wanyu/IteraTeR_full_sent huggingface dataset:
Original:  First, we show that the value of the game exists in stationary strategies, which depend solely on the previous action of the player (and not the entire history ), and we provide a full characterization of the value and the optimal strategies.
Revised:  First, we show that the value of the game exists in stationary strategies, which depend solely on the previous action of the minimizing player, not the entire history ), and we provide a full characterization of the value and the optimal strategies.
Edit intent: meaning-changed
Original:  First, we show that the value of the game exists in stationary strategies, which depend solely on the previous action of the player (and not the entire history ), and we provide a full characterization of the value and the optimal strategies.
Revised:  First, we show that the value of the game exists in stationary strategies, which depend solely on the previous action of the player (and not the entire history . We provide a full characterization of the value and the optimal strategies.
Edit intent: coherence
Original:  Second, we consider a case where the  player is limited to playing  completely history-independent strategies and provide a full characterization of the value and optimal strategies in this case. Naturally, this limitation worsens his situation. We deduce a bound on his loss in the general case as well as more precise bounds when more assumptions regarding the game or the switching costs are introduced .
Revised:  Second, we consider a case where the player is limited to playing completely history-independent strategies and provide a full characterization of the (minimax) value and the strategies for obtaining it. Moreover, we present several bounds on the loss due to this limitation .
Edit intent: clarity
Original:  In an attempt to throw light on this issue here we present analysis , in terms of the amide hydrogen exchange protection factor,  to determine the likely existence of structural fluctuations in the native-state consistent with both the protein's marginal stability upper bound and the metamorphism presence.
Revised:  In an attempt to throw light on this issue here we present an assessment , in terms of the amide hydrogen exchange protection factor, to determine the likely existence of structural fluctuations in the native-state consistent with both the protein's marginal stability upper bound and the metamorphism presence.
Edit intent: clarity
Original:  In an attempt to throw light on this issue here we present analysis , in terms of the amide hydrogen exchange protection factor,  to determine the likely existence of structural fluctuations in the native-state consistent with both the protein's marginal stability upper bound and the metamorphism presence.
Revised:  In an attempt to throw light on this issue here we present analysis , in terms of the amide hydrogen exchange protection factor, that aims to determine the likely existence of structural fluctuations in the native-state consistent with both the protein's marginal stability upper bound and the metamorphism presence.
Edit intent: clarity
Original:  In an attempt to throw light on this issue here we present analysis , in terms of the amide hydrogen exchange protection factor,  to determine the likely existence of structural fluctuations in the native-state consistent with both the protein's marginal stability upper bound and the metamorphism presence.
Revised:  In an attempt to throw light on this issue here we present analysis , in terms of the amide hydrogen exchange protection factor, to determine the likely existence of structural fluctuations in the native-state consistent with both the upper bound marginal stability of proteins and the metamorphism presence.
Edit intent: clarity
Original: Transformer is a type of self-attention-based neural networks originally applied for NLP tasks. Recently, pure transformer-based models are proposed to solve computer vision problems. These visual transformers usually view an image as a sequence of patches while they ignore the intrinsic structure informationinside each patch .
Revised: Transformer is a new kind of neural architecture which encodes the input data as powerful features via the attention mechanism. Basically, the visual transformers first divide the input images into several local patches and then calculate both representations and their relationship. Since natural images are of high complexity with abundant detail and color information, the granularity of the patch dividing is not fine enough for excavating features of objects in different scales and locations .
Edit intent: meaning-changed
Original:  In this paper, we propose a novel Transformer-iN-Transformer (TNT) model for modeling both patch-level and pixel-level representation.
Revised:  In this paper, we point out that the attention inside these local patches are also essential for building visual transformers with high performance and we explore a new architecture, namely, Transformer iN Transformer (TNT) model for modeling both patch-level and pixel-level representation.
Edit intent: meaning-changed
Original:  In this paper, we propose a novel Transformer-iN-Transformer (TNT) model for modeling both patch-level and pixel-level representation. In each TNT block, an outer transformer block is utilized to process patch embeddings, and an inner transformer block extracts local features from pixel embeddings.The pixel-level feature is projected to the space of patch embedding by a linear transformation layer and then added into the patch. By stacking the TNT blocks, we build the TNT model for image recognition. Experiments on ImageNet benchmark and downstream tasks demonstrate the superiority and efficiency\times16\times4 of the proposed TNT architecture .
Revised:  In this paper, we propose a novel Transformer-iN-Transformer (TNT) . Specifically, we regard the local patches (e.g., 16\times16\times4 of the proposed TNT architecture .
Edit intent: clarity
Original:  Experiments on ImageNet benchmark and downstream tasks demonstrate the superiority and efficiency\times16\times4 of the proposed TNT architecture .
Revised:  Experiments on ImageNet benchmark and downstream tasks demonstrate the superiority and efficiency\times16\times4) as "visual words". The attention of each word will be calculated with other words in the given visual sentence with negligible computational costs. Features of both words and sentences will be aggregated to enhance the representation ability. Experiments on several benchmarks demonstrate the effectiveness of the proposed TNT architecture .
Edit intent: meaning-changed
Original:  Experiments on ImageNet benchmark and downstream tasks demonstrate the superiority and efficiency\times16\times4 of the proposed TNT architecture . For example, our TNT achieves 81.3%DIFDELCMD < \\%%% %DIF < top-1 accuracy on ImageNet which is 1.5\\% higher than that of DeiT with similar computational cost.
Revised:  Experiments on ImageNet benchmark and downstream tasks demonstrate the superiority and efficiency\times16\times4 of the proposed TNT architecture %DIFDELCMD < \\%%% %DIF < top-1 accuracy on ImageNet which is 1.5\\% higher than that of DeiT with similar computational cost.
Edit intent: coherence
Original:  The code will be available at URLabstract 
Revised:  The code will be available at URLabstract , e.g., we achieve an 81.5\% top-1 accuracy on the ImageNet, which is about 1.7\% higher than that of the state-of-the-art visual transformer with similar computational cost. The PyTorch code is available at URL and the MindSpore code is at URL
Edit intent: meaning-changed
Original:  In recent years, research on Physarum  has become more popular after Nakagaki et al. (2000) performed his famous experiments showing that Physarum was able to find the shortest route through a maze.
Revised:  In recent years, research on Physarum polycephalum has become more popular after Nakagaki et al. (2000) performed his famous experiments showing that Physarum was able to find the shortest route through a maze.
Edit intent: meaning-changed
Original:  In recent years, research on Physarum  has become more popular after Nakagaki et al. (2000) performed his famous experiments showing that Physarum was able to find the shortest route through a maze.
Revised:  In recent years, research on Physarum has become more popular after Nakagaki et al. (2000) performed their famous experiment showing that Physarum was able to find the shortest route through a maze.
Edit intent: fluency
Original:  This review will through light on recent Physarum polycephalum biological aspects, mathematical models, and Physarum bio-inspired algorithms and their applications  .
Revised:  In contrast to previous reviews that either focus on biological aspects or bio-inspired applications, here we present a comprehensive review that highlights recent Physarum polycephalum biological aspects, mathematical models, and Physarum bio-inspired algorithms and their applications .
Edit intent: clarity
Original:  This review will through light on recent Physarum polycephalum biological aspects, mathematical models, and Physarum bio-inspired algorithms and their applications  .
Revised:  This review will through light on recent Physarum polycephalum biological aspects, mathematical models, and Physarum bio-inspired algorithms and their applications . The novelty of this review stems from our exploration of Physarum intelligent behaviour in competition settings .
Edit intent: meaning-changed
Original:  In this paper, we set forth the discussion of extending the random field LIBOR market model (RFLMM) to the cross-currency setting. We assume that forward LIBOR rates of the domestics and foreign economies are lognormal and derive an approximate closed-form pricing solution for a Quanto cap written on a foreign forward LIBOR rate .
Revised:  In this paper, we set forth the discussion of extending the random field LIBOR market model (RFLMM) to the cross-currency derivatives. The uncertainty of the forward LIBOR rates of the domestics and foreign economies are lognormal and derive an approximate closed-form pricing solution for a Quanto cap written on a foreign forward LIBOR rate .
Edit intent: clarity
Original:  We assume that forward LIBOR rates of the domestics and foreign economies are lognormal and derive an approximate closed-form pricing solution for a Quanto cap written on a foreign forward LIBOR rate .
Revised:  We assume that forward LIBOR rates of our cross-currency model is driven by a two time parameter random field instead of a finite dimensional Brownian motion. To demonstrate the applications of this model, we develop an approximate closed-form pricing solution for a Quanto cap written on a foreign forward LIBOR rate .
Edit intent: meaning-changed
Original:  To address this problem , we propose a connectivity-based approach named bilateral connectivity network (BiconNet), which uses a connectivity map instead of a saliency map as the network output for effective modeling of inter-pixel relationships and object saliency.
Revised:  To address this limitation , we propose a connectivity-based approach named bilateral connectivity network (BiconNet), which uses a connectivity map instead of a saliency map as the network output for effective modeling of inter-pixel relationships and object saliency.
Edit intent: clarity
Original:  To address this problem , we propose a connectivity-based approach named bilateral connectivity network (BiconNet), which uses a connectivity map instead of a saliency map as the network output for effective modeling of inter-pixel relationships and object saliency.
Revised:  To address this problem , we propose a connectivity-based approach called bilateral connectivity network (BiconNet), which uses a connectivity map instead of a saliency map as the network output for effective modeling of inter-pixel relationships and object saliency.
Edit intent: fluency
Original:  To address this problem , we propose a connectivity-based approach named bilateral connectivity network (BiconNet), which uses a connectivity map instead of a saliency map as the network output for effective modeling of inter-pixel relationships and object saliency.
Revised:  To address this problem , we propose a connectivity-based approach named bilateral connectivity network (BiconNet), which uses connectivity masks together with saliency masks as labels for effective modeling of inter-pixel relationships and object saliency.
Edit intent: meaning-changed
Original:  Moreover, we propose a bilateral voting module to enhance the output connectivity map  and a novel edge feature enhancement method that efficiently utilizes edge-specific features with negligible parameter increase. We show that our model can use any existing saliency-based SOD framework as its backbone.
Revised:  Moreover, we propose a bilateral voting module to enhance the output connectivity map and a novel edge feature enhancement method that efficiently utilizes edge-specific features .
Edit intent: coherence
Original:  Furthermore, we compare the performance of several representative 3D semantic segmentation methods on our annotated dataset. The results show our initial segmentation outperforms other methods and achieves an overall accuracy of 93.0\% and mIoU of 66.2\% with less training time compared to other deep learning methods. We also evaluate the effect of the input training data, which shows that our method only requires about 7\% (which covers about 0.23 km2) to approach robust and adequate results whereas KPConv needs at least 33\% (which covers about 1.0 km2) .
Revised:  Furthermore, we compare the performance of several representative 3D semantic segmentation methods on the new benchmark dataset. Other researchers can use our results to train their networks: the dataset is publicly available, and the annotation tool is released as open-source .
Edit intent: clarity
Original:  To conquer this challenge, we are inspired by the latest observation of Kalibhat et al. (2020); Chen et al.(2021d) , that one can discover independently trainable and highly sparse subnetworks (a.k.a., lottery tickets) from GANs.
Revised:  To conquer this challenge, we are inspired by the latest observations , that one can discover independently trainable and highly sparse subnetworks (a.k.a., lottery tickets) from GANs.
Edit intent: clarity
Original:  Treating this as an inductive prior, we decompose the data-hungry GAN traininginto two sequential sub-problems: (i) identifying the lottery ticket from the original GAN ; then (ii) training the found sparse subnetwork with aggressive data and feature augmentations. Both sub-problems re-use the same small training setof real images. Such a coordinated framework enables us to focus on lower-complexity and more data-efficient sub-problems, effectively stabilizing training and improving convergence . Comprehensive experiments endorse the effectiveness of our proposed ultra-data-efficient training framework, across various GAN architectures (SNGAN, BigGAN, and StyleGAN2 ) and diverse datasets (CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet).
Revised:  Treating this as an inductive prior, we decompose the data-hungry GAN traininginto two sequential sub-problems: (i) identifying the lottery ticket from the original GAN using the small training set of real images; and then focusing on training that sparse subnetwork by re-using the same set. Both steps have lower complexity and are more data-efficient sub-problems, effectively stabilizing training and improving convergence . Comprehensive experiments endorse the effectiveness of our proposed ultra-data-efficient training framework, across various GAN architectures (SNGAN, BigGAN, and StyleGAN2 ) and diverse datasets (CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet).
Edit intent: clarity
Original:  Such a coordinated framework enables us to focus on lower-complexity and more data-efficient sub-problems, effectively stabilizing training and improving convergence . Comprehensive experiments endorse the effectiveness of our proposed ultra-data-efficient training framework, across various GAN architectures (SNGAN, BigGAN, and StyleGAN2 ) and diverse datasets (CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet).
Revised:  Such a coordinated framework enables us to focus on lower-complexity and more data-efficient to train. We find our coordinated framework to offer orthogonal gains to existing real image data augmentation methods, and we additionally offer a new feature-level augmentation that can be applied together with them . Comprehensive experiments endorse the effectiveness of our proposed ultra-data-efficient training framework, across various GAN architectures (SNGAN, BigGAN, and StyleGAN2 ) and diverse datasets (CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet).
Edit intent: meaning-changed
Original:  Such a coordinated framework enables us to focus on lower-complexity and more data-efficient sub-problems, effectively stabilizing training and improving convergence . Comprehensive experiments endorse the effectiveness of our proposed ultra-data-efficient training framework, across various GAN architectures (SNGAN, BigGAN, and StyleGAN2 ) and diverse datasets (CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet).
Revised:  Such a coordinated framework enables us to focus on lower-complexity and more data-efficient sub-problems, effectively stabilizing training and improving convergence . Comprehensive experiments endorse the effectiveness of our proposed framework, across various GAN architectures (SNGAN, BigGAN, and StyleGAN2 ) and diverse datasets (CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet).
Edit intent: clarity
Original:  Such a coordinated framework enables us to focus on lower-complexity and more data-efficient sub-problems, effectively stabilizing training and improving convergence . Comprehensive experiments endorse the effectiveness of our proposed ultra-data-efficient training framework, across various GAN architectures (SNGAN, BigGAN, and StyleGAN2 ) and diverse datasets (CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet).
Revised:  Such a coordinated framework enables us to focus on lower-complexity and more data-efficient sub-problems, effectively stabilizing training and improving convergence . Comprehensive experiments endorse the effectiveness of our proposed ultra-data-efficient training framework, across various GAN architectures (SNGAN, BigGAN, and StyleGAN-V2 ) and diverse datasets (CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet).
Edit intent: meaning-changed
Original:  It is known that it requires \Omega(\log\log n) time for queries provided O(n\mathrm{polylog} n) space is available and weights are from [0..n], where n is the number of tree nodes.
Revised:  It is known to require \Omega(\log\log n) time for queries provided O(n\mathrm{polylog} n) space is available and weights are from [0..n], where n is the number of tree nodes.
Edit intent: coherence
Original:  However, when applied to suffix trees, the problem, surprisingly, admits an O(n)-space solution with constant query time  as was shown by Gawrychowski, Lewenstein, and Nicholson .
Revised:  However, when applied to suffix trees, the problem, surprisingly, admits an O(n)-space solution with constant query time , as was shown by Gawrychowski, Lewenstein, and Nicholson .
Edit intent: fluency
Original:  However, when applied to suffix trees, the problem, surprisingly, admits an O(n)-space solution with constant query time  as was shown by Gawrychowski, Lewenstein, and Nicholson .
Revised:  However, when applied to suffix trees, the problem, surprisingly, admits an O(n)-space solution with constant query time as was shown by Gawrychowski, Lewenstein, and Nicholson (Proc. ESA 2014).
Edit intent: meaning-changed
Original:  In weighted multilayer noisy networks , a natural hierarchy is induced by the assumption of all but one block being under the `umbrella' of signal , while a single block is classified as exhibiting the same characteristics as interstitial interactions and subsumed into ambient noise.
Revised:  We induce a hierarchy of classifications in weighted multilayer networks by assuming that all but one block being under the `umbrella' of signal , while a single block is classified as exhibiting the same characteristics as interstitial interactions and subsumed into ambient noise.
Edit intent: clarity
Original:  In weighted multilayer noisy networks , a natural hierarchy is induced by the assumption of all but one block being under the `umbrella' of signal , while a single block is classified as exhibiting the same characteristics as interstitial interactions and subsumed into ambient noise.
Revised:  In weighted multilayer noisy networks , a natural hierarchy is induced by the assumption of all but one cluster (block) are governed by unique local signals , while a single block is classified as exhibiting the same characteristics as interstitial interactions and subsumed into ambient noise.
Edit intent: meaning-changed
Original:  In weighted multilayer noisy networks , a natural hierarchy is induced by the assumption of all but one block being under the `umbrella' of signal , while a single block is classified as exhibiting the same characteristics as interstitial interactions and subsumed into ambient noise. We use hierarchical variational inference to jointly detect block-structures and differentiate the communities' local signals from global noise.
Revised:  In weighted multilayer noisy networks , a natural hierarchy is induced by the assumption of all but one block being under the `umbrella' of signal , while a single block is classified as ambient noise, which behaves identically as interactions across differing blocks. Hierarchical variational inference is employed to jointly detect block-structures and differentiate the communities' local signals from global noise.
Edit intent: clarity
Original:  We propose a novel algorithm to find clusters in Gaussian weighted multilayer networks with a focus on these principles called Stochastic Block (with) Ambient Noise Model (SBANM)  . We apply this method to several different domains.
Revised:  These principles are incorporated into novel community detection algorithm called Stochastic Block (with) Ambient Noise Model (SBANM) . We apply this method to several different domains.
Edit intent: clarity
Original:  We propose a novel algorithm to find clusters in Gaussian weighted multilayer networks with a focus on these principles called Stochastic Block (with) Ambient Noise Model (SBANM)  . We apply this method to several different domains.
Revised:  We propose a novel algorithm to find clusters in Gaussian weighted multilayer networks with a focus on these principles called Stochastic Block (with) Ambient Noise Model (SBANM) for multilayer weighted networks . We apply this method to several different domains.
Edit intent: meaning-changed
Original:  We induce a hierarchy of classifications in weighted multilayer networks by assuming that all but one cluster (block) are governed by unique local signals, while a single block is classified as ambient noise, which behaves identically as interactions across differing blocks  .
Revised:  We induce a hierarchy of classifications in weighted multilayer networks by assuming that all but one cluster (block) are governed by unique local signals, while a single block behaves identically as interactions across differing blocks .
Edit intent: coherence
Original:  We induce a hierarchy of classifications in weighted multilayer networks by assuming that all but one cluster (block) are governed by unique local signals, while a single block is classified as ambient noise, which behaves identically as interactions across differing blocks  .
Revised:  We induce a hierarchy of classifications in weighted multilayer networks by assuming that all but one cluster (block) are governed by unique local signals, while a single block is classified as ambient noise, which behaves identically as interactions across differing blocks (ambient noise) .
Edit intent: clarity