wanyu/IteraTeR_v2 huggingface dataset:
Original:  The spillback behavior requires modeling the entire metropolitan area to recognize all of the upstream and downstream effects from intentional or unintentional perturbations to the network.
Revised:  The spillback behavior requires modeling the entire metropolitan area to recognize all of the upstream and downstream effects on the network.
Edit intent: clarity
Original:   This paper addresses these performance shortcomings by introducing a new platform MANTA for traffic microsimulation at the metropolitan-scale.
Revised:  This paper addresses the performance shortcomings by introducing a new platform MANTA for traffic microsimulation at the metropolitan-scale.
Edit intent: fluency
Original:  MANTA employs a highly parallelized GPU implementation that is fast enough to run simulations on large-scale demand and networks within a few minutes.
Revised:  MANTA employs a highly parallelized GPU implementation that is capable of running metropolitan-scale simulations within a few minutes.
Edit intent: clarity
Original:  We test our platform to simulate the entire Bay Area metropolitan region over the course of the morning using half-second time steps.
Revised:  The runtime to simulate all morning trips, using half-second time steps.
Edit intent: clarity
Original:  The runtime for the nine-county Bay Area simulation is just over four minutes, not including routing and initialization.
Revised:  The runtime for the nine-county San Francisco Bay Area is just over four minutes, not including routing and initialization.
Edit intent: clarity
Original:  This computational performance significantly improves  state of the art in large-scale traffic microsimulation and offers new capacity for analyzing the detailed travel patterns and travel choices of individuals for infrastructure planning and emergency management .
Revised:  This computational performance significantly improves the state of the art in large-scale traffic microsimulation and offers new capacity for analyzing the detailed travel patterns and travel choices of individuals for infrastructure planning and emergency management .
Edit intent: fluency
Original:  This computational performance significantly improves  state of the art in large-scale traffic microsimulation and offers new capacity for analyzing the detailed travel patterns and travel choices of individuals for infrastructure planning and emergency management .
Revised:  This computational performance significantly improves state of the art in large-scale traffic microsimulation . MANTA expands the capacity to analyze detailed travel patterns and travel choices of individuals for infrastructure planning and emergency management .
Edit intent: clarity
Original:  This computational performance significantly improves  state of the art in large-scale traffic microsimulation and offers new capacity for analyzing the detailed travel patterns and travel choices of individuals for infrastructure planning and emergency management .
Revised:  This computational performance significantly improves state of the art in large-scale traffic microsimulation and offers new capacity for analyzing the detailed travel patterns and travel choices of individuals for infrastructure planning .
Edit intent: clarity
Original:   The described approach may serve as the first step in developing "universal diagnostics  " utilizing artificial intelligence, with applicability extending beyond the automotive domain.
Revised:  The described approach may serve as the first step in developing "universal diagnostics , " utilizing artificial intelligence, with applicability extending beyond the automotive domain.
Edit intent: fluency
Original:   The described approach may serve as the first step in developing "universal diagnostics  " utilizing artificial intelligence, with applicability extending beyond the automotive domain.
Revised:  The described approach may serve as the first step in developing "universal diagnostics " with applicability extending beyond the automotive domain.
Edit intent: clarity
Original:  Therefore, we propose to decompose the generation space  into two subspaces: geometric and texture space.
Revised:  Therefore, we propose to decompose the generation space into two subspaces: a geometry space and a texture space.
Edit intent: fluency
Original:  We first encode portrait scans with a semantic occupancy field (SOF) , which represents semantic-embedded geometry structure and output free-viewpoint semantic segmentation maps .
Revised:  We first encode portrait scans with a semantic occupancy field (SOF) . The SOF allows the rendering of consistent 2D semantic segmentation maps .
Edit intent: clarity
Original:  Adequate experiments show that our representations enable appearance consistent shape, pose, regional styles controlling, achieve state-of-the-art results, and generalize well in various application scenarios .
Revised:  Adequate experiments show that our system can generate high quality portrait images with independently controllable geometry and texture attributes. The method also generalizes well in various application scenarios .
Edit intent: clarity
Original:  Adequate experiments show that our representations enable appearance consistent shape, pose, regional styles controlling, achieve state-of-the-art results, and generalize well in various application scenarios .
Revised:  Adequate experiments show that our representations enable appearance consistent shape, pose, regional styles controlling, achieve state-of-the-art results, and generalize well in various applications such as appearance-consistent facial animation and dynamic styling .
Edit intent: clarity
Original: There has been recent interest in collaborative multi-agent bandits, where groups of agents share recommendations to decrease per-agent regret.
Revised: Recent works have shown that agents facing independent instances of a stochastic K-armed bandit can collaborate to decrease regret.
Edit intent: clarity
Original:  We  show that even with a single malicious agent, existing collaboration-based algorithms fail to improve regret guarantees over a single-agent baseline.
Revised:  We first show that even with a single malicious agent, existing collaboration-based algorithms fail to improve regret guarantees over a single-agent baseline.
Edit intent: clarity
Original:  We propose a scheme where honest agents learn who is malicious and dynamically reduce communication with them, i.e., " blacklist "  them.
Revised:  We propose a scheme where honest agents learn who is malicious and dynamically reduce communication with ( i.e., " blacklist " them.
Edit intent: fluency
Original:  We propose a scheme where honest agents learn who is malicious and dynamically reduce communication with them, i.e., " blacklist "  them.
Revised:  We propose a scheme where honest agents learn who is malicious and dynamically reduce communication with them, i.e., " block " them.
Edit intent: fluency
Original:  We propose a scheme where honest agents learn who is malicious and dynamically reduce communication with them, i.e., " blacklist "  them.
Revised:  We propose a scheme where honest agents learn who is malicious and dynamically reduce communication with them, i.e., " blacklist " ) them.
Edit intent: fluency
Original:  We show that collaboration indeed decreases regret for this algorithm, when the number of malicious agents is small compared to the number of arms, and crucially without assumptions on the malicious agents' behavior .
Revised:  We show that collaboration indeed decreases regret for this algorithm, assuming m is small compared to the number of arms, and crucially without assumptions on the malicious agents' behavior .
Edit intent: clarity
Original:  We show that collaboration indeed decreases regret for this algorithm, when the number of malicious agents is small compared to the number of arms, and crucially without assumptions on the malicious agents' behavior .
Revised:  We show that collaboration indeed decreases regret for this algorithm, when the number of malicious agents is small compared to K but without assumptions on the malicious agents' behavior .
Edit intent: coherence
Original:  We show that collaboration indeed decreases regret for this algorithm, when the number of malicious agents is small compared to the number of arms, and crucially without assumptions on the malicious agents' behavior .
Revised:  We show that collaboration indeed decreases regret for this algorithm, when the number of malicious agents is small compared to the number of arms, and crucially without assumptions on malicious agents' behavior .
Edit intent: clarity
Original: For a graph class C, the C -Edge-Deletion problem asks for a given graph G to delete the minimum number of edges from G in order to obtain a graph in C.
Revised: For a graph class C, the C Edge-Deletion problem asks for a given graph G to delete the minimum number of edges from G in order to obtain a graph in C.
Edit intent: fluency
Original: For a graph class C, the C -Edge-Deletion problem asks for a given graph G to delete the minimum number of edges from G in order to obtain a graph in C.
Revised: For a graph class C, the C --Edge-Deletion problem asks for a given graph G to delete the minimum number of edges from G in order to obtain a graph in C.
Edit intent: fluency
Original:  We study the C -Edge-Deletion problem for C the permutation graphs, interval graphs, and other related graph classes.
Revised:  We study the C Edge-Deletion problem for C the permutation graphs, interval graphs, and other related graph classes.
Edit intent: fluency
Original:  We study the C -Edge-Deletion problem for C the permutation graphs, interval graphs, and other related graph classes.
Revised:  We study the C --Edge-Deletion problem for C the permutation graphs, interval graphs, and other related graph classes.
Edit intent: fluency
Original:  Two instrumental examples are (1)~audio spectral unmixing for which the frequency-by-time data matrix is computed with the short-time Fourier transform and is the result of a trade-off between the frequency resolution and the temporal resolution, and (2)~blind hyperspectral unmixing for which the wavelength-by-location data matrix is a trade-off between the number of wavelengths measured and the spatial resolution .
Revised:  Two instrumental examples are (1)~audio spectral unmixing for which the frequency-by-time data matrix is computed with the short-time Fourier transform and is the result of a trade-off between the frequency and the temporal resolution, and (2)~blind hyperspectral unmixing for which the wavelength-by-location data matrix is a trade-off between the number of wavelengths measured and the spatial resolution .
Edit intent: fluency
Original:  Two instrumental examples are (1)~audio spectral unmixing for which the frequency-by-time data matrix is computed with the short-time Fourier transform and is the result of a trade-off between the frequency resolution and the temporal resolution, and (2)~blind hyperspectral unmixing for which the wavelength-by-location data matrix is a trade-off between the number of wavelengths measured and the spatial resolution .
Revised:  Two instrumental examples are (1)~audio spectral unmixing for which the frequency-by-time data matrix is computed with the short-time Fourier transform and is the result of a trade-off between the frequency resolution and the temporal resolutions for the spectrogram of an audio signal, and between the number of wavelengths measured and the spatial resolution .
Edit intent: clarity
Original:  Two instrumental examples are (1)~audio spectral unmixing for which the frequency-by-time data matrix is computed with the short-time Fourier transform and is the result of a trade-off between the frequency resolution and the temporal resolution, and (2)~blind hyperspectral unmixing for which the wavelength-by-location data matrix is a trade-off between the number of wavelengths measured and the spatial resolution .
Revised:  Two instrumental examples are (1)~audio spectral unmixing for which the frequency-by-time data matrix is computed with the short-time Fourier transform and is the result of a trade-off between the frequency resolution and the temporal resolution, and (2)~blind hyperspectral unmixing for which the wavelength-by-location data matrix is a trade-off between the number of wavelengths and the spatial resolution .
Edit intent: clarity
Original:  MR- \beta -NMF performs a form of nonnegative joint factorization based on the \beta -divergence .
Revised:  MR- \beta -divergence .
Edit intent: clarity
Original:  We show on numerical experiments that MR-\beta-NMF is able to obtain high resolutions in both dimensions for two applications: the joint-factorization of two audio spectrograms, and the hyperspectral and multispectral data fusion problem .
Revised:  We show on numerical experiments that MR-\beta-NMF is able to obtain high resolutions in both dimensions on two applications: the joint-factorization of two audio spectrograms, and the hyperspectral and multispectral data fusion problem .
Edit intent: clarity
Original: Academic publication of latest research results are crucial to advance  development of all disciplines.
Revised: Academic publications of latest research results are crucial to advance development of all disciplines.
Edit intent: fluency
Original: Academic publication of latest research results are crucial to advance  development of all disciplines.
Revised: Academic publication of latest research results are crucial to advance the development of all disciplines.
Edit intent: fluency
Original:  However, there are a number of severe disadvantages in current academic publication systems.
Revised:  However, there are several severe disadvantages in current academic publication systems.
Edit intent: clarity
Original:  The first problem is the misconduct during the publication process due to the opaque paper review process.
Revised:  The first is the misconduct during the publication process due to the opaque paper review process.
Edit intent: clarity
Original:  An anonymous reviewer may give biased comments to a paper without being noticed or punished, because the comments are seldom published for evaluation.
Revised:  An anonymous reviewer may give biased comments to a paper without being noticed because the comments are seldom published for evaluation.
Edit intent: clarity
Original:  With this group signature , authors can choose to submit papers anonymously and validators take turns to distribute papers anonymously to reviewers on the blockchain according to their research interests.
Revised:  With this strong double-blind mechanism , authors can choose to submit papers anonymously and validators take turns to distribute papers anonymously to reviewers on the blockchain according to their research interests.
Edit intent: clarity
Original:  With this group signature , authors can choose to submit papers anonymously and validators take turns to distribute papers anonymously to reviewers on the blockchain according to their research interests.
Revised:  With this group signature , authors can choose to submit papers anonymously , and validators distribute papers anonymously to reviewers on the blockchain according to their research interests.
Edit intent: clarity
Original:  These processes will be recorded on the blockchain so that everyone can trace the entire process .
Revised:  These processes will be recorded on the blockchain so as to realize transparent peer preview .
Edit intent: clarity
Original:  To evaluate its efficiency, we implement Open-Pub based on Ethereum source code and conduct comprehensive experiments to evaluate its performance, including computation cost and processing delay.
Revised:  To evaluate its efficiency, we implement Open-Pub based on Ethereum and conduct comprehensive experiments to evaluate its performance, including computation cost and processing delay.
Edit intent: clarity
Original:  RDHEI is an effective technology that can embed additional data after image encryption, extract additional data without any errors and reconstruct original images losslessly.
Revised:  RDHEI is an effective technology that can embed additional data after image encryption, extract additional data error-free and reconstruct original images losslessly.
Edit intent: clarity
Original:  First, we use the median edge detector (MED) predictor  to calculate the predicted value.
Revised:  First, the median edge detector (MED) predictor to calculate the predicted value.
Edit intent: coherence
Original:  First, we use the median edge detector (MED) predictor  to calculate the predicted value.
Revised:  First, we use the median edge detector (MED) predictor is used to calculate the predicted value.
Edit intent: clarity
Original:  Next, unlike previous methods, in our proposed method, signs of prediction errors (PEs) are represented by one bit plane and absolute value of PEs are represented by other bit planes.
Revised:  Next, unlike previous methods, in our proposed method, signs of prediction errors (PEs) are represented by one bit plane and absolute values of PEs are represented by other bit planes.
Edit intent: fluency
Original:  Finally, according to different pixel prediction schemes, we embed different number of additional data  adaptively.
Revised:  Finally, according to different pixel prediction schemes, different number of additional data adaptively.
Edit intent: clarity
Original:  In this paper, a high-capacity and fully reversible RDHEI method  based on pixel prediction and multi-MSB (most significant bit) planes rearrangement is proposed .
Revised:  In this paper, a high-capacity and fully reversible RDHEI method is proposed, which is based on pixel prediction and multi-MSB (most significant bit) planes rearrangement is proposed .
Edit intent: coherence
Original:  In this paper, a high-capacity and fully reversible RDHEI method  based on pixel prediction and multi-MSB (most significant bit) planes rearrangement is proposed .
Revised:  In this paper, a high-capacity and fully reversible RDHEI method based on pixel prediction and multi-MSB (most significant bit) planes rearrangement .
Edit intent: clarity
Original:  Finally, according to different pixel prediction schemes, different number of additional data are embedded adaptively.
Revised:  Finally, according to different pixel prediction schemes, different numbers of additional data are embedded adaptively.
Edit intent: fluency
Original:  components of H, represented by an SPQR tree of H. Our approach, however, is conceptually simple  and thus likely to be more amenable to practical implementations.
Revised:  components of H, represented by an SPQR tree of H. Our approach, however, is conceptually simple , and thus likely to be more amenable to practical implementations.
Edit intent: fluency
Original: We define an all-small ruleset, Bipass, within the  normal-play theory of combinatorial games.
Revised: We define an all-small ruleset, Bipass, within the framework of normal-play theory of combinatorial games.
Edit intent: clarity
Original: We define an all-small ruleset, Bipass, within the  normal-play theory of combinatorial games.
Revised: We define an all-small ruleset, Bipass, within the normal-play combinatorial games.
Edit intent: clarity
Original:  We find a surjective function from the strips to integer atomic weights (Berlekamp, Conway and Guy 1982) that measures the number of 'units ' in all-small games.
Revised:  We find a surjective function from the strips to integer atomic weights (Berlekamp, Conway and Guy 1982) that measures the number of units in all-small games.
Edit intent: fluency
Original:  It provides detailed statistics about the fixed-target  and fixed-budget running times of the benchmarked algorithms.
Revised:  It provides detailed statistics about the fixed-target running times and fixed-budget running times of the benchmarked algorithms.
Edit intent: fluency
Original:  It provides detailed statistics about the fixed-target  and fixed-budget running times of the benchmarked algorithms.
Revised:  It provides detailed statistics about the fixed-target and fixed-budget results of the benchmarked algorithms.
Edit intent: clarity
Original:  A key advantages of IOHanalyzer over exiting packages is its highly interactive design, which allows the user to specify the performance measures, ranges, and granularity that is most useful for her experiments.
Revised:  A key advantage of IOHanalyzer over exiting packages is its highly interactive design, which allows the user to specify the performance measures, ranges, and granularity that is most useful for her experiments.
Edit intent: fluency
Original:  A key advantages of IOHanalyzer over exiting packages is its highly interactive design, which allows the user to specify the performance measures, ranges, and granularity that is most useful for her experiments.
Revised:  A key advantages of IOHanalyzer over other performance analysis packages is its highly interactive design, which allows the user to specify the performance measures, ranges, and granularity that is most useful for her experiments.
Edit intent: clarity
Original:  A key advantages of IOHanalyzer over exiting packages is its highly interactive design, which allows the user to specify the performance measures, ranges, and granularity that is most useful for her experiments.
Revised:  A key advantages of IOHanalyzer over exiting packages is its highly interactive design, which allows the user to specify the performance measures, ranges, and granularity that are most useful for her experiments.
Edit intent: fluency
Original:  It is designed to analyze not only performance traces, but also the evolution of dynamic state parameters that directly influence the search behavior of the solver . IOHanalyzer can directly process performance data from the main benchmarking platforms, including the COCO platform, Nevergrad, and our own IOHexperimenter.
Revised:  It is designed to analyze not only performance traces, but also the evolution of dynamic state parameters . IOHanalyzer can directly process performance data from the main benchmarking platforms, including the COCO platform, Nevergrad, and our own IOHexperimenter.
Edit intent: clarity
Original: We propose IOHanalyzer, a new software for analyzing the empirical performance of iterative optimization heuristics (IOHs) such as local search algorithms, genetic and evolutionary algorithms, Bayesian optimization algorithms, and similar optimizers.
Revised: Benchmarking and performance analysis play an important role in understanding the behaviour of iterative optimization heuristics (IOHs) such as local search algorithms, genetic and evolutionary algorithms, Bayesian optimization algorithms, and similar optimizers.
Edit intent: clarity
Original:  It provides detailed statistics about the fixed-target running times and  fixed-budget results of the benchmarked algorithms  . Performance aggregation over several benchmark problems is also possible, for example in the form of empirical cumulative distribution functions.
Revised:  It provides detailed statistics about fixed-target running times and fixed-budget results of the benchmarked algorithms . Performance aggregation over several benchmark problems is also possible, for example in the form of empirical cumulative distribution functions.
Edit intent: fluency
Original:  It provides detailed statistics about the fixed-target running times and  fixed-budget results of the benchmarked algorithms  . Performance aggregation over several benchmark problems is also possible, for example in the form of empirical cumulative distribution functions.
Revised:  It provides detailed statistics about the fixed-target running times and about fixed-budget results of the benchmarked algorithms . Performance aggregation over several benchmark problems is also possible, for example in the form of empirical cumulative distribution functions.
Edit intent: clarity