods are the primary tools for sampling from graphical models, such as Markov random fields ( MRF).
Edit intent: clarity
Original:  The problem is well motivated by the growing volume and velocity of data in today's applications of the MCMC methods.
Revised:  Such dynamic inference problems arise naturally in today's applications of the MCMC methods.
Edit intent: clarity
Original:  Furthermore, our algorithms are efficient with (n  ) space cost, and (\log^2n) incremental time cost upon each local update to the input MRF, as long as certain decay conditions are satisfied in each step by natural couplings of the corresponding single-site chains.
Revised:  Furthermore, our algorithms are efficient with (n ) space cost, and (\log^2n) incremental time cost upon each update to the input MRF, as long as certain decay conditions are satisfied in each step by natural couplings of the corresponding single-site chains.
Edit intent: clarity
Original:  Furthermore, our algorithms are efficient with (n  ) space cost, and (\log^2n) incremental time cost upon each local update to the input MRF, as long as certain decay conditions are satisfied in each step by natural couplings of the corresponding single-site chains.
Revised:  Furthermore, our algorithms are efficient with (n ) space cost, and (\log^2n) incremental time cost upon each local update to the MRF, as long as certain decay conditions are satisfied in each step by natural couplings of the corresponding single-site chains.
Edit intent: clarity
Original:  We tested our model according to five broadly accepted metrics (including the widely used bleu ), showing that it yields competitive performance with respect to both character-based and word-based approaches.
Revised:  We tested our model according to five broadly accepted metrics (including the widely used BLEU ), showing that it yields competitive performance with respect to both character-based and word-based approaches.
Edit intent: fluency
Original: Research communications often introduce biases or misrepresentations without providing reliable links to the research they use so readers can check the veracity of the claims being made .
Revised: Online health communications often provide biased interpretations of evidence and have unreliable links to the research they use so readers can check the veracity of the claims being made .
Edit intent: clarity
Original: Research communications often introduce biases or misrepresentations without providing reliable links to the research they use so readers can check the veracity of the claims being made .
Revised: Research communications often introduce biases or misrepresentations without providing reliable links to the source research .
Edit intent: clarity
Original:  We tested the feasibility of a tool that can be used to automatically recommend research articles to research communications .
Revised:  We tested the feasibility of a tool for matching webpages to their source evidence .
Edit intent: clarity
Original:  From 207,538  vaccination-related PubMed articles, we selected 3,573 unique links to webpages using Altmetric.
Revised:  From 207,538 vaccination-related PubMed articles, we selected 3,573 unique links to webpages from Altmetric.
Edit intent: style
Original:  and in the top 50 for 38\% . Tools to help people identify source articles for vaccination-related research communications are potentially feasible and may support the prevention of bias and misrepresentation of research in news and social media.
Revised:  and in the top 50 for 38\% . Tools to help people identify evidence-based sources for the content they access on vaccination-related research communications are potentially feasible and may support the prevention of bias and misrepresentation of research in news and social media.
Edit intent: clarity
Original:  and in the top 50 for 38\% . Tools to help people identify source articles for vaccination-related research communications are potentially feasible and may support the prevention of bias and misrepresentation of research in news and social media.
Revised:  and in the top 50 for 38\% . Tools to help people identify source articles for vaccination-related webpages are potentially feasible and may support the prevention of bias and misrepresentation of research in news and social media.
Edit intent: clarity
Original:  We also make novel inroads into a different notion for quantum mixing: the problem of sampling from the limiting distribution of quantum walks, defined in a time-averaged sense.
Revised:  There also exists a different notion for quantum mixing: the problem of sampling from the limiting distribution of quantum walks, defined in a time-averaged sense.
Edit intent: clarity
Original:  In this article, using several results in random matrix theory, we prove an upper bound on the quantum mixing time of Erd\"os-Renyi random graphs : graphs of n nodes where each edge exists with probability p independently.
Revised:  In this article, using several results in random matrix theory, we prove an upper bound on the quantum mixing time for Erd\"os-Renyi random graphs : graphs of n nodes where each edge exists with probability p independently.
Edit intent: fluency
Original:  For example for dense random graphs, where p is a constant, we show that the quantum mixing time is O(n^{3/2 + o(1) almost all graphs , i.e.\ the fraction of graphs for which this bound holds, goes to one in the asymptotic limit  .
Revised:  For example for dense random graphs, where p is a constant, we show that the quantum mixing time is O(n^{3/2 + o(1) , i.e.\ the fraction of graphs for which this bound holds, goes to one in the asymptotic limit .
Edit intent: clarity
Original: Cardiovascular disease (CVD) is considered as one of the main causes of death in the world.
Revised: Cardiovascular Disease (CVD) is considered as one of the main causes of death in the world.
Edit intent: fluency
Original: Cardiovascular disease (CVD) is considered as one of the main causes of death in the world.
Revised: Cardiovascular disease (CVD) is considered as one of the principal causes of death in the world.
Edit intent: clarity
Original:  Accordingly, scientists look for methods to recognize normal/abnormal heart patterns.
Edit intent: coherence
Original:  In this study we proposed an approach for normal/abnormal heart sound detection, based on i-vector features on phiysionet 2016 corpus.
Revised:  In this study , an approach is proposed for normal/abnormal heart sound detection, based on i-vector features on phiysionet 2016 corpus.
Edit intent: clarity
Original:  In this study we proposed an approach for normal/abnormal heart sound detection, based on i-vector features on phiysionet 2016 corpus.
Revised:  In this study we proposed an approach for normal/abnormal heart sound classification on the Physionet challenge 2016 corpus.
Edit intent: clarity
Original:  In this method , a fixed length vector, namely i-vector , is extracted from each record, and then Principal Component Analysis (PCA) is applied.
Revised:  In this method , a fixed length feature vector; called i-vector , is extracted from each record, and then Principal Component Analysis (PCA) is applied.
Edit intent: clarity
Original:  In this method , a fixed length vector, namely i-vector , is extracted from each record, and then Principal Component Analysis (PCA) is applied.
Revised:  In this method , a fixed length vector, namely i-vector ; is extracted from each record, and then Principal Component Analysis (PCA) is applied.
Edit intent: fluency
Original:  Then Variational AuotoEncoders (VAE) is used to reduce dimensions of the obtained i-vector .
Revised:  Then Variational AuotoEncoders (VAE) are applied on the i-vector .
Edit intent: clarity
Original:  We reported the results based on Equal Error Rate (EER) and Modified Accuracy (MAcc) . Experimental results show the obtained Accuracy by our proposed system could improve the results reported on the baseline system by 16 \% \\%DIF > based on Modified Accuracy (MAcc) compared with the baseline system on  .
Revised:  We reported the results based on Equal Error Rate (EER) and Modified Accuracy (MAcc) . Experimental results demonstrate the proposed method could achieve a performance improvement of 16 \% \\%DIF > based on Modified Accuracy (MAcc) compared with the baseline system on .
Edit intent: clarity
Original:  We reported the results based on Equal Error Rate (EER) and Modified Accuracy (MAcc) . Experimental results show the obtained Accuracy by our proposed system could improve the results reported on the baseline system by 16 \% \\%DIF > based on Modified Accuracy (MAcc) compared with the baseline system on  .
Revised:  We reported the results based on Equal Error Rate (EER) and Modified Accuracy (MAcc) . Experimental results show the obtained Accuracy by our proposed system could improve the results reported on the baseline system by 16 \\%DIF > based on Modified Accuracy (MAcc) compared with the baseline system on .
Edit intent: fluency
Original:  For the first time, a fixed length feature vector;
Revised:  For the first time, a fixed-length feature vector;
Edit intent: fluency
Original:  and there is a nontrivial storage cost of the projected system.
Revised:  and there is a nontrivial storage cost of the sketched system.
Edit intent: clarity
Original: How well does a classic deep net architecture like AlexNet or VGG19 classify on a standard dataset such as CIFAR-10 when its "width " --- namely, number of channels in convolutional layers, and number of nodes in fully-connected internal layers --- is allowed to increase to infinity?
Revised: How well does a classic deep net architecture like AlexNet or VGG19 classify on a standard dataset such as CIFAR-10 when its width --- namely, number of channels in convolutional layers, and number of nodes in fully-connected internal layers --- is allowed to increase to infinity?
Edit intent: fluency
Original:  A subsequent paper Lee et al., 2019%DIFDELCMD < ] %%% gave heuristic Monte Carlo methods to estimate the NTK and its extension, Convolutional Neural Tangent Kernel (CNTK) and used this to try to understand the limiting behavior on datasets like CIFAR-10 . The current paper gives the first efficient exact algorithm (based upon dynamic programming) for computing CNTK as well as an efficient GPU implementation of this algorithm.
Revised:  Lee et al., 2019%DIFDELCMD < ] %%% gave heuristic Monte Carlo methods to estimate the NTK and its extension, Convolutional Neural Tangent Kernel (CNTK) and used this to try to understand the limiting behavior on datasets like CIFAR-10 . The current paper gives the first efficient exact algorithm (based upon dynamic programming) for computing CNTK as well as an efficient GPU implementation of this algorithm.
Edit intent: coherence
Original:  A subsequent paper Lee et al., 2019%DIFDELCMD < ] %%% gave heuristic Monte Carlo methods to estimate the NTK and its extension, Convolutional Neural Tangent Kernel (CNTK) and used this to try to understand the limiting behavior on datasets like CIFAR-10 . The current paper gives the first efficient exact algorithm (based upon dynamic programming) for computing CNTK as well as an efficient GPU implementation of this algorithm.
Revised:  A subsequent paper %DIFDELCMD < ] %%% gave heuristic Monte Carlo methods to estimate the NTK and its extension, Convolutional Neural Tangent Kernel (CNTK) and used this to try to understand the limiting behavior on datasets like CIFAR-10 . The current paper gives the first efficient exact algorithm (based upon dynamic programming) for computing CNTK as well as an efficient GPU implementation of this algorithm.
Edit intent: clarity
Original:  This results in a significant new benchmark for  performance of a pure kernel-based method on CIFAR-10, being 10 \% higher than the methods reported \\%DIF > higher than the methods reported in [Novak et al., 2019], and only 5\% lower than the performance of the\\%DIF > lower than the performance of the corresponding finite deep net architecture (once batch normalization  etc. are turned off).
Revised:  This results in a significant new benchmark for the performance of a pure kernel-based method on CIFAR-10, being 10 \% higher than the methods reported \\%DIF > higher than the methods reported in [Novak et al., 2019], and only 5\% lower than the performance of the\\%DIF > lower than the performance of the corresponding finite deep net architecture (once batch normalization etc. are turned off).
Edit intent: fluency
Original:  This results in a significant new benchmark for  performance of a pure kernel-based method on CIFAR-10, being 10 \% higher than the methods reported \\%DIF > higher than the methods reported in [Novak et al., 2019], and only 5\% lower than the performance of the\\%DIF > lower than the performance of the corresponding finite deep net architecture (once batch normalization  etc. are turned off).
Revised:  This results in a significant new benchmark for performance of a pure kernel-based method on CIFAR-10, being 10 \\%DIF > higher than the methods reported in [Novak et al., 2019], and only 5\% lower than the performance of the\\%DIF > lower than the performance of the corresponding finite deep net architecture (once batch normalization etc. are turned off).
Edit intent: fluency
Original:  This results in a significant new benchmark for  performance of a pure kernel-based method on CIFAR-10, being 10 \% higher than the methods reported \\%DIF > higher than the methods reported in [Novak et al., 2019], and only 5\% lower than the performance of the\\%DIF > lower than the performance of the corresponding finite deep net architecture (once batch normalization  etc. are turned off).
Revised:  This results in a significant new benchmark for performance of a pure kernel-based method on CIFAR-10, being 10 \% higher than the methods reported \\%DIF > higher than the methods reported in [Novak et al., 2019], and only 5\% lower than the performance of the\\%DIF > lower than the performance of the corresponding finite deep net architecture (once batch normalization , etc. are turned off).
Edit intent: fluency
Original:  Specifically, for any d= O(1), \epsilon = \Omega({n}^{-1{d-1}}): _ We show that any (1+\epsilon)-spanner must have n \Omega(\epsilon^{-d+1}) edges, implying that the greedy (and other) spanners achieve the optimal size.
Revised:  Specifically, for any d= O(1), \epsilon = \Omega({n}^{-1{d-1}}): - We show that any (1+\epsilon)-spanner must have n \Omega(\epsilon^{-d+1}) edges, implying that the greedy (and other) spanners achieve the optimal size.
Edit intent: fluency
Original:  _ We show that any (1+\epsilon)-spanner must have lightness \Omega(\epsilon^{-d}), and then improve the upper bound on the lightness of the greedy spanner from O(\epsilon^{-2d}) to O(\epsilon^{-d}).
Revised:  - We show that any (1+\epsilon)-spanner must have lightness \Omega(\epsilon^{-d}), and then improve the upper bound on the lightness of the greedy spanner from O(\epsilon^{-2d}) to O(\epsilon^{-d}).
Edit intent: fluency
Original:  However, these learned latent embeddings are inherently transductive and are not designed to generalize to unseen users / items or new tasks.
Revised:  However, these learned latent embeddings are inherently transductive and are not designed totransductive generalize to unseen users / items or new tasks.
Edit intent: fluency
Original:  However, these learned latent embeddings are inherently transductive and are not designed to generalize to unseen users / items or new tasks.
Revised:  However, these learned latent embeddings are inherently transductive and are not designed to generalize to unseen rows / items or new tasks.
Edit intent: fluency
Original:  To learn such a model, we extract a local enclosing subgraph for each training (user, item) pair, and feed the subgraphs to a graph neural network (GNN) to train a rating prediction model.
Revised:  To learn such a model, we extract a local enclosing subgraph for each training (user, item) pair, and feed the subgraphs toinductive a graph neural network (GNN) to train a rating prediction model.
Edit intent: clarity
Original:  By factorizing the (rating) matrix into the product of low-dimensional latent embeddings of rows (users) and columns (items), a majority of existing matrix completion methods are transductive , since the learned embeddings cannot generalize to unseen rows/columns or to new matrices.
Revised:  By factorizing the (rating) matrix into the product of low-dimensional latent embeddings of rows (users) and columns (items), a majority of existing matrix completion methods are transductive , since the learned embeddings cannot generalize to unseen rows/columns or to new matrices.
Edit intent: fluency
Original:  To make matrix completion inductive , content (side information), such as user's age or movie's genre, has to be used previously .
Revised:  To make matrix completion , content (side information), such as user's age or movie's genre, has to be used previously .
Edit intent: clarity
Original:  To make matrix completion inductive , content (side information), such as user's age or movie's genre, has to be used previously .
Revised:  To make matrix completion inductive , content (side information), such as user's age or movie's genre, to make predictions .
Edit intent: clarity
Original:  In this paper, we investigate this seemingly impossible problem and propose an Inductive Graph-based Matrix Completion (IGMC) model without using any side information.
Revised:  In this paper, we propose an Inductive Graph-based Matrix Completion (IGMC) model without using any side information.
Edit intent: coherence
Original:  Our model achieves highly competitive performance with state-of-the-art transductive baselines.
Revised:  It achieves highly competitive performance with state-of-the-art transductive baselines.
Edit intent: clarity
Original:  In addition, since our model is inductive , it can generalize to users/items unseen during the training (given that their ratings exist), and can even transfer to new tasks.
Revised:  In addition, IGMC is inductive -- it can generalize to users/items unseen during the training (given that their ratings exist), and can even transfer to new tasks.
Edit intent: clarity
Original:  In addition, since our model is inductive , it can generalize to users/items unseen during the training (given that their ratings exist), and can even transfer to new tasks.
Revised:  In addition, since our model is inductive , it can generalize to users/items unseen during the training (given that their interactions exist), and can even transfer to new tasks.
Edit intent: clarity
Original: This paper presents a self-supervised deep neural network solution to speech denoising by easing the requirement that clean speech signals need to be available for network training.
Revised: Existing deep learning-based speech denoising approaches require clean speech signals need to be available for network training.
Edit intent: clarity
Original: This paper presents a self-supervised deep neural network solution to speech denoising by easing the requirement that clean speech signals need to be available for network training.
Revised: This paper presents a self-supervised deep neural network solution to speech denoising by easing the requirement that clean speech signals to be available for network training.
Edit intent: coherence