wanyu/IteraTeR_v2 huggingface dataset:
Original:  From a set of d-dimensional vectors, we obtain a d-dot product graph\dpg by letting each vector %DIFDELCMD < {\bf %%% a \va ^u correspond to a vertex u and by adding an edge between two vertices u and v if and only if their dot product %DIFDELCMD < {\bf %%% a ^{u \cdot }%DIFDELCMD < {\bf %%% a \va \cdot }\va ^{v} \geq t, for some fixed, positive threshold~t.
Revised:  From a set of d-dimensional vectors, we obtain a d-dot product graph\dpg by letting each vector %DIFDELCMD < {\bf %%% \va ^u correspond to a vertex u and by adding an edge between two vertices u and v if and only if their dot product %DIFDELCMD < {\bf %%% a ^{u \cdot }%DIFDELCMD < {\bf %%% a \va \cdot }\va ^{v} \geq t, for some fixed, positive threshold~t.
Edit intent: fluency
Original:  From a set of d-dimensional vectors, we obtain a d-dot product graph\dpg by letting each vector %DIFDELCMD < {\bf %%% a \va ^u correspond to a vertex u and by adding an edge between two vertices u and v if and only if their dot product %DIFDELCMD < {\bf %%% a ^{u \cdot }%DIFDELCMD < {\bf %%% a \va \cdot }\va ^{v} \geq t, for some fixed, positive threshold~t.
Revised:  From a set of d-dimensional vectors, we obtain a d-dot product graph\dpg by letting each vector %DIFDELCMD < {\bf %%% a \va ^u correspond to a vertex u and by adding an edge between two vertices u and v if and only if their dot product %DIFDELCMD < {\bf %%% ^{u \cdot }%DIFDELCMD < {\bf %%% a \va \cdot }\va ^{v} \geq t, for some fixed, positive threshold~t.
Edit intent: fluency
Original:  From a set of d-dimensional vectors, we obtain a d-dot product graph\dpg by letting each vector %DIFDELCMD < {\bf %%% a \va ^u correspond to a vertex u and by adding an edge between two vertices u and v if and only if their dot product %DIFDELCMD < {\bf %%% a ^{u \cdot }%DIFDELCMD < {\bf %%% a \va \cdot }\va ^{v} \geq t, for some fixed, positive threshold~t.
Revised:  From a set of d-dimensional vectors, we obtain a d-dot product graph\dpg by letting each vector %DIFDELCMD < {\bf %%% a \va ^u correspond to a vertex u and by adding an edge between two vertices u and v if and only if their dot product %DIFDELCMD < {\bf %%% a \cdot }%DIFDELCMD < {\bf %%% a \va \cdot }\va ^{v} \geq t, for some fixed, positive threshold~t.
Edit intent: fluency
Original:  From a set of d-dimensional vectors, we obtain a d-dot product graph\dpg by letting each vector %DIFDELCMD < {\bf %%% a \va ^u correspond to a vertex u and by adding an edge between two vertices u and v if and only if their dot product %DIFDELCMD < {\bf %%% a ^{u \cdot }%DIFDELCMD < {\bf %%% a \va \cdot }\va ^{v} \geq t, for some fixed, positive threshold~t.
Revised:  From a set of d-dimensional vectors, we obtain a d-dot product graph\dpg by letting each vector %DIFDELCMD < {\bf %%% a \va ^u correspond to a vertex u and by adding an edge between two vertices u and v if and only if their dot product %DIFDELCMD < {\bf %%% a ^{u \cdot }%DIFDELCMD < {\bf %%% \va \cdot }\va ^{v} \geq t, for some fixed, positive threshold~t.
Edit intent: fluency
Original:  We also find that topic-level taxonomies, created from paper-level clustering, are far more accurate than discipline-level taxonomies based on journal clusters .
Revised:  We also find that discipline-level taxonomies based on journal clusters .
Edit intent: clarity
Original: We show that the internal representations of an image in a deep neural network (DNN) can be manipulated to mimic those of other natural images  with only minor, imperceptible perturbations to the original image.
Revised: We show that the representation of an image in a deep neural network (DNN) can be manipulated to mimic those of other natural images with only minor, imperceptible perturbations to the original image.
Edit intent: clarity
Original: We show that the internal representations of an image in a deep neural network (DNN) can be manipulated to mimic those of other natural images  with only minor, imperceptible perturbations to the original image.
Revised: We show that the internal representations of an image in a deep neural network (DNN) can be manipulated to mimic those of other natural images , with only minor, imperceptible perturbations to the original image.
Edit intent: fluency
Original:  Previous methods for generating adversarial images focused on image manipulation designed to produce erroneous class labels, while here we look at internal layers of the representation.
Revised:  Previous methods for generating adversarial images focused on image perturbations designed to produce erroneous class labels, while here we look at internal layers of the representation.
Edit intent: clarity
Original:  Previous methods for generating adversarial images focused on image manipulation designed to produce erroneous class labels, while here we look at internal layers of the representation.
Revised:  Previous methods for generating adversarial images focused on image manipulation designed to produce erroneous class labels, while we concentrate on the internal layers of the representation.
Edit intent: clarity
Original:  Specifically, while the input image is perceptually similar to an image of one class , its internal representation appear remarkably like that of natural images in a different class .
Revised:  Specifically, while the input image is perceptually similar to one image , its internal representation appear remarkably like that of natural images in a different class .
Edit intent: clarity
Original: Full waveform inversion (FWI) delivers high-resolution images of a subsurface medium model by minimizing iteratively the least-squares misfit between the observed and simulated seismic data.
Revised: Full waveform inversion (FWI) delivers high-resolution images of the subsurface by minimizing iteratively the least-squares misfit between the observed and simulated seismic data.
Edit intent: clarity
Original: Full waveform inversion (FWI) delivers high-resolution images of a subsurface medium model by minimizing iteratively the least-squares misfit between the observed and simulated seismic data.
Revised: Full waveform inversion (FWI) delivers high-resolution images of a subsurface medium model by minimizing iteratively the misfit between the observed and simulated seismic data.
Edit intent: clarity
Original: Full waveform inversion (FWI) delivers high-resolution images of a subsurface medium model by minimizing iteratively the least-squares misfit between the observed and simulated seismic data.
Revised: Full waveform inversion (FWI) delivers high-resolution images of a subsurface medium model by minimizing iteratively the least-squares misfit between the recorded and calculated seismic data.
Edit intent: clarity
Original: The problem of Visual Question Answering (VQA)  requires joint image and language understanding to answer a question about a given photograph.
Revised: We address the problem of Visual Question Answering (VQA) requires joint image and language understanding to answer a question about a given photograph.
Edit intent: clarity
Original: The problem of Visual Question Answering (VQA)  requires joint image and language understanding to answer a question about a given photograph.
Revised: The problem of Visual Question Answering (VQA) , which requires joint image and language understanding to answer a question about a given photograph.
Edit intent: coherence
Original:  In thispaper , we propose a memory network with spatial attention for the VQA task.
Revised:  To remedy this , we propose a memory network with spatial attention for the VQA task.
Edit intent: coherence
Original:  In thispaper , we propose a memory network with spatial attention for the VQA task.
Revised:  In thispaper , we propose a model we call the Spatial Memory Network and apply it to the VQA task.
Edit intent: clarity
Original:  We store neuron activations from different spatial receptive fields in the memory, and use the question to choose relevant regions for computing the answer .
Revised:  We store neuron activations from different spatial regions of the image in its memory, and use the question to choose relevant regions for computing the answer .
Edit intent: clarity
Original:  We store neuron activations from different spatial receptive fields in the memory, and use the question to choose relevant regions for computing the answer .
Revised:  We store neuron activations from different spatial receptive fields in the memory, and uses the question to choose relevant regions for computing the answer .
Edit intent: fluency
Original: We introduce a statistical model for the problem of finding a subgraph with specified topology in an otherwise random graph.
Revised: We consider a statistical model for the problem of finding a subgraph with specified topology in an otherwise random graph.
Edit intent: clarity
Original: We introduce a statistical model for the problem of finding a subgraph with specified topology in an otherwise random graph.
Revised: We introduce a statistical model for the problem of finding subgraphs with specified topology in an otherwise random graph.
Edit intent: fluency
Original:  In these type of networks, small subgraphs with a specific structure have important functional roles .
Revised:  In these types of networks, small subgraphs with a specific structure have important functional roles .
Edit intent: fluency
Original:  We ask whether the resulting graph can be distinguished reliably from a pure Erdos-Renyi random graph, and  present two types of result.
Revised:  We ask whether the resulting graph can be distinguished reliably from a pure Erdos-Renyi random graph, and we present two types of result.
Edit intent: fluency
Original:  First we investigate the question from a purely statistical perspective, and ask whether there is any test that can distinguish between the two graph models.
Revised:  First we investigate the question from a purely statistical perspective, and ask whether there is any test that can distinguish between the two graph models.
Edit intent: fluency
Original:  We provide necessary and sufficient conditions that are essentially tight for subgraphs of size asymptotically smaller than n^{2/5 . Next we study two polynomial-time algorithms for solving the same problem: a spectral algorithm, and a semidefinite programming (SDP) relaxation.
Revised:  We provide necessary and sufficient conditions that are essentially tight for small enough subgraphs . Next we study two polynomial-time algorithms for solving the same problem: a spectral algorithm, and a semidefinite programming (SDP) relaxation.
Edit intent: clarity
Original: Community detection is a central pursuit for understanding the structure and function of biological, social and technological networks, and it is important to understand the fundamental limitations on detectability.
Edit intent: coherence
Original:  Many systems are naturally represented by a multilayer network in which edges exist in multiple layers that encode different--but potentially related--types of interactions . Using random matrix theory for stochastic block models , we analyze detectability limitations for multilayer networks and find that by aggregating together similar layers, it is possible to identify structure that is undetectable in a single layer.
Revised:  Many systems are naturally represented by a multilayer network in which edges exist in multiple layers that encode different--but potentially related--types of interactions . Using random matrix theory , we analyze detectability limitations for multilayer networks and find that by aggregating together similar layers, it is possible to identify structure that is undetectable in a single layer.
Edit intent: clarity
Original:  We explore this phenomenon for several aggregation methods  including summation of the layers' adjacency matrices , for which detectability limit vanishes  with increasing number of layers, L , decaying as O(L^{-1/2 , we find a similar scaling behavior when the summation is thresholded at an optimal value, supporting the common--but not well understood--practice of thresholding data matrices to obtain sparse network representations.
Revised:  We explore this phenomenon for several aggregation methods , including summation of the layers' adjacency matrices , for which detectability limit vanishes with increasing number of layers, L , decaying as O(L^{-1/2 , we find a similar scaling behavior when the summation is thresholded at an optimal value, supporting the common--but not well understood--practice of thresholding data matrices to obtain sparse network representations.
Edit intent: fluency
Original:  We explore this phenomenon for several aggregation methods  including summation of the layers' adjacency matrices , for which detectability limit vanishes  with increasing number of layers, L , decaying as O(L^{-1/2 , we find a similar scaling behavior when the summation is thresholded at an optimal value, supporting the common--but not well understood--practice of thresholding data matrices to obtain sparse network representations.
Revised:  We explore this phenomenon for several aggregation methods including summation of the layers' adjacency matrices , for which detectability limit vanishes as O(L^{-1/2 with increasing number of layers, L , decaying as O(L^{-1/2 , we find a similar scaling behavior when the summation is thresholded at an optimal value, supporting the common--but not well understood--practice of thresholding data matrices to obtain sparse network representations.
Edit intent: fluency
Original:  We explore this phenomenon for several aggregation methods  including summation of the layers' adjacency matrices , for which detectability limit vanishes  with increasing number of layers, L , decaying as O(L^{-1/2 , we find a similar scaling behavior when the summation is thresholded at an optimal value, supporting the common--but not well understood--practice of thresholding data matrices to obtain sparse network representations.
Revised:  We explore this phenomenon for several aggregation methods including summation of the layers' adjacency matrices , for which detectability limit vanishes with increasing number of layers, L . Importantly , we find a similar scaling behavior when the summation is thresholded at an optimal value, supporting the common--but not well understood--practice of thresholding data matrices to obtain sparse network representations.
Edit intent: coherence
Original:  In this paper, we propose an approach for combining the power of high-level spatio-temporal graphs and sequence learning success of Recurrent Neural Networks ~ (RNNs).
Revised:  In this paper, we propose an approach for combining the power of high-level spatio-temporal graphs and sequence learning success of Recurrent Neural Networks (RNNs).
Edit intent: fluency
Original:  Spatio-temporal graphs are a popular flexible tool for imposing such high-level intuitions in the formulation of real world problems.
Revised:  Spatio-temporal graphs are a popular tool for imposing such high-level intuitions in the formulation of real world problems.
Edit intent: clarity
Original:  In this paper, we propose an approach for combining the power of high-level spatio-temporal graphs and sequence learning success of Recurrent Neural Networks  (RNNs).
Revised:  In this paper, we propose an approach for combining the power of high-level spatio-temporal graphs and sequence learning success of Recurrent Neural Networks ~ (RNNs).
Edit intent: fluency
Original:  We expect this method to empower a new convenient approach to problem formulation through high-level spatio-temporal graphs and Recurrent Neural Networks , and be of broad interest to the community .
Revised:  We expect this method to empower new approaches to problem formulation through high-level spatio-temporal graphs and Recurrent Neural Networks , and be of broad interest to the community .
Edit intent: clarity
Original:  We expect this method to empower a new convenient approach to problem formulation through high-level spatio-temporal graphs and Recurrent Neural Networks , and be of broad interest to the community .
Revised:  We expect this method to empower a new convenient approach to problem formulation through high-level spatio-temporal graphs and Recurrent Neural Networks .
Edit intent: coherence
Original:  Such human-machine networks (HMNs) are embedded in the daily lives of people, both or personal and professional use.
Revised:  Such human-machine networks (HMNs) are embedded in the daily lives of people, both for personal and professional use.
Edit intent: fluency
Original: In this letter, we study the memristor-based oscillators, and propose for the first time the method of calculating its PPV (Perturbation Projection Vector) .
Revised: In this letter, we propose for the first time the method of calculating its PPV (Perturbation Projection Vector) .
Edit intent: coherence
Original: In this letter, we study the memristor-based oscillators, and propose for the first time the method of calculating its PPV (Perturbation Projection Vector) .
Revised: In this letter, we study the memristor-based oscillators, and propose for the first time a method of abstracting the PPV (Perturbation Projection Vector) .
Edit intent: clarity
Original:  Thanks to its high efficiency , the PPV of the memristor-based oscillator can serve as the basis for the fast simulation of phase noise and large scale oscillatory neural networks.
Revised:  Thanks to its high efficiency , the PPV modelling is highly efficient to describe the phase dynamics due to the oscillator coupling, and will be very suitable for the fast simulation of phase noise and large scale oscillatory neural networks.
Edit intent: clarity
Original:  Thanks to its high efficiency , the PPV of the memristor-based oscillator can serve as the basis for the fast simulation of phase noise and large scale oscillatory neural networks.
Revised:  Thanks to its high efficiency , the PPV of the memristor-based oscillator can serve as the basis for the fast simulation of large scale oscillatory neural networks.
Edit intent: clarity
Original: The \Pi -Vertex Deletion problem has as input an undirected graph G=(V,E) and an integer k and asks whether there is a set of at most k vertices that can be deleted  such that the resulting graph is a member of the graph class \Pi.
Revised: For a graph class \Pi, the \Pi -Vertex Deletion problem has as input an undirected graph G=(V,E) and an integer k and asks whether there is a set of at most k vertices that can be deleted such that the resulting graph is a member of the graph class \Pi.
Edit intent: clarity
Original: The \Pi -Vertex Deletion problem has as input an undirected graph G=(V,E) and an integer k and asks whether there is a set of at most k vertices that can be deleted  such that the resulting graph is a member of the graph class \Pi.
Revised: The \Pi -Vertex Deletion problem has as input an undirected graph G=(V,E) and an integer k and asks whether there is a set of at most k vertices that can be deleted such that the resulting graph is a member of \Pi.
Edit intent: clarity
Original:  We show that \Pi-Vertex Deletion does not admit a 2^{o(n)}-time algorithm where n is the number of vertices in G. We also obtain a dichotomy for  the number m of edges in the input graph: If \Pi contains all independent sets, then there is no 2^{o(  m)}-time algorithm for \Pi-Vertex Deletion.
Revised:  We show that \Pi-Vertex Deletion does not admit a 2^{o(n)}-time algorithm where n is the number of vertices in G. We also obtain a dichotomy for the number m of edges in the input graph: On the one hand, if \Pi contains all independent sets, then there is no 2^{o( m)}-time algorithm for \Pi-Vertex Deletion.
Edit intent: clarity
Original:  If there is a fixed independent set that is not contained in \Pi  , then \Pi-Vertex Deletion can be solved in 2^{ o ( m ) %DIFDELCMD < \MBLOCKRIGHTBRACE %%% time.
Revised:  On the other hand, if there is a fixed independent set that is not contained in \Pi , then \Pi-Vertex Deletion can be solved in 2^{ o ( m ) %DIFDELCMD < \MBLOCKRIGHTBRACE %%% time.
Edit intent: coherence
Original:  If there is a fixed independent set that is not contained in \Pi  , then \Pi-Vertex Deletion can be solved in 2^{ o ( m ) %DIFDELCMD < \MBLOCKRIGHTBRACE %%% time.
Revised:  If there is a fixed independent set that is not contained in \Pi , then \Pi-Vertex Deletion can be solved in 2^{ O ( m ) %DIFDELCMD < \MBLOCKRIGHTBRACE %%% time.
Edit intent: fluency
Original:  Finally, we consider the special case that Gis planar and obtain that \Pi-Vertex Deletion cannot be solved in 2^{o(n)} time for all hereditary \Pi containing and excluding infinitely many planar graphs  .
Revised:  Finally, we consider the special case that Gis planar and obtain that \Pi-Vertex Deletion cannot be solved in 2^{o(n)} time if G is planar and \Pi is hereditary and contains and excludes infinitely many planar graphs .
Edit intent: clarity
Original:  One of the main goals of denoising algorithms is to eliminate such noise so that the curated data lie within a bounded Hausdorff distance of K. Deconvolution and thresholding , the two prevailing techniques for this problem suffer from the difficulty that they burden the user with setting several parameters and/or choosing an appropriate noise model while guaranteeing only asymptotic convergence.
Revised:  One of the main goals of denoising algorithms is to eliminate such noise so that the curated data lie within a bounded Hausdorff distance of K. Popular denoising approaches such as deconvolution and thresholding often require the user to set several parameters and/or choosing an appropriate noise model while guaranteeing only asymptotic convergence.
Edit intent: clarity
Original:  One of the main goals of denoising algorithms is to eliminate such noise so that the curated data lie within a bounded Hausdorff distance of K. Deconvolution and thresholding , the two prevailing techniques for this problem suffer from the difficulty that they burden the user with setting several parameters and/or choosing an appropriate noise model while guaranteeing only asymptotic convergence.
Revised:  One of the main goals of denoising algorithms is to eliminate such noise so that the curated data lie within a bounded Hausdorff distance of K. Deconvolution and thresholding , the two prevailing techniques for this problem suffer from the difficulty that they burden the user with setting several parameters and/or to choose an appropriate noise model while guaranteeing only asymptotic convergence.
Edit intent: fluency
Original:  Our goal is to lighten this burden as much as possible while ensuring the theoretical guarantees in all cases.
Revised:  Our goal is to lighten this burden as much as possible while ensuring theoretical guarantees in all cases.
Edit intent: fluency
Original:  We propose SPIDER, an OpenFlow-like pipeline architecture that provides i) a detection mechanism based on switches' periodic link probing and ii) fast reroute of traffic flows even in case of distant failures, regardless of controller availability.
Revised:  We propose SPIDER, an OpenFlow-like pipeline design that provides i) a detection mechanism based on switches' periodic link probing and ii) fast reroute of traffic flows even in case of distant failures, regardless of controller availability.
Edit intent: clarity
Original:  Supervised domain adaptation methods have been proposed for the case when the target data have labels, including some that perform very well despite being ``frustratingly easy'' to implement.
Revised:  Supervised domain adaptation methods have been proposed for the case when the target data have labels, including some that perform very well despite being "frustratingly easy" to implement.
Edit intent: fluency
Original: Recurrent Neural Networks (RNNs) have the ability to retain memory and learn data sequences , and are a recent breakthrough of machine learning .
Revised: Recurrent Neural Networks (RNNs) have the ability to retain memory and learn data sequences .
Edit intent: coherence
Original:  It is supplied with differential equations obtained from three-dimensional model by the dimension-reduction procedure .
Revised:  It is supplied with differential equations obtained by the dimension-reduction procedure .
Edit intent: clarity