wanyu/IteraTeR_v2 huggingface dataset:
Original:  We provide a simulation study which demonstrates the good behaviour of the method in terms of outliers selection and prediction of the missing links  .
Revised:  We provide a simulation study which demonstrates the good behaviour of the method in terms of outliers detection and prediction of the missing links .
Edit intent: clarity
Original: The distances are shown to posses fast implementations and are included in an adapted Variational Auto-Encoder termed X-ray Sobolev Variational Auto-Encoder (XS-VAE) which produces good quality results on standard generative datasets.
Revised: The distances are shown to posses fast implementations andare included in an adapted Variational Auto-Encoder termed X-ray Sobolev Variational Auto-Encoder (XS-VAE) which produces good quality results on standard generative datasets.
Edit intent: fluency
Original: The distances are shown to posses fast implementations and are included in an adapted Variational Auto-Encoder termed X-ray Sobolev Variational Auto-Encoder (XS-VAE) which produces good quality results on standard generative datasets.
Revised: The distances are shown to posses fast implementations and are included in an adapted Variational Auto-Encoder termed X-ray Sobolev Variational Auto-Encoder (XS-VAE) which produces good quality resultson standard generative datasets.
Edit intent: fluency
Original: The quality of the generative models (  Generative adversarial networks , Variational Auto-Encoders , ... ) depends heavily on the choice of a good probability distance.
Revised: The quality of generative models ( Generative adversarial networks , Variational Auto-Encoders , ... ) depends heavily on the choice of a good probability distance.
Edit intent: fluency
Original: The quality of the generative models (  Generative adversarial networks , Variational Auto-Encoders , ... ) depends heavily on the choice of a good probability distance.
Revised: The quality of the generative models ( such as Generative adversarial networks , Variational Auto-Encoders , ... ) depends heavily on the choice of a good probability distance.
Edit intent: clarity
Original: The quality of the generative models (  Generative adversarial networks , Variational Auto-Encoders , ... ) depends heavily on the choice of a good probability distance.
Revised: The quality of the generative models ( Generative adversarial networks and Variational Auto-Encoders , ... ) depends heavily on the choice of a good probability distance.
Edit intent: fluency
Original: The quality of the generative models (  Generative adversarial networks , Variational Auto-Encoders , ... ) depends heavily on the choice of a good probability distance.
Revised: The quality of the generative models ( Generative adversarial networks , Variational Auto-Encoders ) depends heavily on the choice of a good probability distance.
Edit intent: fluency
Original:  The distances are shown to posses fast implementations andare included in an adapted Variational Auto-Encoder termed X-ray Sobolev Variational Auto-Encoder ( XS-VAE ) which produces good quality resultson standard generative datasets. 
Revised:  The distances are shown to possess fast implementations and are included in an adapted Variational Auto-Encoder termed X-ray Sobolev Variational Auto-Encoder ( XS-VAE ) which produces good quality resultson standard generative datasets. 
Edit intent: fluency
Original:  The distances are shown to posses fast implementations andare included in an adapted Variational Auto-Encoder termed X-ray Sobolev Variational Auto-Encoder ( XS-VAE ) which produces good quality resultson standard generative datasets. 
Revised:  The distances are shown to posses fast implementations andare included in an adapted Variational Auto-Encoder termed X-ray Sobolev Variational Auto-Encoder ( XS-VAE ) which produces high quality results on standard generative datasets. 
Edit intent: clarity
Original: In this paper we are interested in the all-pairs shortest paths problem (APSP) for an input graph G assumed to be connected undirected with non negative real edge weights.
Revised: In this paper we are interested in the all-pairs shortest paths problem (APSP) for an input graph G assumed to be connected undirected with nonnegative real edge weights.
Edit intent: fluency
Original: In this paper we are interested in the all-pairs shortest paths problem (APSP) for an input graph G assumed to be connected undirected with nonnegative real edge weights.
Revised: In this paper we are interested in a version of the All-pairs Shortest Paths problem (APSP) for an input graph G assumed to be connected undirected with nonnegative real edge weights.
Edit intent: clarity
Original:  We define a measure of centrality of a shortest path, related to the "importance" of such shortest path in the graph, and propose a randomized algorithm that, for {\it any fixed constants} 0 < \epsilon, \delta < 1, given an undirected graph G with non-negative edge weights, outputs with probability 1 - \delta a n^2 sized matrix in time O( n^2 \lg n ) containing the (exact)  shortest path between every pair of vertices (u,v) that has centrality at least \epsilon.
Revised:  We define a measure of centrality of a shortest path, related to the ``importance'' of such shortest path in the graph, and propose a randomized algorithm that, for {\it any fixed constants} 0 < \epsilon, \delta < 1, given an undirected graph G with non-negative edge weights, outputs with probability 1 - \delta a n^2 sized matrix in time O( n^2 \lg n ) containing the (exact) shortest path between every pair of vertices (u,v) that has centrality at least \epsilon.
Edit intent: fluency
Original:  We define a measure of centrality of a shortest path, related to the "importance" of such shortest path in the graph, and propose a randomized algorithm that, for {\it any fixed constants} 0 < \epsilon, \delta < 1, given an undirected graph G with non-negative edge weights, outputs with probability 1 - \delta a n^2 sized matrix in time O( n^2 \lg n ) containing the (exact)  shortest path between every pair of vertices (u,v) that has centrality at least \epsilon.
Revised:  We define a measure of centrality of a shortest path, related to the "importance" of such shortest path in the graph, and propose a randomized algorithm that, for {\it any fixed constants} 0 < \epsilon, \delta < 1, given an undirected graph G with non-negative edge weights, outputs with probability 1 - \delta a data structure of size n \cdot time O( n^2 \lg n ) containing the (exact) shortest path between every pair of vertices (u,v) that has centrality at least \epsilon.
Edit intent: clarity
Original:  We define a measure of centrality of a shortest path, related to the "importance" of such shortest path in the graph, and propose a randomized algorithm that, for {\it any fixed constants} 0 < \epsilon, \delta < 1, given an undirected graph G with non-negative edge weights, outputs with probability 1 - \delta a n^2 sized matrix in time O( n^2 \lg n ) containing the (exact)  shortest path between every pair of vertices (u,v) that has centrality at least \epsilon.
Revised:  We define a measure of centrality of a shortest path, related to the "importance" of such shortest path in the graph, and propose a randomized algorithm that, for {\it any fixed constants} 0 < \epsilon, \delta < 1, given an undirected graph G with non-negative edge weights, outputs with probability 1 - \delta a n^2 sized matrix inDiam time O( n^2 \lg n ) containing the (exact) shortest path between every pair of vertices (u,v) that has centrality at least \epsilon.
Edit intent: fluency
Original:  We define a measure of centrality of a shortest path, related to the "importance" of such shortest path in the graph, and propose a randomized algorithm that, for {\it any fixed constants} 0 < \epsilon, \delta < 1, given an undirected graph G with non-negative edge weights, outputs with probability 1 - \delta a n^2 sized matrix in time O( n^2 \lg n ) containing the (exact)  shortest path between every pair of vertices (u,v) that has centrality at least \epsilon.
Revised:  We define a measure of centrality of a shortest path, related to the "importance" of such shortest path in the graph, and propose a randomized algorithm that, for {\it any fixed constants} 0 < \epsilon, \delta < 1, given an undirected graph G with non-negative edge weights, outputs with probability 1 - \delta a n^2 sized matrix in time O( n^2 \lg n ) containing the (exact) distance and the shortest path between every pair of vertices (u,v) that has centrality at least \epsilon.
Edit intent: clarity
Original:  The central idea is to sample shortest path trees so that all shortest paths with the desired centrality are covered and our main contribution is showing a bound on the  sample size using tools from VC-dimension theory .
Revised:  The progressive sampling technique is sensitive to the sample size using tools from VC-dimension theory .
Edit intent: clarity
Original:  The central idea is to sample shortest path trees so that all shortest paths with the desired centrality are covered and our main contribution is showing a bound on the  sample size using tools from VC-dimension theory .
Revised:  The central idea is to sample shortest path trees so that all shortest paths with the desired centrality are covered and our main contribution is showing a bound on the sample size using VC-dimension theory .
Edit intent: clarity
Original:  We optimize both the forward and backward pass of our rendering layer to make it run efficiently with affordable memory consumption on a commodity graphics card.
Revised:  We optimize both the forward and backward passes of our rendering layer to make it run efficiently with affordable memory consumption on a commodity graphics card.
Edit intent: fluency
Original:  Our rendering method is fully differentiable such that losses can be directly computed on the rendered 2D observations, and the gradients can be propagated backward to optimize the 3D geometry.
Revised:  Our rendering method is fully differentiable such that losses can be directly computed on the rendered 2D observations, and the gradients can be propagated backwards to optimize the 3D geometry.
Edit intent: fluency
Original:  With the geometry based reasoning, our 3D shape prediction methods show excellent generalization capability and robustness against various noise .
Revised:  With the geometry based reasoning, our 3D shape prediction methods show excellent generalization capability and robustness against various noises .
Edit intent: fluency
Original:  In this paper, we first show that an adaptation of Conv-Tasnet (Luo \& Mesgarani, 2019), a waveform-to-waveform model for source separation for speech,significantly beats the state-of-the-art on the MusDB dataset, the standard benchmark of multi-instrument source separation .
Revised:  In this paper, we first show that an adaptation of Conv-Tasnet , initially developed for speech source separation,to the task of music source separation for speech,significantly beats the state-of-the-art on the MusDB dataset, the standard benchmark of multi-instrument source separation .
Edit intent: clarity
Original:  In this paper, we first show that an adaptation of Conv-Tasnet (Luo \& Mesgarani, 2019), a waveform-to-waveform model for source separation for speech,significantly beats the state-of-the-art on the MusDB dataset, the standard benchmark of multi-instrument source separation .
Revised:  In this paper, we first show that an adaptation of Conv-Tasnet (Luo \& Mesgarani, 2019), a waveform-to-waveform model for source separation .
Edit intent: clarity
Original:  Second, we observe that Conv-Tasnet follows a masking approach on the input signal, which has the potential drawback of removing parts of the relevant source without the capacity to reconstruct it .
Revised:  While Conv-Tasnet follows a masking approach on the input signal, which has the potential drawback of removing parts of the relevant source without the capacity to reconstruct it .
Edit intent: coherence
Original:  We propose  Demucs, a new waveform-to-waveform model, which has an architecture closer to models for audio generation with more capacity on the decoder .
Revised:  We propose instead Demucs, a new waveform-to-waveform model, which has an architecture closer to models for audio generation with more capacity on the decoder .
Edit intent: coherence
Original:  We propose  Demucs, a new waveform-to-waveform model, which has an architecture closer to models for audio generation with more capacity on the decoder .
Revised:  We propose Demucs, a novel waveform-to-waveform model, which has an architecture closer to models for audio generation with more capacity on the decoder .
Edit intent: clarity
Original: Adversarial or test time robustness measures the susceptibility of a machine learning system to small perturbations made to the input  at test time  .
Revised: Many machine learning systems are vulnerable to small perturbations made to the input at test time .
Edit intent: clarity
Original: Adversarial or test time robustness measures the susceptibility of a machine learning system to small perturbations made to the input  at test time  .
Revised: Adversarial or test time robustness measures the susceptibility of a machine learning system to small perturbations made to the input either at test time .
Edit intent: clarity
Original:  This has attracted much interest on the empirical side, since many existing ML systems perform poorly under imperceptible adversarial perturbations to the test inputs.
Revised:  This has received much recent interest on the empirical side, since many existing ML systems perform poorly under imperceptible adversarial perturbations to the test inputs.
Edit intent: clarity
Original:  On the other hand, our theoretical understanding of this phenomenon is limited, and has mostly focused on supervised learning tasks .
Revised:  On the other hand, our theoretical understanding of the design of adversarially robust algorithms for the above settings is limited .
Edit intent: clarity
Original:  We formulate a natural extension of Principal Component Analysis (PCA) where the goal is to find a low dimensional subspace to represent the given data with minimum projection error, and that is in addition robust to small perturbations measured in \ell_q norm (say q=\infty).
Revised:  We formulate a natural robust variant of PCA, where the goal is to find a low dimensional subspace to represent the given data with minimum projection error, and that is in addition robust to small perturbations measured in \ell_q norm (say q=\infty).
Edit intent: clarity
Original:  We show the following algorithmic and statistical results . -
Revised:  We show various algorithmic and statistical results . -
Edit intent: clarity
Original:  We show the following algorithmic and statistical results . -
Revised:  We show the following algorithmic and statistical results including: -
Edit intent: clarity
Original:  We prove that our formulation (and algorithms) also enjoy significant statistical benefits in terms of sample complexity over standard PCA on account of a "regularization effect" , that is formalized using the well-studied spiked covariance model. - Surprisingly, we show that our algorithmic techniques can also be made robust to corruptions in the training data, in addition to yielding representations that are robust at test time!
Revised:  We prove that our formulation (and algorithms) also enjoy significant statistical benefits in terms of sample complexity over standard PCA on account of a ``regularization effect'' , that is formalized using the well-studied spiked covariance model. - Surprisingly, we show that our algorithmic techniques can also be made robust to corruptions in the training data, in addition to yielding representations that are robust at test time!
Edit intent: fluency
Original: Many machine learning systems are vulnerable to small perturbations made to the input either at test time or at training time.
Revised: Many machine learning systems are vulnerable to small perturbations made to inputs either at test time or at training time.
Edit intent: fluency
Original:  This has received much recent interest on the empirical front due to several applications where reliability and security are critical , and the emergence of paradigms such as low precision machine learning.
Revised:  This has received much recent interest on the empirical front due to applications where reliability and security are critical , and the emergence of paradigms such as low precision machine learning.
Edit intent: clarity
Original:  Howeverour theoretical understanding of the design of adversarially robust algorithms for the above settings is limited.
Revised:  Howeverour theoretical understanding of algorithms that are robust to adversarial perturbations is limited.
Edit intent: clarity
Original:  We formulate a natural robust variant of PCA , where the goal is to find a low dimensional subspace to represent the given data with minimum projection error, and that is in addition robust to small perturbations measured in \ell_q norm (say q=\infty).
Revised:  We formulate a natural robust variant of PCA where the goal is to find a low dimensional subspace to represent the given data with minimum projection error, and that is in addition robust to small perturbations measured in \ell_q norm (say q=\infty).
Edit intent: fluency
Original:  We formulate a natural robust variant of PCA , where the goal is to find a low dimensional subspace to represent the given data with minimum projection error, and that is in addition robust to small perturbations measured in \ell_q norm (say q=\infty).
Revised:  We formulate a natural robust variant of PCA , where the goal is to find a low dimensional subspace to represent the given data with minimum projection error, that is in addition robust to small perturbations measured in \ell_q norm (say q=\infty).
Edit intent: coherence
Original:  We show various algorithmic and statistical resultsincluding: - Polynomial time algorithm that is constant factor competitive in the worst-case , with respect to the best subspace both in terms of the projection error and the robustness criterion.
Revised:  We show the following results: -Polynomial time algorithm that is constant factor competitive in the worst-case , with respect to the best subspace both in terms of the projection error and the robustness criterion.
Edit intent: clarity
Original:  We show various algorithmic and statistical resultsincluding: - Polynomial time algorithm that is constant factor competitive in the worst-case , with respect to the best subspace both in terms of the projection error and the robustness criterion.
Revised:  We show various algorithmic and statistical resultsincluding: - Polynomial time algorithm that is constant factor competitive in the worst-case with respect to the best subspace both in terms of the projection error and the robustness criterion.
Edit intent: fluency
Original:  We show various algorithmic and statistical resultsincluding: - Polynomial time algorithm that is constant factor competitive in the worst-case , with respect to the best subspace both in terms of the projection error and the robustness criterion.
Revised:  We show various algorithmic and statistical resultsincluding: - Polynomial time algorithm that is constant factor competitive in the worst-case , with respect to the best subspace , in terms of the projection error and the robustness criterion.
Edit intent: coherence
Original:  We also show that our algorithmic techniques can  be made robust to corruptions in the training data as well , in addition to yielding representations that are robust  at test time. -
Revised:  -We show that our algorithmic techniques can be made robust to corruptions in the training data as well , in addition to yielding representations that are robust at test time. -
Edit intent: clarity
Original:  We also show that our algorithmic techniques can  be made robust to corruptions in the training data as well , in addition to yielding representations that are robust  at test time. -
Revised:  We also show that our algorithmic techniques can also be made robust to corruptions in the training data as well , in addition to yielding representations that are robust at test time. -
Edit intent: fluency
Original:  In this work, we extend this existing notion of taxis to rotating environments and introduce the idea of " roto-taxis " to bio-locomotion.
Revised:  In this work, we extend this existing notion of taxis to rotating environments and introduce the idea of roto-taxis " to bio-locomotion.
Edit intent: fluency
Original:  In this work, we extend this existing notion of taxis to rotating environments and introduce the idea of " roto-taxis " to bio-locomotion.
Revised:  In this work, we extend this existing notion of taxis to rotating environments and introduce the idea of " roto-taxis to bio-locomotion.
Edit intent: fluency
Original:  In this work, we propose MSTDP, a new framework that uses only STDP rules for supervised and unsupervised learning  .
Revised:  In this work, we propose a new framework that uses only STDP rules for supervised and unsupervised learning .
Edit intent: clarity
Original: According to  features of drug addiction, this paper constructs a SEIR-based SUC model to describe and predict the spread of drug addiction.
Revised: According to the features of drug addiction, this paper constructs a SEIR-based SUC model to describe and predict the spread of drug addiction.
Edit intent: fluency
Original: According to  features of drug addiction, this paper constructs a SEIR-based SUC model to describe and predict the spread of drug addiction.
Revised: According to features of drug addiction, this paper constructs an SEIR-based SUC model to describe and predict the spread of drug addiction.
Edit intent: fluency
Original:  Predictions are that the number of drug addictions will continue to fluctuate with a reduced amplitude and eventually stabilize.
Revised:  Predictions are that the number of drug addictions will continue to fluctuate with reduced amplitude and eventually stabilize.
Edit intent: fluency
Original:  In other words, the drug transmission in the two states of Ohio and Pennsylvania require awareness.
Revised:  In other words, drug transmission in the two states of Ohio and Pennsylvania require awareness.
Edit intent: fluency
Original:  Hereby the number of drug addictions in KY, OH, and VA is projected to increase in three years.
Revised:  As a result of this, the number of drug addictions in KY, OH, and VA is projected to increase in three years.
Edit intent: coherence
Original:  Based on them, by using  K-means algorithm, 464 counties were categorized into three basket .
Revised:  Based on them, by using the K-means algorithm, 464 counties were categorized into three basket .
Edit intent: fluency
Original:  Based on them, by using  K-means algorithm, 464 counties were categorized into three basket .
Revised:  Based on them, by using K-means algorithm, 464 counties were categorized into three baskets .
Edit intent: fluency
Original:  To combat  opioid crisis, detailed action will be discussed in the sensitivity analysis section.
Revised:  To combat the opioid crisis, detailed action will be discussed in the sensitivity analysis section.
Edit intent: fluency
Original:  After modeling and analytics, innovation is require to control addict and advocate anti-drug news campaigns.
Revised:  After modeling and analytics, innovation is required to control addicts and advocate anti-drug news campaigns.
Edit intent: fluency
Original:  In other words, if such boundary exceeded,  number of drug addictions may rocket and peak in a short period.
Revised:  In other words, if such boundary exceeded, the number of drug addictions may rocket and peak in a short period.
Edit intent: fluency