wanyu/IteraTeR_v2 huggingface dataset:
Original:  To further lower the memory consumption from gradient optimization on faces, we also propose a formulation that optimizes the projected gradients on edges, further reducing the memory footprint by about 50 %DIFDELCMD < \\%%% %DIF < . Our approach is  trivially parallelizable, with a low memory footprint that grows linearly with respect to the model size.
Revised:  To further lower the memory consumption from gradient optimization on faces, we also propose a formulation that optimizes the projected gradients on edges, further reducing the memory footprint by about 50 %DIFDELCMD < \\%%% %DIF < . Our approach is \%. Our approach is trivially parallelizable, with a low memory footprint that grows linearly with respect to the model size.
Edit intent: coherence
Original:  Javelin allows for improved parallel factorization on shared-memory many-core systems , while packaging the coefficient matrix into a format that allows for high performance sparse matrix-vector multiplication and sparse triangular solves with minimal overheads.
Revised:  Javelin allows for improved parallel factorization on shared-memory many-core systems by packaging the coefficient matrix into a format that allows for high performance sparse matrix-vector multiplication and sparse triangular solves with minimal overheads.
Edit intent: coherence
Original:   Using these changes, traditional fill-in and drop tolerance methods can be used, while still being able to have observed speedups of up to ~42x on 68 Intel Knights Landing cores and ~ 12 x on 14 Intel Haswell cores.
Revised:  Using these changes, traditional fill-in and drop tolerance methods can be used, while still being able to have observed speedups of up to ~42x on 68 Intel Knights Landing cores and ~ 12x on 14 Intel Haswell cores.
Edit intent: fluency
Original: We consider a multiple-unicast network flow problem of maximizing aggregate user utilities over a multi-hop network, subject to link capacity constraints, maximum end-to-end delay constraints, and user throughput requirements.
Revised: We consider the problem of maximizing aggregate user utilities over a multi-hop network, subject to link capacity constraints, maximum end-to-end delay constraints, and user throughput requirements.
Edit intent: clarity
Original:  The problem is important for supporting real-time multimedia or Cyber-Physical System (CPS) traffic, and it is uniquely challenging due to the need of simultaneously considering maximum delay constraints and throughput requirements.
Revised:  The problem is important for supporting real-time multimedia traffic, and it is uniquely challenging due to the need of simultaneously considering maximum delay constraints and throughput requirements.
Edit intent: clarity
Original:  The problem is important for supporting real-time multimedia or Cyber-Physical System (CPS) traffic, and it is uniquely challenging due to the need of simultaneously considering maximum delay constraints and throughput requirements.
Revised:  The problem is important for supporting real-time multimedia or Cyber-Physical System (CPS) traffic, and is uniquely challenging due to the need of simultaneously considering maximum delay constraints and throughput requirements.
Edit intent: fluency
Original:  We then develop the first set of polynomial-time algorithms in the literature, to our best knowledge, named PASS, to obtain solutions that (i) achieve constant or problem-dependent approximation ratios, at the cost of (ii) violating maximum delay constraints or throughput requirements up to constant or problem-dependent ratios , under certain conditions derived in this paper.
Revised:  We then develop the first polynomial-time algorithms in the literature, to our best knowledge, named PASS, to obtain solutions that (i) achieve constant or problem-dependent approximation ratios, at the cost of (ii) violating maximum delay constraints or throughput requirements up to constant or problem-dependent ratios , under certain conditions derived in this paper.
Edit intent: clarity
Original:  We then develop the first set of polynomial-time algorithms in the literature, to our best knowledge, named PASS, to obtain solutions that (i) achieve constant or problem-dependent approximation ratios, at the cost of (ii) violating maximum delay constraints or throughput requirements up to constant or problem-dependent ratios , under certain conditions derived in this paper.
Revised:  We then develop the first set of polynomial-time algorithms in the literature, to our best knowledge, named PASS, to obtain solutions that (i) achieve constant or problem-dependent approximation ratios, at the cost of violating maximum delay constraints or throughput requirements up to constant or problem-dependent ratios , under certain conditions derived in this paper.
Edit intent: fluency
Original:  A user's utility is an arbitrary function of the achieved throughput or the experienced maximum delay.
Revised:  A user's utility is a concave function of the achieved throughput or the experienced maximum delay.
Edit intent: clarity
Original:  We first show that it is even NP-complete  (i) to construct a feasible solution  meeting all constraints, or (ii) to obtain an optimal solution that meets relaxed constraints .
Revised:  We first show that it is NP-complete (i) to construct a feasible solution meeting all constraints, or (ii) to obtain an optimal solution that meets relaxed constraints .
Edit intent: clarity
Original:  We then develop the first polynomial-time approximation algorithm named PASS.
Revised:  We then develop a polynomial-time approximation algorithm named PASS.
Edit intent: clarity
Original:  Our algorithm design leverages a novel understanding between non-convex maximum-delay-aware problems and their convex average-delay-aware counterparts, which can be of independent interest and suggest a new avenue for solving maximum-delay-aware network optimization problems.
Revised:  The design of PASS leverages a novel understanding between non-convex maximum-delay-aware problems and their convex average-delay-aware counterparts, which can be of independent interest and suggest a new avenue for solving maximum-delay-aware network optimization problems.
Edit intent: clarity
Original:  Under realistic conditions, theoretically PASS provides constant or problem-dependent approximation ratios, at the cost of violating maximum delay constraints or throughput requirements  up to constant or problem-dependent ratios.
Revised:  Under realistic conditions, PASS achieves constant or problem-dependent approximation ratios, at the cost of violating maximum delay constraints or throughput requirements up to constant or problem-dependent ratios.
Edit intent: clarity
Original:  Under realistic conditions, theoretically PASS provides constant or problem-dependent approximation ratios, at the cost of violating maximum delay constraints or throughput requirements  up to constant or problem-dependent ratios.
Revised:  Under realistic conditions, theoretically PASS provides constant or problem-dependent approximation ratios, at the cost of violating maximum delay constraints or throughput requirements by up to constant or problem-dependent ratios.
Edit intent: clarity
Original:  Compared to existing algorithms and a conceivable baseline, PASS can obtain up to 100\\%DIF < improvement of utilities, meeting throughput requirements but relaxed %DIF > improvement of utilities, by meeting the throughput  maximum delay constraints that are acceptable for practical video conferencing applications.
Revised:  Compared to existing algorithms and a conceivable baseline, PASS obtains up to 100\\%DIF < improvement of utilities, meeting throughput requirements but relaxed %DIF > improvement of utilities, by meeting the throughput maximum delay constraints that are acceptable for practical video conferencing applications.
Edit intent: clarity
Original:  Compared to existing algorithms and a conceivable baseline, PASS can obtain up to 100\\%DIF < improvement of utilities, meeting throughput requirements but relaxed %DIF > improvement of utilities, by meeting the throughput  maximum delay constraints that are acceptable for practical video conferencing applications.
Revised:  Compared to existing algorithms and a conceivable baseline, PASS can obtain up to 100\\%DIF < %DIF > improvement of utilities, by meeting the throughput maximum delay constraints that are acceptable for practical video conferencing applications.
Edit intent: coherence
Original:  Compared to existing algorithms and a conceivable baseline, PASS can obtain up to 100\\%DIF < improvement of utilities, meeting throughput requirements but relaxed %DIF > improvement of utilities, by meeting the throughput  maximum delay constraints that are acceptable for practical video conferencing applications.
Revised:  Compared to existing algorithms and a conceivable baseline, PASS can obtain up to 100\\%DIF < improvement of utilities, meeting throughput requirements but relaxed %DIF > improvement of utilities, by meeting the throughput requirements but relaxing the maximum delay constraints that are acceptable for practical video conferencing applications.
Edit intent: fluency
Original:  This paper proposes a novel  easy-to-implement multi-tasking evolutionary algorithm (MTEA ), which copes well with significantly different optimization tasks by estimating and using the bias among them.
Revised:  This paper proposes a novel and easy-to-implement multi-tasking evolutionary algorithm (MTEA ), which copes well with significantly different optimization tasks by estimating and using the bias among them.
Edit intent: coherence
Original:  Comparative studies with eight state-of-the-art single- and multi-task approaches in the literature on nine benchmarks demonstrated that on average the MTEA outperformed all of them, and has lower computational cost than six of them.
Revised:  Comparative studies with eight state-of-the-art single- and multi-task approaches in the literature on nine benchmarks demonstrated that on average the MTGA outperformed all of them, and has lower computational cost than six of them.
Edit intent: clarity
Original:  Comparative studies with eight state-of-the-art single- and multi-task approaches in the literature on nine benchmarks demonstrated that on average the MTEA outperformed all of them, and has lower computational cost than six of them.
Revised:  Comparative studies with eight state-of-the-art single- and multi-task approaches in the literature on nine benchmarks demonstrated that on average the MTEA outperformed all of them, and had lower computational cost than six of them.
Edit intent: fluency
Original:  Data wrangling is widely acknowledged to be a critical part of the data analysis pipeline .
Revised: Networks are a natural way of thinking about many datasets. The data on which a network is based, however, is rarely collected in a form that suits the analysis process, making it necessary to create and reshape networks. Data wrangling is widely acknowledged to be a critical part of the data analysis pipeline .
Edit intent: coherence
Original:  Designed for wrangling, rather than analysis, Origraph can be used to load data in many forms, wrangle and transform the network, and export it in formats compatible with common network visualization tools .
Revised:  Origraph provides views to investigate the network model, a sample of the network, and export it in formats compatible with common network visualization tools .
Edit intent: clarity
Original:  This paper gives a first set of results proving that  deep learning algorithms fail at learning certain efficiently learnable functions.
Revised:  This paper gives a first set of results proving that certain deep learning algorithms fail at learning certain efficiently learnable functions.
Edit intent: fluency
Original:  Parity functions form the running example of our results and the paper puts forward a notion of low cross-predictability that defines a more general class of functions for which such failures tend to generalize (with examples in community detection and arithmetic learning ) . Recall that it is known that the class of neural networks (NNs)
Revised:  The results put forward a notion of low cross-predictability that defines a more general class of functions for which such failures tend to generalize (with examples in community detection and arithmetic learning ) . Recall that it is known that the class of neural networks (NNs)
Edit intent: clarity
Original:  Parity functions form the running example of our results and the paper puts forward a notion of low cross-predictability that defines a more general class of functions for which such failures tend to generalize (with examples in community detection and arithmetic learning ) . Recall that it is known that the class of neural networks (NNs)
Revised:  Parity functions form the running example of our results and the paper puts forward a notion of cross-predictability that defines a more general class of functions for which such failures tend to generalize (with examples in community detection and arithmetic learning ) . Recall that it is known that the class of neural networks (NNs)
Edit intent: clarity
Original:  We further claim that such types of constraints are necessary to obtain failures, in that exact SGD with careful non-random initialization can  learn parities.
Revised:  We further claim that such types of constraints are necessary to obtain failures, in that exact SGD with careful non-random initialization can be shown to learn parities.
Edit intent: clarity
Original:  The cross-predictability notion has some similarity with the statistical dimension used in statistical query (SQ) algorithms, however the two definitions are different for reasons explained in the paper.
Revised:  The cross-predictability in our results plays a similar role the statistical dimension used in statistical query (SQ) algorithms, however the two definitions are different for reasons explained in the paper.
Edit intent: clarity
Original:  The cross-predictability notion has some similarity with the statistical dimension used in statistical query (SQ) algorithms, however the two definitions are different for reasons explained in the paper.
Revised:  The cross-predictability notion has some similarity with the statistical dimension in statistical query (SQ) algorithms, however the two definitions are different for reasons explained in the paper.
Edit intent: clarity
Original:  The cross-predictability notion has some similarity with the statistical dimension used in statistical query (SQ) algorithms, however the two definitions are different for reasons explained in the paper.
Revised:  The cross-predictability notion has some similarity with the statistical dimension used in statistical query (SQ) algorithms, with distinctions explained in the paper.
Edit intent: clarity
Original: We tackle a stochastic version of the Critical Node Problem (CNP) where the goal is to minimize the pairwise connectivity of a graph by the deletion of a subset of its nodes.
Revised: We tackle a stochastic version of the Critical Node Problem (CNP) where the goal is to minimize the pairwise connectivity of a graph by attacking a subset of its nodes.
Edit intent: clarity
Original:  In the stochastic setting considered, the removal of nodes can fail with a certain probability.
Revised:  In the stochastic setting considered, the attacks on nodes can fail with a certain probability.
Edit intent: clarity
Original:  In our work we focus on tree graphs and demonstrate that over trees the stochastic CNP actually generalizes to the stochastic Critical Element Detection Problem where also the deletion of edges can fail with a certain probability.
Revised:  In our work we focus on trees and demonstrate that over trees the stochastic CNP actually generalizes to the stochastic Critical Element Detection Problem where also the deletion of edges can fail with a certain probability.
Edit intent: clarity
Original:  In our work we focus on tree graphs and demonstrate that over trees the stochastic CNP actually generalizes to the stochastic Critical Element Detection Problem where also the deletion of edges can fail with a certain probability.
Revised:  In our work we focus on tree graphs and demonstrate that over trees the stochastic CNP actually generalizes to the stochastic Critical Element Detection Problem where attacks on edges can also fail with a certain probability.
Edit intent: clarity
Original:  We then derive linear and non-linear models for the considered CNP version.
Revised:  We then derive linear and nonlinear models for the considered CNP version.
Edit intent: fluency
Original:  As  side result, we introduce an approximation algorithm for a problem variant of interest.
Revised:  As a side result, we introduce an approximation algorithm for a problem variant of interest.
Edit intent: fluency
Original: Multiple hypothesis testing requires a control procedure . Simply increasing simulations or permutations to meet a Bonferroni-style threshold is prohibitively expensive.
Revised: Multiple hypothesis testing requires a control procedure . 
Edit intent: clarity
Original:  We adapt an False Discovery Rate (FDR) control approach to the topological setting, and show it to be compatible both with our null model approach and with previous approaches to hypothesis testing in persistent homology.
Revised:  We adapt an False Discovery Rate (FDR) control procedures to the topological setting, and show it to be compatible both with our null model approach and with previous approaches to hypothesis testing in persistent homology.
Edit intent: clarity
Original:  We adapt an False Discovery Rate (FDR) control approach to the topological setting, and show it to be compatible both with our null model approach and with previous approaches to hypothesis testing in persistent homology.
Revised:  We adapt an False Discovery Rate (FDR) control approach to the topological setting, and use our null model approach and with previous approaches to hypothesis testing in persistent homology.
Edit intent: coherence
Original:  We adapt an False Discovery Rate (FDR) control approach to the topological setting, and show it to be compatible both with our null model approach and with previous approaches to hypothesis testing in persistent homology.
Revised:  We adapt an False Discovery Rate (FDR) control approach to the topological setting, and show it to be compatible both with our null model for hypothesis testing using persistent homology.
Edit intent: clarity
Original:  By extending a limit theorem for persistent homology on samples from point processes, we provide theoretical validation for our FWER and FDR control methods .
Revised:  By extending a limit theorem for persistent homology for point processes .
Edit intent: clarity
Original: In this work, we propose a theoretical formulation for the tomographic linear fan-beam backprojection having low computational cost .
Revised: We propose a theoretical formulation for the tomographic linear fan-beam backprojection having low computational cost .
Edit intent: clarity
Original: We propose a theoretical  formulation for the tomographic fan-beam backprojection in standard and linear geometries.
Revised: In this work we propose a theoretical formulation for the tomographic fan-beam backprojection in standard and linear geometries.
Edit intent: clarity
Original:  Such formula is written as a Bessel-Neumann series representation in the frequency domain of the target space in polar coordinates.
Revised:  Such formula is written as a Bessel-Neumann series representation in the frequency domain in polar coordinates.
Edit intent: clarity
Original: Detecting epileptic seizure through analysis of the electroencephalography (EEG) signal becomes a standard method for the diagnosis of epilepsy.
Revised: Identifying epileptic seizures through analysis of the electroencephalography (EEG) signal becomes a standard method for the diagnosis of epilepsy.
Edit intent: clarity
Original:  The attention mechanism is to capture spatial features more effectively according to the contributions of brain areas to seizures.
Revised:  The attention mechanism is to capture spatial features according to the contributions of brain areas to seizures.
Edit intent: clarity
Original:  The attention mechanism is to capture spatial features more effectively according to the contributions of brain areas to seizures.
Revised:  The attention mechanism is to capture spatial features more effectively according to the contributions of different brain regions to seizures.
Edit intent: clarity
Original:  The BiLSTM model is to extract more discriminating temporal features in the forward and the backward directions.
Revised:  The BiLSTM is to extract more discriminating temporal features in the forward and the backward directions.
Edit intent: clarity
Original:  The BiLSTM model is to extract more discriminating temporal features in the forward and the backward directions.
Revised:  The BiLSTM model is to extract discriminating temporal features in the forward and the backward directions.
Edit intent: clarity
Original:  The testing results over the noisy real data of CHB-MIT show that the proposed method outperforms the current state-of-the-art methods.
Revised:  The testing results over the noisy data of CHB-MIT show that the proposed method outperforms the current state-of-the-art methods.
Edit intent: clarity
Original:  In both mixing-patients and cross-patient experiments, the average sensitivity and specificity are both higher while their corresponding standard deviations are lower than the methods in comparison .
Revised:  In both mixing-patients and cross-patient experiments indicate that, our approach has better performance compared with the current state-of-the-art methods and is more robust across patients .
Edit intent: clarity
Original:  Previous SNNs training methods can basically be categorized into two classes, backpropagation-like training methods and plasticity-based learning methods.
Revised:  Previous SNNs training methods can be generally categorized into two classes, backpropagation-like training methods and plasticity-based learning methods.
Edit intent: clarity
Original:  Previous SNNs training methods can basically be categorized into two classes, backpropagation-like training methods and plasticity-based learning methods.
Revised:  Previous SNNs training methods can basically be categorized into two basic classes, i.e., backpropagation-like training methods and plasticity-based learning methods.
Edit intent: clarity
Original: Out of practical concerns and with the expectation to achieve high overall efficiency of the resource utilization, this paper transforms the large scale black-box optimization problems with limited resources into online decision problems from the perspective of dynamic multi-armed bandits , a simplified view of Markov decision processes.
Revised: Out of practical concerns and with the expectation to achieve high overall efficiency of the resource utilization, this paper transforms the large scale black-box optimization problems with limited resources into online decision processes to maximize efficiency of resource utilization. Then, using the perspective of dynamic multi-armed bandits , a simplified view of Markov decision processes.
Edit intent: coherence
Original: Out of practical concerns and with the expectation to achieve high overall efficiency of the resource utilization, this paper transforms the large scale black-box optimization problems with limited resources into online decision problems from the perspective of dynamic multi-armed bandits , a simplified view of Markov decision processes.
Revised: Out of practical concerns and with the expectation to achieve high overall efficiency of the resource utilization, this paper transforms the large scale black-box optimization problems with limited resources into online decision problems from the perspective of multi-armed bandits , a simplified view of Markov decision processes.
Edit intent: clarity