wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The proposed method leverages intentional adversarial perturbations to detect whether the image contains a trigger, which can be applied in two scenarios (sanitize the training set in training stage and detect the backdoor instances in inference stage).
Revised:  The proposed method leverages intentional adversarial perturbations to detect whether the image contains a trigger, which can be applied in both the training stage and the inference stage (sanitize the training set in training stage and detect the backdoor instances in inference stage).
Edit intent: clarity
Original:  Specifically, given an untrusted image, the adversarial perturbation is added to the input image intentionally, if the prediction of  model on the perturbed image is consistent with that on the unperturbed image, the input image will be considered as a backdoor instance.
Revised:  Specifically, given an untrusted image, the adversarial perturbation is added to the image intentionally. If the prediction of model on the perturbed image is consistent with that on the unperturbed image, the input image will be considered as a backdoor instance.
Edit intent: fluency
Original:  Specifically, given an untrusted image, the adversarial perturbation is added to the input image intentionally, if the prediction of  model on the perturbed image is consistent with that on the unperturbed image, the input image will be considered as a backdoor instance.
Revised:  Specifically, given an untrusted image, the adversarial perturbation is added to the input image intentionally, if the prediction of the model on the perturbed image is consistent with that on the unperturbed image, the input image will be considered as a backdoor instance.
Edit intent: fluency
Original:  Experimental results show that, the  proposed defense method reduces the backdoor attack success rates from 99.47\%, 99.77\% and 97.89\% to 0.37\%, 0.24\% and 0.09\% on Fashion-MNIST, CIFAR-10 and GTSRB datasets, respectively.
Revised:  Experimental results show that, the backdoor detection rate of the proposed defense method reduces the backdoor attack success rates from 99.47\%, 99.77\% and 97.89\% to 0.37\%, 0.24\% and 0.09\% on Fashion-MNIST, CIFAR-10 and GTSRB datasets, respectively.
Edit intent: meaning-changed
Original:  Experimental results show that, the  proposed defense method reduces the backdoor attack success rates from 99.47\%, 99.77\% and 97.89\% to 0.37\%, 0.24\% and 0.09\% on Fashion-MNIST, CIFAR-10 and GTSRB datasets, respectively.
Revised:  Experimental results show that, the proposed defense method is 99.63\%, 99.76\% and 99.91\% on Fashion-MNIST, CIFAR-10 and GTSRB datasets, respectively.
Edit intent: meaning-changed
Original:  Besides, the proposed method maintains the visual quality of the image as the added perturbation is very small. In addition, for attacks under different settings (trigger transparency, trigger size and trigger pattern), the false acceptance rates of the proposed method are as low as 1.2\%, 0.3\% and 0.04\% on Fashion-MNIST, CIFAR-10 and GTSRB datasets, respectively , which demonstrates that the proposed method can achieve high defense performance against backdoor attacks under different attack settings  .
Revised:  Besides, the proposed method maintains the visual quality of the image as the l2 norm of the added perturbation are as low as 1.2\%, 0.3\% and 0.04\% on Fashion-MNIST, CIFAR-10 and GTSRB datasets, respectively , which demonstrates that the proposed method can achieve high defense performance against backdoor attacks under different attack settings .
Edit intent: meaning-changed
Original:  In addition, for attacks under different settings (trigger transparency, trigger size and trigger pattern), the false acceptance rates of the proposed method are as low as 1.2\%, 0.3\% and 0.04\% on Fashion-MNIST, CIFAR-10 and GTSRB datasets, respectively , which demonstrates that the proposed method can achieve high defense performance against backdoor attacks under different attack settings  .
Revised:  In addition, for attacks under different settings (trigger transparency, trigger size and trigger pattern), the false acceptance rates of the proposed method are as low as 2.8715, 3.0513 and 2.4362 on Fashion-MNIST, CIFAR-10 and GTSRB datasets, respectively , which demonstrates that the proposed method can achieve high defense performance against backdoor attacks under different attack settings .
Edit intent: meaning-changed
Original:  In addition, for attacks under different settings (trigger transparency, trigger size and trigger pattern), the false acceptance rates of the proposed method are as low as 1.2\%, 0.3\% and 0.04\% on Fashion-MNIST, CIFAR-10 and GTSRB datasets, respectively , which demonstrates that the proposed method can achieve high defense performance against backdoor attacks under different attack settings  .
Revised:  In addition, for attacks under different settings (trigger transparency, trigger size and trigger pattern), the false acceptance rates of the proposed method are as low as 1.2\%, 0.3\% and 0.04\% on Fashion-MNIST, CIFAR-10 and GTSRB datasets, respectively . In addition, it is also demonstrated that the proposed method can achieve high defense performance against backdoor attacks under different attack settings .
Edit intent: coherence
Original:  In addition, for attacks under different settings (trigger transparency, trigger size and trigger pattern), the false acceptance rates of the proposed method are as low as 1.2\%, 0.3\% and 0.04\% on Fashion-MNIST, CIFAR-10 and GTSRB datasets, respectively , which demonstrates that the proposed method can achieve high defense performance against backdoor attacks under different attack settings  .
Revised:  In addition, for attacks under different settings (trigger transparency, trigger size and trigger pattern), the false acceptance rates of the proposed method are as low as 1.2\%, 0.3\% and 0.04\% on Fashion-MNIST, CIFAR-10 and GTSRB datasets, respectively , which demonstrates that the proposed method can achieve high defense performance against backdoor attacks under different attack settings (trigger transparency, trigger size and trigger pattern). Compared with the existing defense work (STRIP), the proposed method has better detection performance on all the three datasets, and is more efficient than STRIP .
Edit intent: meaning-changed
Original:  In this work, character tokenization, morpheme tokenization, and BPE tokenization were applied to Korean as the source language and English as the target language respectively, and the comparison experiment was conducted by repeating 50,000 epochs of each 9 models using the Transformer neural network.
Revised:  In this work, alphabet tokenization, morpheme tokenization, and BPE tokenization were applied to Korean as the source language and English as the target language respectively, and the comparison experiment was conducted by repeating 50,000 epochs of each 9 models using the Transformer neural network.
Edit intent: clarity
Original: In this note we prove the following result: the sample Fr\'echet mean of a set of sparse graphs is sparse.
Revised: The availability of large datasets composed of graphs creates an unprecedented need to invent novel tools in statistical learning for "graph-valued random variables". To characterize the "average" of a sample of graphs, one can compute the sample Fr\'echet mean. Because the sample mean should provide an interpretable summary of the graph sample, one would expect that the structural properties of the sample be transmitted to the Fr\'echet mean. In this paper, we address the following foundational question: does the sample Fr\'echet mean inherit the structural properties of the graphs in the sample? Specifically, we prove the following result: the sample Fr\'echet mean of a set of sparse graphs is sparse.
Edit intent: coherence
Original:  We prove the result for the graph Hamming distance, and the spectral adjacency pseudometric, using very different arguments  .
Revised:  We prove the result for the graph Hamming distance, and the spectral adjacency pseudometric, using very different arguments . In fact, we prove a stronger result: the edge density of the sample Fr\'echet mean is bounded by the edge density of the graphs in the sample. This result guarantees that sparsity is an hereditary property, which can be transmitted from a graph sample to its sample Fr\'echet mean, irrespective of the method used to estimate the sample Fr\'echet mean .
Edit intent: meaning-changed
Original:  By replacing the 3x3 convolution with the PSA module in the bottleneck blocks of the ResNet, a novel representational block named Efficient Pyramid Split Attention (EPSA) is obtained.
Revised:  By replacing the 3x3 convolution with the PSA module in the bottleneck blocks of the ResNet, a novel representational block named Efficient Pyramid Squeeze Attention (EPSA) is obtained.
Edit intent: meaning-changed
Original:  We provide an O(m)-time randomized algorithm for the \ell_2-norm flow diffusion problem, obtaining the approximation factor of 1+1/\mathrm{poly local cut structure, we give an alternative approach for finding locally-biased low conductance cuts .
Revised:  We design an O(m)-time randomized algorithm for the \ell_2-norm flow diffusion problem, obtaining the approximation factor of 1+1/\mathrm{poly local cut structure, we give an alternative approach for finding locally-biased low conductance cuts .
Edit intent: clarity
Original:  We provide an O(m)-time randomized algorithm for the \ell_2-norm flow diffusion problem, obtaining the approximation factor of 1+1/\mathrm{poly local cut structure, we give an alternative approach for finding locally-biased low conductance cuts .
Revised:  We provide an O(m)-time randomized algorithm for the \ell_2-norm flow diffusion problem, a recently proposed diffusion model based on network flow with demonstrated graph clustering related applications both in theory and in practice. Examples include finding locally-biased low conductance cuts. Using a known connection between the optimal dual solution of the flow diffusion problem and the local cut structure, we give an alternative approach for finding locally-biased low conductance cuts .
Edit intent: meaning-changed
Original:  We provide an O(m)-time randomized algorithm for the \ell_2-norm flow diffusion problem, obtaining the approximation factor of 1+1/\mathrm{poly local cut structure, we give an alternative approach for finding locally-biased low conductance cuts .
Revised:  We provide an O(m)-time randomized algorithm for the \ell_2-norm flow diffusion problem, obtaining the approximation factor of 1+1/\mathrm{poly local cut structure, our algorithm gives an alternative approach for finding locally-biased low conductance cuts .
Edit intent: clarity
Original:  We provide an O(m)-time randomized algorithm for the \ell_2-norm flow diffusion problem, obtaining the approximation factor of 1+1/\mathrm{poly local cut structure, we give an alternative approach for finding locally-biased low conductance cuts . It is done simply by sweeping over the dual solution vector. This algorithm demonstrates a novel way of dealing with inequality constraints in graph optimization problems.
Revised:  We provide an O(m)-time randomized algorithm for the \ell_2-norm flow diffusion problem, obtaining the approximation factor of 1+1/\mathrm{poly local cut structure, we give an alternative approach for finding such cuts in nearly linear time. From a technical point of view, our algorithm contributes a novel way of dealing with inequality constraints in graph optimization problems.
Edit intent: meaning-changed
Original:  It adapts the high-level algorithmic framework of  Laplacian system solvers, but requires several new tools: vertex elimination under constraints, a new family of graph ultra-sparsifiers, and accelerated proximal gradient methods with inexact proximal mapping computation.
Revised:  It adapts the high-level algorithmic framework of nearly linear time Laplacian system solvers, but requires several new tools: vertex elimination under constraints, a new family of graph ultra-sparsifiers, and accelerated proximal gradient methods with inexact proximal mapping computation.
Edit intent: meaning-changed
Original:  These semi-discrete and fully-discrete schemes are proved to preserve the discrete averaged energy evolution law , In particular, we also prove that the proposed fully-discrete schemes exactly inherit the averaged energy evolution law almost surely if the considered model is driven by additive noise.
Revised:  These semi-discrete and fully-discrete schemes are proved to preserve the discrete averaged energy evolution law . In particular, we also prove that the proposed fully-discrete schemes exactly inherit the averaged energy evolution law almost surely if the considered model is driven by additive noise.
Edit intent: fluency
Original: This paper examines regression-adjusted estimation and inference of unconditional quantile treatment effects (QTEs) under covariate-adaptive randomizations (CARs) . Datasets from field experiments usually contain extra baseline covariates in addition to the strata indicators.
Revised: Datasets from field experiments with covariate-adaptive randomizations (CARs) . Datasets from field experiments usually contain extra baseline covariates in addition to the strata indicators.
Edit intent: clarity
Original: This paper examines regression-adjusted estimation and inference of unconditional quantile treatment effects (QTEs) under covariate-adaptive randomizations (CARs) . Datasets from field experiments usually contain extra baseline covariates in addition to the strata indicators.
Revised: This paper examines regression-adjusted estimation and inference of unconditional quantile treatment effects (QTEs) under covariate-adaptive randomizations (CARs) usually contain extra baseline covariates in addition to the strata indicators.
Edit intent: clarity
Original:  We establish the consistency, limit distribution, and validity of the multiplier bootstrap of the QTE estimator under CARs . The auxiliary regression may be estimated parametrically, nonparametrically, or via regularization when the data are high-dimensional.
Revised:  We establish the consistency, limiting distribution, and validity of the multiplier bootstrap of the QTE estimator under CARs . The auxiliary regression may be estimated parametrically, nonparametrically, or via regularization when the data are high-dimensional.
Edit intent: fluency
Original:  We establish the consistency, limit distribution, and validity of the multiplier bootstrap of the QTE estimator under CARs . The auxiliary regression may be estimated parametrically, nonparametrically, or via regularization when the data are high-dimensional.
Revised:  We establish the consistency, limit distribution, and validity of the multiplier bootstrap of the regression-adjusted QTE estimator . The auxiliary regression may be estimated parametrically, nonparametrically, or via regularization when the data are high-dimensional.
Edit intent: meaning-changed
Original:  Our estimation and inferential methods can be implemented without tuning parameters and they allow for common choices of auxiliary regressions such as linear, probit and logit regressions despite the fact that these regressions may be misspecified. Finite-sample performance of the new estimation and inferential methods is assessed in simulations and an empirical application studying the impact of child health and nutrition on educational outcomes is included .
Revised:  We demonstrate the finite sample performance of the new estimation and inferential methods is assessed in simulations and an empirical application studying the impact of child health and nutrition on educational outcomes is included .
Edit intent: clarity
Original:  In this work, we contribute a novel Large-scale Spatio-Temporal (LaST ) person re-ID dataset, including 10, 860 identities with more than 224k images.
Revised:  In this work, we contribute a novel Large-scale Spatio-Temporal LaST person re-ID dataset, including 10, 860 identities with more than 224k images.
Edit intent: clarity
Original:  In this work, we contribute a novel Large-scale Spatio-Temporal (LaST ) person re-ID dataset, including 10, 860 identities with more than 224k images.
Revised:  In this work, we contribute a novel Large-scale Spatio-Temporal (LaST ) person re-ID dataset, including 10, 862 identities with more than 224k images.
Edit intent: meaning-changed
Original:  In this work, we contribute a novel Large-scale Spatio-Temporal (LaST ) person re-ID dataset, including 10, 860 identities with more than 224k images.
Revised:  In this work, we contribute a novel Large-scale Spatio-Temporal (LaST ) person re-ID dataset, including 10, 860 identities with more than 228k images.
Edit intent: meaning-changed
Original:  We push the model scale to over 1 trillion parameters and implement it on solely 480 NVIDIA V100-32GB GPUs, in comparison with the recent SOTA Switch Transformer on 2048 TPUs . The proposed giant model achieves substantial speedup in convergence over the same-size baseline.
Revised:  We push the model scale to over 1 trillion parameters and implement it on solely 480 NVIDIA V100-32GB GPUs, in comparison with the recent SOTAs on 2048 TPUs . The proposed giant model achieves substantial speedup in convergence over the same-size baseline.
Edit intent: clarity
Original:  We push the model scale to over 1 trillion parameters and implement it on solely 480 NVIDIA V100-32GB GPUs, in comparison with the recent SOTA Switch Transformer on 2048 TPUs . The proposed giant model achieves substantial speedup in convergence over the same-size baseline.
Revised:  We push the model scale to over 1 trillion parameters and implement it on solely 480 NVIDIA V100-32GB GPUs, in comparison with the recent SOTA Switch Transformer on 2048 TPU cores . The proposed giant model achieves substantial speedup in convergence over the same-size baseline.
Edit intent: clarity
Original:  Indeed, in self-supervised learning, it is inevitable to relate to the optimization/generalization of neural networks to how they can encode the latent structures in the data, which we refer to as the feature learning process .
Revised:  Indeed, in self-supervised learning, it is inevitable to relate to the optimization/generalization of neural networks to how they can encode the latent structures in the data, which we refer to as the feature learning process .
Edit intent: fluency
Original:   The obtained results not only resolve the contradiction between the dynamic consistency and high-order accuracy of NSFD schemes but also improve  some well-known results that have been published recently in [Applied Mathematics Letters 112(2021) 106775], [AIP Conference Proceedings 2302(2020) 110003] and [Applied Mathematics Letters 50(2015) 78-82]  ]  .
Revised:  This method is based on novel non-local approximations for right-hand side functions of differential equations in combination with nonstandard denominator functions. The obtained results not only resolve the contradiction between the dynamic consistency and high-order accuracy of NSFD schemes but also improve some well-known results that have been published recently in [Applied Mathematics Letters 112(2021) 106775], [AIP Conference Proceedings 2302(2020) 110003] and [Applied Mathematics Letters 50(2015) 78-82] ] .
Edit intent: meaning-changed
Original:   The obtained results not only resolve the contradiction between the dynamic consistency and high-order accuracy of NSFD schemes but also improve  some well-known results that have been published recently in [Applied Mathematics Letters 112(2021) 106775], [AIP Conference Proceedings 2302(2020) 110003] and [Applied Mathematics Letters 50(2015) 78-82]  ]  .
Revised:  The obtained results not only resolve the contradiction between the dynamic consistency and high-order accuracy of NSFD methods but also improve some well-known results that have been published recently in [Applied Mathematics Letters 112(2021) 106775], [AIP Conference Proceedings 2302(2020) 110003] and [Applied Mathematics Letters 50(2015) 78-82] ] .
Edit intent: clarity
Original:   The obtained results not only resolve the contradiction between the dynamic consistency and high-order accuracy of NSFD schemes but also improve  some well-known results that have been published recently in [Applied Mathematics Letters 112(2021) 106775], [AIP Conference Proceedings 2302(2020) 110003] and [Applied Mathematics Letters 50(2015) 78-82]  ]  .
Revised:  The obtained results not only resolve the contradiction between the dynamic consistency and high-order accuracy of NSFD schemes but also improve and extend some well-known results that have been published recently in [Applied Mathematics Letters 112(2021) 106775], [AIP Conference Proceedings 2302(2020) 110003] and [Applied Mathematics Letters 50(2015) 78-82] ] .
Edit intent: meaning-changed
Original:   The obtained results not only resolve the contradiction between the dynamic consistency and high-order accuracy of NSFD schemes but also improve  some well-known results that have been published recently in [Applied Mathematics Letters 112(2021) 106775], [AIP Conference Proceedings 2302(2020) 110003] and [Applied Mathematics Letters 50(2015) 78-82]  ]  .
Revised:  The obtained results not only resolve the contradiction between the dynamic consistency and high-order accuracy of NSFD schemes but also improve some well-known results that have been published recently in [Applied Mathematics Letters 112(2021) 106775], [AIP Conference Proceedings 2302(2020) 110003] and [Applied Mathematics Letters 50(2015) 78-82] . Furthermore, as a simple but important application, we apply the constructed NSFD method for solving the logistic, sine, cubic, and Monod equations; consequently, the NSFD schemes constructed in the earlier work ] .
Edit intent: meaning-changed
Original:   The obtained results not only resolve the contradiction between the dynamic consistency and high-order accuracy of NSFD schemes but also improve  some well-known results that have been published recently in [Applied Mathematics Letters 112(2021) 106775], [AIP Conference Proceedings 2302(2020) 110003] and [Applied Mathematics Letters 50(2015) 78-82]  ]  .
Revised:  The obtained results not only resolve the contradiction between the dynamic consistency and high-order accuracy of NSFD schemes but also improve some well-known results that have been published recently in [Applied Mathematics Letters 112(2021) 106775], [AIP Conference Proceedings 2302(2020) 110003] and [Applied Mathematics Letters 50(2015) 78-82] Journal of Computational and Applied Mathematics 110(1999) 181-185] .
Edit intent: meaning-changed
Original:   The obtained results not only resolve the contradiction between the dynamic consistency and high-order accuracy of NSFD schemes but also improve  some well-known results that have been published recently in [Applied Mathematics Letters 112(2021) 106775], [AIP Conference Proceedings 2302(2020) 110003] and [Applied Mathematics Letters 50(2015) 78-82]  ]  .
Revised:  The obtained results not only resolve the contradiction between the dynamic consistency and high-order accuracy of NSFD schemes but also improve some well-known results that have been published recently in [Applied Mathematics Letters 112(2021) 106775], [AIP Conference Proceedings 2302(2020) 110003] and [Applied Mathematics Letters 50(2015) 78-82] ] are improved significantly. Finally, we report some numerical experiments to support and illustrate the theoretical assertions as well as advantages of the constructed NSFD method .
Edit intent: meaning-changed