wanyu/IteraTeR_v2 huggingface dataset:
Original:  For researchers, it is essential to utilize data from these sources to develop and design systems that not only provide solutions to computational politics but also help other businesses, such as marketers , to increase the users\textquotesingle participation and interaction .
Revised:  For researchers, it is essential to utilize data from these sources to develop and design systems that not only provide solutions to computational politics but also help other businesses, such as marketers to increase users, participation and interactions .
Edit intent: coherence
Original:  In this survey, we attempt to categorize main areas in computational politics and summarize the prominent studies at one place to better understand computational politics across different and multidimensional platforms. e.g., online social networks, online forums, and political debates.
Revised:  In this survey, we attempt to categorize main areas in computational politics and summarize the prominent studies in one place to better understand computational politics across different and multidimensional platforms. e.g., online social networks, online forums, and political debates.
Edit intent: fluency
Original: Disorder is a characteristic of real social networks generated by heterogeneity in person-to-person interactions .
Revised: In real social networks generated by heterogeneity in person-to-person interactions .
Edit intent: clarity
Original: Disorder is a characteristic of real social networks generated by heterogeneity in person-to-person interactions .
Revised: Disorder is a characteristic of real social networks , person-to-person interactions .
Edit intent: clarity
Original:  Using a comprehensive spectrum of cognitive outcomes associated to Alzheimer\'s disease, we identify and characterize functional networks associated to specific cognitive deficits exhibited in Alzheimer\'s disease.
Revised:  Using a comprehensive spectrum of cognitive outcomes associated to Alzheimer's disease, we identify and characterize functional networks associated to specific cognitive deficits exhibited in Alzheimer\'s disease.
Edit intent: fluency
Original:  Using a comprehensive spectrum of cognitive outcomes associated to Alzheimer\'s disease, we identify and characterize functional networks associated to specific cognitive deficits exhibited in Alzheimer\'s disease.
Revised:  Using a comprehensive spectrum of cognitive outcomes associated to Alzheimer\'s disease, we identify and characterize functional networks associated to specific cognitive deficits exhibited in Alzheimer's disease.
Edit intent: fluency
Original:  In a mixed-criticality system (MCS), the over-provisioning of CPU time to accommodate the WCETs of highly critical tasks can lead to degraded service for less critical tasks.
Revised:  In a mixed-criticality system (MCS), the over-provisioning of CPU time to accommodate the WCETs of highly critical tasks may lead to degraded service for less critical tasks.
Edit intent: clarity
Original:   Using LITMUS-RT to implement both AMC and AMC-PAStime, we observe that AMC-PAStime significantly improves the utilization of  low-criticality tasks while guaranteeing service to high-criticality tasks.
Revised:  Using LITMUS-RT to implement both AMC and AMC-PAStime, we observe that AMC-PAStime significantly improves the utilization of the low-criticality tasks while guaranteeing service to high-criticality tasks.
Edit intent: coherence
Original: We present a new algorithm for the graph isomorphism problem which solves an equivalent maximum clique formulation via semidefinite programming.
Revised: It is well-known that the graph isomorphism problem which solves an equivalent maximum clique formulation via semidefinite programming.
Edit intent: coherence
Original:  Following previous results, we show that graph isomorphism is equivalent to determining whether an auxiliary graph structure has a clique of specific order.
Revised:  Following previous results, we show that graph isomorphism is equivalent to determining whether an auxiliary graph structure contains a clique of specific order.
Edit intent: clarity
Original:  We show that the problem of determining whether that graph contains such a clique can be formulated as a semidefinite programming problem, and thus can be (almost exactly) solved in polynomial time  .
Revised:  We show that the problem of determining whether that graph contains such a clique can be formulated as a semidefinite programming problem, and can thus be (almost exactly) solved in polynomial time .
Edit intent: fluency
Original:  Based upon challenging speech and image datasets including TI46, N-TIDIGITS, and Fashion-MNIST  , ST-RSBP is able to train RSNNs with an accuracy surpassing that of the current state-of-art SNN BP algorithms and conventional non-spiking deep learning models.
Revised:  Based upon challenging speech and image datasets including TI46, N-TIDIGITS, Fashion-MNIST , ST-RSBP is able to train RSNNs with an accuracy surpassing that of the current state-of-art SNN BP algorithms and conventional non-spiking deep learning models.
Edit intent: fluency
Original:  In this paper, we propose a hybrid architecture, incorporating densely connected convolutional networks (DenseNet) and gated recurrent units (GRU), to enable dual-level temporal context aggregation.
Revised:  In this paper, we propose a densely connected convolutional networks (DenseNet) and gated recurrent units (GRU), to enable dual-level temporal context aggregation.
Edit intent: clarity
Original:  Due to the dense connectivity pattern and a cross-component identical shortcut, the proposed model consistently outperforms competing convolutional baselines with an average STOI improvement of 0.23 and PESQ of 1.38 at three SNR levels.
Revised:  With the dense connectivity pattern and a cross-component identical shortcut, the proposed model consistently outperforms competing convolutional baselines with an average STOI improvement of 0.23 and PESQ of 1.38 at three SNR levels.
Edit intent: clarity
Original:  Due to the dense connectivity pattern and a cross-component identical shortcut, the proposed model consistently outperforms competing convolutional baselines with an average STOI improvement of 0.23 and PESQ of 1.38 at three SNR levels.
Revised:  Due to the dense connectivity and cross-component identical shortcut, the proposed model consistently outperforms competing convolutional baselines with an average STOI improvement of 0.23 and PESQ of 1.38 at three SNR levels.
Edit intent: clarity
Original:  Due to the dense connectivity pattern and a cross-component identical shortcut, the proposed model consistently outperforms competing convolutional baselines with an average STOI improvement of 0.23 and PESQ of 1.38 at three SNR levels.
Revised:  Due to the dense connectivity pattern and a cross-component identical shortcut, DCCRN consistently outperforms competing convolutional baselines with an average STOI improvement of 0.23 and PESQ of 1.38 at three SNR levels.
Edit intent: clarity
Original:  In addition, the proposed hybrid architecture is computationally efficient with  1.38 million  parameters.
Revised:  The proposed method is computationally efficient with 1.38 million parameters.
Edit intent: clarity
Original:  In addition, the proposed hybrid architecture is computationally efficient with  1.38 million  parameters.
Revised:  In addition, the proposed hybrid architecture is computationally efficient with only 1.38 million parameters.
Edit intent: clarity
Original: We introduce a deep learning model to learn continuous-time dynamics on complex networks and infer the semantic labels of nodes in the network at terminal time.
Revised: Learning continuous-time dynamics on complex networks and infer the semantic labels of nodes in the network at terminal time.
Edit intent: clarity
Original:  We solve the problem by a differential deep learning framework: as for the forward process of the system, rather than forwarding through a discrete number of hidden layers , we integrate the ordinary differential equation systems on graphs over continuous time ;
Revised:  We solve the problem by a differential deep learning framework: as for the forward process of the system, rather than forwarding through a discrete number of neural layers in the forward process , we integrate the ordinary differential equation systems on graphs over continuous time ;
Edit intent: clarity
Original:  The promising experimental results demonstrate our model's capability of jointly capturing the structure , dynamics and semantics of complex systems  .
Revised:  The promising experimental results demonstrate our model's capability of jointly capturing the structure and dynamics of complex systems .
Edit intent: clarity
Original:  Our findings indicate that memory limitations have permeated grammatical descriptions, suggesting that it may be theoretically impossible to capture human linguistic productivity independent of  cognitive constraints.
Revised:  Our findings indicate that memory limitations have permeated grammatical descriptions, suggesting that it may be impossible to build a parsimonious theory of human linguistic productivity independent of cognitive constraints.
Edit intent: clarity
Original:  We seek a minimizer of this cost subject to a set of affine equality constraints, and a set of affine inequality constrains specifying the bounds on the decision variables if such bounds exist.
Revised:  We seek a minimizer of this cost subject to a set of affine equality constraints, and a set of affine inequality constraints specifying the bounds on the decision variables if such bounds exist.
Edit intent: fluency
Original:   Our proposed distributed algorithm is a novel continuous-time algorithm that is linked to the augmented Lagrangian approach.
Revised:  Our proposed distributed solution is a novel continuous-time algorithm that is linked to the augmented Lagrangian approach.
Edit intent: clarity
Original:  We use an \epsilon -exact penalty function to address the inequality constraints, and drive an explicit lower bound on the penalty function weight to guarantee convergence to \epsilon -neighborhood of the global minimum value of the cost.
Revised:  We use an Moreover, we use an \epsilon -exact penalty function to address the inequality constraints, and drive an explicit lower bound on the penalty function weight to guarantee convergence to \epsilon -neighborhood of the global minimum value of the cost.
Edit intent: coherence
Original:  We use an \epsilon -exact penalty function to address the inequality constraints, and drive an explicit lower bound on the penalty function weight to guarantee convergence to \epsilon -neighborhood of the global minimum value of the cost.
Revised:  We use an \epsilon -exact penalty function to address the inequality constraints, and derive an explicit lower bound on the penalty function weight to guarantee convergence to \epsilon -neighborhood of the global minimum value of the cost.
Edit intent: clarity
Original:  We use an \epsilon -exact penalty function to address the inequality constraints, and drive an explicit lower bound on the penalty function weight to guarantee convergence to \epsilon -neighborhood of the global minimum value of the cost.
Revised:  We use an \epsilon -exact penalty function to address the inequality constraints, and drive an explicit lower bound on the penalty function weight to guarantee convergence to \epsilon -neighborhood of the global minimum value of the cost.
Edit intent: fluency
Original:  The total cost in this optimization problem is the sum of the local convex cost of the subagents of each cluster.
Revised:  The total cost in this optimization problem is the sum of the local convex costs of the subagents of each cluster.
Edit intent: fluency
Original:  Moreover, we use an \epsilon-exact penalty function to address the inequality constraints , and derive an explicit lower bound on the penalty function weight to guarantee convergence to \epsilon-neighborhood of the global minimum value of the cost.
Revised:  Moreover, we use an \epsilon-exact penalty function to address the inequality constraints and derive an explicit lower bound on the penalty function weight to guarantee convergence to \epsilon-neighborhood of the global minimum value of the cost.
Edit intent: fluency
Original:  We demonstrate our resultsvia an optimal resource allocation problem for power generators, and an optimal multi-sensor deployment problem .
Revised:  A numerical example demonstrates our results .
Edit intent: clarity
Original: To respond to the need of efficient training and inference of deep neural networks, a pletora of domain-specific hardware architectures have been introduced, such as Google Tensor Processing Units and NVIDIA Tensor Cores.
Revised: To respond to the need of efficient training and inference of deep neural networks, a plethora of domain-specific hardware architectures have been introduced, such as Google Tensor Processing Units and NVIDIA Tensor Cores.
Edit intent: fluency
Original:  In order to broad the class of algorithms that exploit these systems, we propose a computational model, named  TCU model, that captures the ability to natively multiply small matrices.
Revised:  In order to broaden the class of algorithms that exploit these systems, we propose a computational model, named TCU model, that captures the ability to natively multiply small matrices.
Edit intent: fluency
Original:  In order to broad the class of algorithms that exploit these systems, we propose a computational model, named  TCU model, that captures the ability to natively multiply small matrices.
Revised:  In order to broad the class of algorithms that exploit these systems, we propose a computational model, named the TCU model, that captures the ability to natively multiply small matrices.
Edit intent: fluency
Original:  We then use the TCU model for designing fast algorithms for linear algebra problems, including  dense and sparse matrix multiplication, FFT , integer multiplication, and polynomial evaluation.
Revised:  We then use the TCU model for designing fast algorithms for several problems, including dense and sparse matrix multiplication, FFT , integer multiplication, and polynomial evaluation.
Edit intent: clarity
Original:  We then use the TCU model for designing fast algorithms for linear algebra problems, including  dense and sparse matrix multiplication, FFT , integer multiplication, and polynomial evaluation.
Revised:  We then use the TCU model for designing fast algorithms for linear algebra problems, including matrix operations ( dense and sparse matrix multiplication, FFT , integer multiplication, and polynomial evaluation.
Edit intent: clarity
Original:  We discuss the performance of the algorithm on datasets that are expected to be classified successfully by those algorithms , arguing that on those cases we can give strong guarantees on the runtime.
Revised:  We discuss the performance of the algorithm on a dataset that is expected to be classified successfully by those algorithms , arguing that on those cases we can give strong guarantees on the runtime.
Edit intent: fluency
Original:  We show that in this case, the resulting programs have non-positive curvature , a notion that we introduce and study here for precubical sets, and can be thought of as an algebraic analogue of the well-known one for metric spaces.
Revised:  We show that in this case, the resulting programs have non-positive curvature , a notion that we introduce and study here for precubical sets, and can be thought of as an algebraic analogue of the well-known one for metric spaces.
Edit intent: fluency
Original:  We present a quantum algorithm for second order cone programs (SOCPs ) based on a quantum variant of the interior point method.
Revised:  We present a quantum algorithm for SOCPs based on a quantum variant of the interior point method.
Edit intent: clarity
Original:  The best known classical algorithms for such instances have complexity \Omega (n O \left(  ^{  \right) 3 %DIFDELCMD < \MBLOCKRIGHTBRACE%%% ) .
Revised:  The best known classical algorithms for such instances have complexity \Omega (n O \left( n ^{ \right) 3 %DIFDELCMD < \MBLOCKRIGHTBRACE%%% ) .
Edit intent: fluency
Original: Second order cone programs (SOCPs) are a class of structured convex optimization problems that generalize linear programs.
Edit intent: coherence
Original:  We provide experimental evidence that the quantum algorithm achieves an asymptotic speedup over classical SVM algorithms with a running time (n^{2.557) for random SVM instances.
Revised:  We provide experimental evidence that (n^{2.557) for random SVM instances.
Edit intent: clarity
Original:  Despite new results on computing the spectrum, there has been no general method able to compute spectral measures (as given by the classical spectral theorem) of infinite-dimensional normal operators.
Revised:  Despite new results on computing spectra, there remains no general method able to compute spectral measures (as given by the classical spectral theorem) of infinite-dimensional normal operators.
Edit intent: clarity
Original:  Given a matrix representation , we show that if each matrix column decays at infinity at a known asymptotic rate, then it is possible to compute spectral measures of self-adjoint and unitary linear operators on separable Hilbert spaces.
Revised:  Given a matrix representation of a self-adjoint or unitary operator, such that each column decays at infinity at a known asymptotic rate, then it is possible to compute spectral measures of self-adjoint and unitary linear operators on separable Hilbert spaces.
Edit intent: clarity
Original:  Given a matrix representation , we show that if each matrix column decays at infinity at a known asymptotic rate, then it is possible to compute spectral measures of self-adjoint and unitary linear operators on separable Hilbert spaces.
Revised:  Given a matrix representation , we show that if each matrix column decays at infinity at a known asymptotic rate, we show how to compute spectral measures of self-adjoint and unitary linear operators on separable Hilbert spaces.
Edit intent: clarity
Original:  The central ingredient of the new algorithm is the computation of the resolvent operator with error control . Computational spectral problems in infinite dimensions have led to the SCI hierarchy, which classifies the difficulty of a problem through the number of limits needed to numerically compute the solution .
Revised:  The central ingredient of the new algorithm is the computation of the resolvent operator with error control . Computational spectral problems in infinite dimensions have led to the SCI hierarchy, which classifies the difficulty of computational problems .
Edit intent: clarity
Original:  We classify the computation of measures, measure decompositions, types of spectra (pure point, absolutely continuous, singular continuous), functional calculusand Radon--Nikodym derivatives in the SCI hierarchy for such operators .
Revised:  We classify the computation of measures, measure decompositions, types of spectra , functional calculus, and Radon-Nikodym derivatives in the SCI hierarchy for such operators .
Edit intent: coherence
Original:  We classify the computation of measures, measure decompositions, types of spectra (pure point, absolutely continuous, singular continuous), functional calculusand Radon--Nikodym derivatives in the SCI hierarchy for such operators .
Revised:  We classify the computation of measures, measure decompositions, types of spectra (pure point, absolutely continuous, singular continuous), functional calculusand Radon--Nikodym derivatives in the SCI hierarchy .
Edit intent: clarity
Original:  The new algorithms are demonstrated to be efficient and practical on examples taken from orthogonal polynomials on the real line and the unit circle, and are also applied to evolution equations on a two-dimensional quasicrystal model .
Revised:  The new algorithms are demonstrated to be efficient on examples taken from orthogonal polynomials on the real line and the unit circle, and are also applied to evolution equations on a two-dimensional quasicrystal model .
Edit intent: clarity
Original:  The new algorithms are demonstrated to be efficient and practical on examples taken from orthogonal polynomials on the real line and the unit circle, and are also applied to evolution equations on a two-dimensional quasicrystal model .
Revised:  The new algorithms are demonstrated to be efficient and practical on examples taken from orthogonal polynomials on the real line and unit circle, and are also applied to evolution equations on a two-dimensional quasicrystal model .
Edit intent: fluency
Original:  The new algorithms are demonstrated to be efficient and practical on examples taken from orthogonal polynomials on the real line and the unit circle, and are also applied to evolution equations on a two-dimensional quasicrystal model .
Revised:  The new algorithms are demonstrated to be efficient and practical on examples taken from orthogonal polynomials on the real line and the unit circle, and are applied to evolution equations on a two-dimensional quasicrystal model .
Edit intent: clarity
Original:  The new algorithms are demonstrated to be efficient and practical on examples taken from orthogonal polynomials on the real line and the unit circle, and are also applied to evolution equations on a two-dimensional quasicrystal model .
Revised:  The new algorithms are demonstrated to be efficient and practical on examples taken from orthogonal polynomials on the real line and the unit circle, and are also applied to evolution equations on a two-dimensional quasicrystal .
Edit intent: clarity
Original: Spectral measures arise in numerous applications such as quantum mechanics, signal processing, resonance phenomena , and fluid stability analysis . Similarly, spectral decompositions (pure point, absolutely continuous and singular continuous) often characterise relevant physical properties such as long-time dynamics of quantum systems.
Revised: Spectral measures arise in numerous applications such as quantum mechanics, signal processing, resonances , and fluid stability analysis . Similarly, spectral decompositions (pure point, absolutely continuous and singular continuous) often characterise relevant physical properties such as long-time dynamics of quantum systems.
Edit intent: clarity
Original: Spectral measures arise in numerous applications such as quantum mechanics, signal processing, resonance phenomena , and fluid stability analysis . Similarly, spectral decompositions (pure point, absolutely continuous and singular continuous) often characterise relevant physical properties such as long-time dynamics of quantum systems.
Revised: Spectral measures arise in numerous applications such as quantum mechanics, signal processing, resonance phenomena , and fluid stability . Similarly, spectral decompositions (pure point, absolutely continuous and singular continuous) often characterise relevant physical properties such as long-time dynamics of quantum systems.
Edit intent: clarity
Original:  Despite new results on computing spectra, there remains no general method able to compute spectral measures or  decompositions of infinite-dimensional normal operators.
Revised:  Despite new results on computing spectra, there remains no general method able to compute spectral measures or spectral decompositions of infinite-dimensional normal operators.
Edit intent: fluency
Original:  Previous efforts have focused on specific examples where analytical formulae are available (or perturbations thereof) , and hence this computational problem is predominantly open.
Revised:  Previous efforts focus on specific examples where analytical formulae are available (or perturbations thereof) , and hence this computational problem is predominantly open.
Edit intent: coherence