wanyu/IteraTeR_v2 huggingface dataset:
Original:  Further, these results indicate that the resolved scales should be defined through an explicit filter so that the dispersive modes will not be energized .
Revised:  Further, these results indicate that the resolved scales should be defined to not include the dispersive modes will not be energized .
Edit intent: clarity
Original:  Further, these results indicate that the resolved scales should be defined through an explicit filter so that the dispersive modes will not be energized .
Revised:  Further, these results indicate that the resolved scales should be defined through an explicit filter so that the dispersive modes .
Edit intent: clarity
Original:  Second, the ASK loss is interpretable: it preserves the mutual information between the perturbed input and the kNN of the unperturbed input .
Revised:  Second, the ASK loss is interpretable: it preserves the mutual information between the perturbed input and the in-class-reference data .
Edit intent: clarity
Original:  Based on the ASK-Atk, we then derive an ASK-Defense (ASK-Def) method that optimizes the worst-case training loss induced by ASK-Atk \\%DIF > (\geq 13\\%) improvement in attack \\%DIF > (\geq 3.5\\%) in  .
Revised:  Based on the ASK-Atk, we then derive an ASK- (ASK-Def) method that optimizes the worst-case training loss induced by ASK-Atk \\%DIF > (\geq 13\\%) improvement in attack \\%DIF > (\geq 3.5\\%) in .
Edit intent: clarity
Original: Images captured in weak illumination conditions will seriously degrade the image quality.
Revised: Images captured in weak illumination conditions could seriously degrade the image quality.
Edit intent: clarity
Original:  Solving a series of degradation of low-light images can effectively improve the visual quality of the image and the performance of high-level visual tasks.
Revised:  Solving a series of degradation of low-light images can effectively improve the visual quality of images and the performance of high-level visual tasks.
Edit intent: fluency
Original:  In this paper, we propose a novel Real-low to Real-normal Network  for low-light image enhancement, dubbed R2RNet, based on the Retinex theory, which includes three subnets: a Decom-Net, a Denoise-Net, and a Relight-Net.
Revised:  In this study, a novel Retinex-based Real-low to Real-normal Network for low-light image enhancement, dubbed R2RNet, based on the Retinex theory, which includes three subnets: a Decom-Net, a Denoise-Net, and a Relight-Net.
Edit intent: clarity
Original:  In this paper, we propose a novel Real-low to Real-normal Network  for low-light image enhancement, dubbed R2RNet, based on the Retinex theory, which includes three subnets: a Decom-Net, a Denoise-Net, and a Relight-Net.
Revised:  In this paper, we propose a novel Real-low to Real-normal Network for low-light image enhancement, which includes three subnets: a Decom-Net, a Denoise-Net, and a Relight-Net.
Edit intent: coherence
Original:   Unlike most previous methods  trained on synthetic images, we collect the first Large-Scale Real-World paired low/normal-light images dataset (LSRW dataset) for training.
Revised:  Unlike most previous methods that were trained on synthetic images, we collect the first Large-Scale Real-World paired low/normal-light images dataset (LSRW dataset) for training.
Edit intent: clarity
Original:   Unlike most previous methods  trained on synthetic images, we collect the first Large-Scale Real-World paired low/normal-light images dataset (LSRW dataset) for training.
Revised:  Unlike most previous methods trained on synthetic images, we collected the first Large-Scale Real-World paired low/normal-light images dataset (LSRW dataset) for training.
Edit intent: fluency
Original:  Extensive experiments on publicly available datasets demonstrate that our method outperforms the existing state-of-the-art methods by a large margin both quantitatively and visually.
Revised:  Extensive experiments on publicly available datasets demonstrated that our method outperforms the existing state-of-the-art methods by a large margin both quantitatively and visually.
Edit intent: fluency
Original:  Extensive experiments on publicly available datasets demonstrate that our method outperforms the existing state-of-the-art methods by a large margin both quantitatively and visually.
Revised:  Extensive experiments on publicly available datasets demonstrate that our method outperforms the existing state-of-the-art methods both quantitatively and visually.
Edit intent: clarity
Original:  And we also show that the performance of the high-level visual task ( i.e. face detection) can be effectively improved by using the enhanced results obtained by our method in low-light conditions.
Revised:  In addition, our results showed that the performance of the high-level visual task ( i.e. face detection) can be effectively improved by using the enhanced results obtained by our method in low-light conditions.
Edit intent: clarity
Original:  And we also show that the performance of the high-level visual task ( i.e. face detection) can be effectively improved by using the enhanced results obtained by our method in low-light conditions.
Revised:  And we also show that the performance of the high-level visual task ( i.e. face detection) can be effectively improved by using the enhanced results obtained by our method in low-light conditions.
Edit intent: fluency
Original:  Its emergence is commonly described as the result of cleavage patterning, but this historical view conceals a large diversity of mechanisms , and overlooks many unsolved questions from a biophysics perspective.
Revised:  Its emergence is commonly described as the result of cleavage patterning, but this historical view conceals a large diversity of mechanisms and overlooks many unsolved questions from a biophysics perspective.
Edit intent: fluency
Original:  In this review, we describe generic mechanisms for blastocoel morphogenesis, rooted from biological literature and simple physical principles.
Revised:  In this review, we describe generic mechanisms for blastocoel morphogenesis, rooted in biological literature and simple physical principles.
Edit intent: fluency
Original: Recent works on sparse neural networks have demonstrated that it is possible to train a sparse network in isolation to match the performance of the corresponding dense networks with a fraction of parameters .
Revised: Recent works on sparse neural networks have demonstrated the possibility to train a sparse network in isolation to match the performance of the corresponding dense networks with a fraction of parameters .
Edit intent: clarity
Original: Recent works on sparse neural networks have demonstrated that it is possible to train a sparse network in isolation to match the performance of the corresponding dense networks with a fraction of parameters .
Revised: Recent works on sparse neural networks have demonstrated that it is possible to train a sparse network in isolation to match the performance of its corresponding dense network .
Edit intent: clarity
Original:  However, the identification of these performant sparse neural networks (winning tickets) either involves a costly iterative train-prune-retrain process (e.g., Lottery Ticket Hypothesis) or an over-extended sparse training time (e.g., Training with Dynamic Sparsity), both of which would raise financial and environmental concerns .
Revised:  However, identifying such sparse subnetworks (winning tickets) either involves a costly iterative train-prune-retrain process (e.g., Lottery Ticket Hypothesis) or an over-extended sparse training time (e.g., Training with Dynamic Sparsity), both of which would raise financial and environmental concerns .
Edit intent: clarity
Original:  However, the identification of these performant sparse neural networks (winning tickets) either involves a costly iterative train-prune-retrain process (e.g., Lottery Ticket Hypothesis) or an over-extended sparse training time (e.g., Training with Dynamic Sparsity), both of which would raise financial and environmental concerns .
Revised:  However, the identification of these performant sparse neural networks (winning tickets) either involves a costly iterative train-prune-retrain process (e.g., Lottery Ticket Hypothesis) or an over-extended training time (e.g., Training with Dynamic Sparsity), both of which would raise financial and environmental concerns .
Edit intent: coherence
Original:  However, the identification of these performant sparse neural networks (winning tickets) either involves a costly iterative train-prune-retrain process (e.g., Lottery Ticket Hypothesis) or an over-extended sparse training time (e.g., Training with Dynamic Sparsity), both of which would raise financial and environmental concerns .
Revised:  However, the identification of these performant sparse neural networks (winning tickets) either involves a costly iterative train-prune-retrain process (e.g., Lottery Ticket Hypothesis) or an over-extended sparse training time (e.g., Dynamic Sparse Training) .
Edit intent: coherence
Original:  Our results provide new insights into the strength of sparse neural networks and suggest that the benefits of sparsity go way beyond the usual training/inference expected efficiency.
Revised:  Our results provide insights into the strength of sparse neural networks and suggest that the benefits of sparsity go way beyond the usual training/inference expected efficiency.
Edit intent: clarity
Original: We design a fast algorithm that computes, for a given linear differential operator with coefficients in Z[  ], all the characteristic polynomials of its-curvatures , for all primes  <  , in asymptotically quasi-linear bit complexity in  .
Revised: We design a fast algorithm that computes, for a given linear differential operator with coefficients in Z[ ], all the characteristic polynomials of its p-curvatures , for all primes < , in asymptotically quasi-linear bit complexity in .
Edit intent: fluency
Original: We design a fast algorithm that computes, for a given linear differential operator with coefficients in Z[  ], all the characteristic polynomials of its-curvatures , for all primes  <  , in asymptotically quasi-linear bit complexity in  .
Revised: We design a fast algorithm that computes, for a given linear differential operator with coefficients in Z[ ], all the characteristic polynomials of its-curvatures , for all primes p < , in asymptotically quasi-linear bit complexity in .
Edit intent: clarity
Original: We design a fast algorithm that computes, for a given linear differential operator with coefficients in Z[  ], all the characteristic polynomials of its-curvatures , for all primes  <  , in asymptotically quasi-linear bit complexity in  .
Revised: We design a fast algorithm that computes, for a given linear differential operator with coefficients in Z[ ], all the characteristic polynomials of its-curvatures , for all primes < N , in asymptotically quasi-linear bit complexity in .
Edit intent: clarity
Original: We design a fast algorithm that computes, for a given linear differential operator with coefficients in Z[  ], all the characteristic polynomials of its-curvatures , for all primes  <  , in asymptotically quasi-linear bit complexity in  .
Revised: We design a fast algorithm that computes, for a given linear differential operator with coefficients in Z[ ], all the characteristic polynomials of its-curvatures , for all primes < , in asymptotically quasi-linear bit complexity in N .
Edit intent: clarity
Original:  We shall see in particular that the good performances of our algorithm are quickly visible . CCS CONCEPTS \bullet Computing methodologies \rightarrow Algebraic algorithms .
Revised:  We shall see in particular that the good performances of our algorithm are quickly visible .
Edit intent: clarity
Original:  Network structure and efficiency can reveal different brain states along with different ways of processing the information.
Revised:  Network structure and efficiency reveal different brain states along with different ways of processing the information.
Edit intent: clarity
Original:  In this work , complex network analysis was performed on the basis of brain connectivity obtained from Electroencephalography (EEG) data, while different auditory stimuli were presented to the subjects.
Revised:  In this work , complex network analysis was performed on the basis of brain coupling obtained from Electroencephalography (EEG) data, while different auditory stimuli were presented to the subjects.
Edit intent: clarity
Original:  This connectivity is inferred from the Phase-Amplitude coupling (PAC) from different EEG electrodes , to explore differences between controls and dyslexic subjects.
Revised:  This coupling is inferred from the Phase-Amplitude coupling (PAC) from different EEG electrodes , to explore differences between controls and dyslexic subjects.
Edit intent: clarity
Original:  This connectivity is inferred from the Phase-Amplitude coupling (PAC) from different EEG electrodes , to explore differences between controls and dyslexic subjects.
Revised:  This connectivity is inferred from the Phase-Amplitude coupling (PAC) from different EEG electrodes to explore differences between controls and dyslexic subjects.
Edit intent: fluency
Original:  Connectivity data allows the construction of a graph, and then, graph theory can be used to study the characteristics of the complex networks throughout time for controls and dyslexics.
Revised:  Coupling data allows the construction of a graph, and then, graph theory can be used to study the characteristics of the complex networks throughout time for controls and dyslexics.
Edit intent: clarity
Original:  Connectivity data allows the construction of a graph, and then, graph theory can be used to study the characteristics of the complex networks throughout time for controls and dyslexics.
Revised:  Connectivity data allows the construction of a graph, and then, graph theory is used to study the characteristics of the complex networks throughout time for controls and dyslexics.
Edit intent: clarity
Original:  From this, different characteristics linked to the temporal evolution of networks and connectivity can be pointed out for dyslexics.
Revised:  From this, different characteristics linked to the temporal evolution of networks and coupling are pointed out for dyslexics.
Edit intent: clarity
Original:  However, for a newly designed system, it takes time to accumulate enough data for neural network moded and only an approximate physics-based model is available.
Revised:  However, for a newly designed system, it takes time to accumulate enough data for neural network model and only an approximate physics-based model is available.
Edit intent: fluency
Original: Challenged by urbanization and increasing travel needs, existing transportation systems call for new mobility paradigms.
Revised: Challenged by urbanization and increasing travel needs, existing transportation systems need new mobility paradigms.
Edit intent: clarity
Original:  In this article, we present the emerging concept of Autonomous Mobility-on-Demand , whereby centrally orchestrated fleets of autonomous vehicles provide mobility service to customers.
Revised:  In this article, we present the emerging concept of autonomous mobility-on-demand , whereby centrally orchestrated fleets of autonomous vehicles provide mobility service to customers.
Edit intent: fluency
Original:  We provide a comprehensive review of methods and tools to model and solve problems related to Autonomous Mobility-on-Demand systems.
Revised:  We provide a comprehensive review of methods and tools to model and solve problems related to autonomous mobility-on-demand systems.
Edit intent: fluency
Original:  Specifically, we first identify problem settings for their analysis and control, both from the operational and the planning perspective .
Revised:  Specifically, we first identify problem settings for their analysis and control, from both operational and planning perspectives .
Edit intent: fluency
Original:  Its instance segmentation performance is also on par with Cascade Mask R-CNNon MS COCO with 60\%-90\% faster inference speeds.
Revised:  Its instance segmentation performance is also on par with Cascade Mask R-CNN on MS COCO with 60\%-90\% faster inference speeds.
Edit intent: fluency
Original: pp builders commonly use security challenges, aform of step-up authentication, to add security to their apps.
Revised: App builders commonly use security challenges, aform of step-up authentication, to add security to their apps.
Edit intent: fluency
Original: pp builders commonly use security challenges, aform of step-up authentication, to add security to their apps.
Revised: pp builders commonly use security challenges, a form of step-up authentication, to add security to their apps.
Edit intent: fluency
Original:  These systems produce recommendations in two steps: (i) multiple nominators -- tuned for low prediction latency -- preselect a small subset of candidates from the whole item pool;
Revised:  These systems produce recommendations in two steps: (i) multiple nominators , tuned for low prediction latency -- preselect a small subset of candidates from the whole item pool;
Edit intent: fluency
Original:  These systems produce recommendations in two steps: (i) multiple nominators -- tuned for low prediction latency -- preselect a small subset of candidates from the whole item pool;
Revised:  These systems produce recommendations in two steps: (i) multiple nominators -- tuned for low prediction latency , preselect a small subset of candidates from the whole item pool;
Edit intent: fluency
Original:  (ii) ~ a slower but more accurate ranker further narrows down the nominated items, and serves to the user.
Revised:  (ii) a slower but more accurate ranker further narrows down the nominated items, and serves to the user.
Edit intent: fluency
Original:  Despite their popularity, the literature on two-stage recommenders is relatively scarce, and the algorithms are often treated as the sum of their parts.
Revised:  Despite their popularity, the literature on two-stage recommenders is relatively scarce, and the algorithms are often treated as mere sums of their parts.
Edit intent: clarity
Original:  Such treatment presupposes that the two-stage performance is explained by the behavior of individual components if they were deployed independently .
Revised:  Such treatment presupposes that the two-stage performance is explained by the behavior of the individual components in isolation .
Edit intent: clarity
Original:  Since searching for a good choice manually is difficult, we  learn one instead .
Revised:  Since searching for a good pool allocation is difficult, we learn one instead .
Edit intent: clarity
Original:  Since searching for a good choice manually is difficult, we  learn one instead .
Revised:  Since searching for a good choice manually is difficult, we propose to learn one instead .
Edit intent: clarity
Original:  In particular, using a Mixture-of-Experts approach, we train the nominators (experts) to specialize on different subsets of the item pool .
Revised:  In particular, using a Mixture-of-Experts based approach .
Edit intent: clarity
Original: Accurately predicting the future motion of surrounding vehicles requires reasoning about the inherent uncertainty in goals and driving behavior.
Revised: Accurately predicting the future motion of surrounding vehicles requires reasoning about the inherent uncertainty in driving behavior.
Edit intent: clarity
Original:  The policy rollouts explore different goals given our current observations, ensuring that the model captures lateral variability.
Revised:  The policy rollouts explore different goals given current observations, ensuring that the model captures lateral variability.
Edit intent: fluency
Original:  The longitudinal variability is captured by our novel latent variable model decoder that is conditioned on various subsets of the lane graph.
Revised:  Longitudinal variability is captured by our novel latent variable model decoder that is conditioned on various subsets of the lane graph.
Edit intent: clarity
Original:  The longitudinal variability is captured by our novel latent variable model decoder that is conditioned on various subsets of the lane graph.
Revised:  The longitudinal variability is captured by our latent variable model decoder that is conditioned on various subsets of the lane graph.
Edit intent: clarity
Original:  Detailed ablations highlight the importance of both the policy rollouts and the decoder architecture.
Revised:  Detailed ablations highlight the importance of the policy rollouts and the decoder architecture.
Edit intent: clarity
Original:  The second contribution is a new method, called acontrario , that explicitly models conditionality for both parts of the adversarial architecture via a novel acontrario loss that involves training the discriminator to learn unconditional (adverse) examples.
Revised:  The second contribution is a new method, called acontrario , that explicitly models conditionality for both parts of the adversarial architecture via a novel a contrario loss that involves training the discriminator to learn unconditional (adverse) examples.
Edit intent: fluency
Original:  This leads to a novel type of data augmentation approach for GANs ( acontrario learning) which allows to restrict the search space of the generator to conditional outputs using adverse examples.
Revised:  This leads to a novel type of data augmentation approach for GANs ( a contrario learning) which allows to restrict the search space of the generator to conditional outputs using adverse examples.
Edit intent: fluency
Original:  Comparisons with the cGAN architecture for different applications show significant improvements in performance on well known datasets including, semantic image synthesis, image segmentation and monocular depth prediction  using different metrics including Fr\'echet Inception Distance (FID), mean Intersection over Union (mIoU), Root Mean Square Error log (RMSE log) and Number of statistically-Different Bins (NDB) 
Revised:  Comparisons with the cGAN architecture for different applications show significant improvements in performance on well known datasets including, semantic image synthesis, image segmentation , monocular depth prediction using different metrics including Fr\'echet Inception Distance (FID), mean Intersection over Union (mIoU), Root Mean Square Error log (RMSE log) and Number of statistically-Different Bins (NDB) 
Edit intent: fluency
Original:  Comparisons with the cGAN architecture for different applications show significant improvements in performance on well known datasets including, semantic image synthesis, image segmentation and monocular depth prediction  using different metrics including Fr\'echet Inception Distance (FID), mean Intersection over Union (mIoU), Root Mean Square Error log (RMSE log) and Number of statistically-Different Bins (NDB) 
Revised:  Comparisons with the cGAN architecture for different applications show significant improvements in performance on well known datasets including, semantic image synthesis, image segmentation and monocular depth prediction using different metrics including Fr\'echet Inception Distance (FID), mean Intersection over Union (mIoU), Root Mean Square Error log (RMSE log) and Number of statistically-Different Bins (NDB) .
Edit intent: fluency
Original: Questionable publications have been criticized for their greedybehaviour, yet have not been investigated their influence on academia quantitatively .
Revised: Questionable publications have been accused of "greedy" practices; however, their influence on academia quantitatively .
Edit intent: clarity