wanyu/IteraTeR_v2 huggingface dataset:
Original:  Every new generated image contains similar spatial information from a source image while transferring textures from a target image of opposite label.
Revised:  Every new generated image contains similar spatial information from a source image while transferring textures from a target image with a different label.
Edit intent: clarity
Original:  Our model integrates a texture co-occurrence loss that determines whether a generated image's texture is similar to that of the target, and a spatial self-similarity loss that determines whether the spatial details between the generated and source images are well preserved.
Revised:  Our model includes a texture co-occurrence loss that determines whether a generated image's texture is similar to that of the target, and a spatial self-similarity loss that determines whether the spatial details between the generated and source images are well preserved.
Edit intent: fluency
Original:  Our model integrates a texture co-occurrence loss that determines whether a generated image's texture is similar to that of the target, and a spatial self-similarity loss that determines whether the spatial details between the generated and source images are well preserved.
Revised:  Our model integrates a texture co-occurrence loss that determines whether a generated image's texture is similar to that of the target, and a spatial self-similarity loss that determines whether the content details between the generated and source images are well preserved.
Edit intent: clarity
Original:  Both generated and original training images are further used to train a classifier that is able to avoid learning unknown bias representations.
Revised:  Both generated and original training images are further used to train a classifier that is able to improve robustness against inconsistent bias representations.
Edit intent: clarity
Original:  We employ three distinct artificially designed datasets with known biases to demonstrate the ability of our method to mitigate bias information , and report competitive performance over existing state-of-the-art methods. 
Revised:  We employ three distinct artificially designed datasets with known biases to demonstrate the ability of our method to mitigate bias information . For all cases, our method outperformed existing state-of-the-art methods. 
Edit intent: clarity
Original:  The code, simulation and the dataset are publicly available on the project page: URL
Revised:  The code, simulation and the dataset are publicly available on URL
Edit intent: clarity
Original: In this article, we aim to study the stability and dynamic transition of an electrically conducting fluid in the presence of an external uniform horizontal magnetic field and a rotation based on a Boussinesq approximation model.
Revised: In this article, we aim to study the stability and dynamic transition of an electrically conducting fluid in the presence of an external uniform horizontal magnetic field and rotation based on a Boussinesq approximation model.
Edit intent: fluency
Original:  The center manifold reduction theory is applied to reduce the infinite dimensional system to the corresponding finite dimensional one together with  several non-dimensional transition numbers that determine the dynamic transition types.
Revised:  The center manifold reduction theory is applied to reduce the infinite dimensional system to the corresponding finite dimensional one together with one or several non-dimensional transition numbers that determine the dynamic transition types.
Edit intent: clarity
Original:  Our results indicate that both transition of continuous type and transition of jump type can occur at certain parameter region .
Revised:  Our results indicate that both continuous and jump transitions can occur at certain parameter region .
Edit intent: clarity
Original:  The scenario used in the contest is an extension of the previous edition (14th) ``Agents Assemble'' wherein two teams of agents move around a 2D grid and compete to assemble complex block structures.
Revised:  The scenario used in the contest is an extension of the previous edition (14th) "Agents Assemble" wherein two teams of agents move around a 2D grid and compete to assemble complex block structures.
Edit intent: fluency
Original:  In addition, regularization-based methods lack transparent tradeoff hyperparameter selection to realize computational budget.
Revised:  In addition, regularization based methods lack transparent tradeoff hyperparameter selection to realize computational budget.
Edit intent: fluency
Original:  Our contribution is twofold : 1) decoupled task and pruning training.
Revised:  Our contribution is two-fold : 1) decoupled task and pruning training.
Edit intent: fluency
Original:  Each mask predictor module is trained to predict if the log-likelihood of each filter in the current layer belongs to the top-k activated filters.
Revised:  Each mask predictor module is trained to predict if the log-likelihood for each filter in the current layer belongs to the top-k activated filters.
Edit intent: fluency
Original:  We show experiments on several neural architectures, such as VGG, ResNet , and MobileNet on CIFAR and ImageNet datasets.
Revised:  We show experiments on several neural architectures, such as VGG, ResNet and MobileNet on CIFAR and ImageNet datasets.
Edit intent: fluency
Original:  Similarly in ImageNet, we achieve a lower drop in accuracy with up to 13\% improvement in FLOPs reduction.
Revised:  Similarly in ImageNet, we achieve lower drop in accuracy with up to 13\% improvement in FLOPs reduction.
Edit intent: fluency
Original:  In this work, we propose Neural Dubber, the first neural network model to solve a novel automatic video dubbing (AVD) task: synthesizing human speech synchronized with the given silent video from the text.
Revised:  In this work, we propose Neural Dubber, the first neural network model to solve a novel automatic video dubbing (AVD) task: synthesizing human speech synchronized with the given video from the text.
Edit intent: clarity
Original:  Regarding \ell_1/\ell_\infty matrix measures, we show their quasiconvexity with respect to positive diagonal weights, their monotonicity with respect to principal submatrices, and provide closed form expressions for certain matrix polytopes.
Revised:  Regarding \ell_1/\ell_\infty matrix measures, we show their quasiconvexity with respect to positive diagonal weights, monotonicity results for principal submatrices, and provide closed form expressions for certain matrix polytopes.
Edit intent: clarity
Original:  Regarding nonsmooth contraction theory, we show that the one-sided Lipschitz constant of a Lipschitz vector field is equal to the essential supremum of the matrix measure of its Jacobian.
Revised:  Regarding nonsmooth contraction theory, we show that the one-sided Lipschitz constant of a Lipschitz vector field equals the essential supremum of the matrix measure of its Jacobian.
Edit intent: clarity
Original:  Second , we apply these general results to classes of recurrent neural circuits, including Hopfield, firing rate, Persidskii, Lur'e and other models.
Revised:  Third , we apply these general results to classes of recurrent neural circuits, including Hopfield, firing rate, Persidskii, Lur'e and other models.
Edit intent: coherence
Original:  Our  analysis establishes also absolute contraction and total contraction.
Revised:  Our analysis establishes also absolute and total contraction.
Edit intent: fluency
Original:  In this paper, we improve CIC from two aspects: 1) Existing reinforcement training methods are not applicable to structure-related CIC models due to the fact that the accuracy-based reward focuses mainly on contents rather than semantic structures  .
Revised:  Several structure-related CIC models due to the fact that the accuracy-based reward focuses mainly on contents rather than semantic structures .
Edit intent: coherence
Original:  In this paper, we improve CIC from two aspects: 1) Existing reinforcement training methods are not applicable to structure-related CIC models due to the fact that the accuracy-based reward focuses mainly on contents rather than semantic structures  .
Revised:  In this paper, we improve CIC from two aspects: 1) Existing reinforcement training methods are not applicable to structure-related CIC models due to the fact that the accuracy-based reward focuses mainly on contents rather than semantic structures , existing reinforcement training methods are not applicable to structure-related CIC models .
Edit intent: coherence
Original:  To solve the problem above , we propose a novel reinforcement training method for structure-related CIC models : Self-Annotated Training (SAT), where a recursive sampling mechanism (RSM ) is designed to force the input control signal to match the actual output sentence.
Revised:  To solve the problem above , we propose a novel reinforcement training method for structure-related control signals : Self-Annotated Training (SAT), where a recursive sampling mechanism (RSM ) is designed to force the input control signal to match the actual output sentence.
Edit intent: clarity
Original:  Extensive experiments conducted on MSCOCO show that our SAT method improves C-Transformer (XE) on CIDEr-D score from 118.6 to 130.1 in the length-control task and from 132.2 to 142.7 in the tense-control task, while maintaining more than 99%DIFDELCMD < \\%%% %DIF < matching accuracy with the control signal.
Revised:  %DIFDELCMD < \\%%% %DIF < matching accuracy with the control signal.
Edit intent: coherence
Original:  Lung adenocarcinomas are classified as pre-invasive , minimally invasive, and invasive adenocarcinomas.
Revised:  Lung adenocarcinomas are classified as pre invasive , minimally invasive, and invasive adenocarcinomas.
Edit intent: fluency
Original:  Currently, the primary imaging modality to assess and predict the invasiveness of LAUCs is the chest CT.
Revised:  Currently, the primary imaging modality to assess and predict the invasiveness of LUACs is the chest CT.
Edit intent: fluency
Original:  In this paper, a predictive transformer-based framework, referred to as the "CAE-Transformer", is developed to classify LAUCs .
Revised:  In this paper, a predictive transformer-based framework, referred to as the "CAE-Transformer", is developed to classify LUACs .
Edit intent: fluency
Original:  Experimental results on our in-house dataset of 114 pathologically proven Sub-Solid Nodules (SSNs) demonstrate the superiority of the CAE-Transformer over the histogram/radiomics-based models and its deep learning-based counterparts, achieving an accuracy of 87.73\%, sensitivity of 88.67\%, specificity of 86.33\%, and AUC of 0.913, using a 10-fold cross-validation.
Revised:  Experimental results on our in-house dataset of 114 pathologically proven Sub Solid Nodules (SSNs) demonstrate the superiority of the CAE-Transformer over the histogram/radiomics-based models and its deep learning-based counterparts, achieving an accuracy of 87.73\%, sensitivity of 88.67\%, specificity of 86.33\%, and AUC of 0.913, using a 10-fold cross-validation.
Edit intent: fluency
Original:  The code and data are available at URL
Revised:  The code are available at URL
Edit intent: clarity
Original:  In this setting, \mathcal{ALC concepts are represented as objects, concept subsumptions as arrows, and memberships as logical quantifiers over objects and arrows of categories.
Revised:  In this setting, ALC concepts are represented as objects, concept subsumptions as arrows, and memberships as logical quantifiers over objects and arrows of categories.
Edit intent: fluency
Original: We introduce graph wegdelets - a tool for data compression on graphs based on the representation of signals by piecewise constant functions on adaptively generated binary wedge partitionings of a graph.
Revised: We introduce graph wedgelets - a tool for data compression on graphs based on the representation of signals by piecewise constant functions on adaptively generated binary wedge partitionings of a graph.
Edit intent: fluency
Original:  We prove that continuous results on best m-term approximation with geometric wavelets can be transferred to the discrete graph setting , and show that our wedgelet representation of graph signals can be encoded and implemented in a simple way.
Revised:  We prove that continuous results on best m-term approximation with geometric wavelets can be transferred to the discrete graph setting and show that our wedgelet representation of graph signals can be encoded and implemented in a simple way.
Edit intent: fluency
Original:  Finally, we illustrate that this graph based method can be applied for the compression of images as well.
Revised:  Finally, we illustrate that this graph-based method can be applied for the compression of images as well.
Edit intent: fluency
Original: Despite the great success of self-supervised learning with large floating point networks , such networks are not readily deployable to edge devices.
Revised: Despite the great success of self-supervised learning with large floating point networks . But such networks are not readily deployable to edge devices.
Edit intent: coherence
Original:  To accelerate deployment of models to edge devices for various downstream tasks by unsupervised representation learning , we propose a self-supervised learning method for binary networks  .
Revised:  To accelerate deployment of models with the benefit of unsupervised representation learning to such resource limited devices for various downstream tasks by unsupervised representation learning , we propose a self-supervised learning method for binary networks .
Edit intent: clarity
Original:  To accelerate deployment of models to edge devices for various downstream tasks by unsupervised representation learning , we propose a self-supervised learning method for binary networks  .
Revised:  To accelerate deployment of models to edge devices for various downstream tasks , we propose a self-supervised learning method for binary networks .
Edit intent: clarity
Original:  In particular, we propose to use a randomly initialized classifier  attached to a pretrained floating point feature extractor as targets and jointly train it with a binary network.
Revised:  In particular, we propose to jointly train a randomly initialized classifier attached to a pretrained floating point feature extractor as targets and jointly train it with a binary network.
Edit intent: coherence
Original:  In particular, we propose to use a randomly initialized classifier  attached to a pretrained floating point feature extractor as targets and jointly train it with a binary network.
Revised:  In particular, we propose to use a randomly initialized classifier , attached to a pretrained floating point feature extractor as targets and jointly train it with a binary network.
Edit intent: fluency
Original:  In particular, we propose to use a randomly initialized classifier  attached to a pretrained floating point feature extractor as targets and jointly train it with a binary network.
Revised:  In particular, we propose to use a randomly initialized classifier attached to a pretrained floating point feature extractor , with a binary network.
Edit intent: coherence
Original:  For better training of the binary network , we propose a feature similarity loss, a dynamic balancing scheme of loss terms, and modified multi-stage training .
Revised:  Additionally , we propose a feature similarity loss, a dynamic balancing scheme of loss terms, and modified multi-stage training .
Edit intent: coherence
Original:  For better training of the binary network , we propose a feature similarity loss, a dynamic balancing scheme of loss terms, and modified multi-stage training .
Revised:  For better training of the binary network , we propose a feature similarity loss, a dynamic loss balancing and modified multi-stage training .
Edit intent: clarity
Original:  We call our method as BSSL . Our empirical validations show that BSSL outperforms self-supervised learning baselines for binary networks in various downstream tasks and outperforms supervised pretraining in certain tasks .
Revised:  We call our method as BSSL . Our empirical validations show that BSSL outperforms self-supervised baselines for binary networks in various downstream tasks and outperforms supervised pretraining in certain tasks .
Edit intent: clarity
Original:  We call our method as BSSL . Our empirical validations show that BSSL outperforms self-supervised learning baselines for binary networks in various downstream tasks and outperforms supervised pretraining in certain tasks .
Revised:  We call our method as BSSL . Our empirical validations show that BSSL outperforms self-supervised learning baselines for binary networks and sometimes outperforms supervised pretraining in certain tasks .
Edit intent: clarity
Original:  We call our method as BSSL . Our empirical validations show that BSSL outperforms self-supervised learning baselines for binary networks in various downstream tasks and outperforms supervised pretraining in certain tasks .
Revised:  We call our method as BSSL . Our empirical validations show that BSSL outperforms self-supervised learning baselines for binary networks in various downstream tasks and outperforms supervised pretraining .
Edit intent: clarity
Original:  We will determine the parameters of these codes and discuss their minimum distance codewords .
Revised:  We will determine the parameters of these codes .
Edit intent: clarity
Original:  Challenges arise when the operating environment is dynamic and unknown since the environment can be found prohibitive, leading to potentially conflicting tasks where pre-specified LTL tasks cannot be fully satisfied.
Revised:  Challenges arise when the operating environment is dynamic and unknown since the environment can be found prohibitive, leading to potentially conflicting tasks where pre-specified timed missions cannot be fully satisfied.
Edit intent: clarity
Original:  The eLegPredict is trained and tested over 3072 supreme court cases and has achieved ~ 76\% accuracy (F1-score).
Revised:  The eLegPredict is trained and tested over 3072 supreme court cases and has achieved 76\% accuracy (F1-score).
Edit intent: coherence
Original:  This paper is a brief survey of the recent literature on 3D face reconstruction from a single image .
Revised:  This paper is a review of the recent literature on 3D face reconstruction from a single image .
Edit intent: clarity
Original: We present a High-Resolution Transformer ( HRT ) that learns high-resolution representations for dense prediction tasks, in contrast to the original Vision Transformer that produces low-resolution representations and has high memory and computational cost.
Revised: We present a High-Resolution Transformer ( HRFormer ) that learns high-resolution representations for dense prediction tasks, in contrast to the original Vision Transformer that produces low-resolution representations and has high memory and computational cost.
Edit intent: fluency
Original:  We demonstrate the effectiveness of the High-Resolution Transformer on both human pose estimation and semantic segmentation tasks, e.g., HRT outperforms Swin transformer by 1.3 AP on COCO pose estimation with 50\\%DIF < fewer parameters and 30\\% fewer FLOPs.
Revised:  We demonstrate the effectiveness of the High-Resolution Transformer on both human pose estimation and semantic segmentation tasks, e.g., HRFormer outperforms Swin transformer by 1.3 AP on COCO pose estimation with 50\\%DIF < fewer parameters and 30\\% fewer FLOPs.
Edit intent: clarity
Original:  We propose constraint-based algorithms for the problem of model checking CTL on BPPs and handle formulas by SMT solver Z3.
Revised:  We propose constraint-based algorithms for the problem of model checking on BPPs and handle formulas by SMT solver Z3.
Edit intent: clarity
Original:  Mining features from these time series is a crucial task with a multidisciplinary impact . Usually, these features are obtained from structural characteristics of time series , such as trend, seasonality and autocorrelation, sometimes requiring data transformations and parametric models.
Revised:  Mining features from these time series is a crucial task with a multidisciplinary impact . Usually, the features are obtained from structural characteristics of time series , such as trend, seasonality and autocorrelation, sometimes requiring data transformations and parametric models.
Edit intent: fluency
Original:   Our results are promising and show that network analysis can be used to characterize different types of time series and that different mapping methods capture different characteristics of the time series  .
Revised:  Our results are very promising, with network features from different mapping methods capturing different properties of the time series .
Edit intent: clarity
Original:  We present here a new tool, called 3M-DNN , forrepairing a given DNN, which is known to err on some set of inputs.
Revised:  We present here a new tool, called 3M-DNN repairing a given DNN, which is known to err on some set of inputs.
Edit intent: fluency
Original:  The novel repair procedure implemented in 3M-DNN computes a modification to the network's weights that corrects its behavior, and attempts to minimize this change via a sequence of calls to a backend, black-box DNN verification engine.
Revised:  The novel repair procedure implemented in 3M-DNN computes a modification to the network's weights that corrects its behavior, and attempts to minimize this change via a sequence of calls to a backend, black-box DNN verification engine.
Edit intent: fluency
Original:  We evaluated 3M-DNN tool on an extensive set of benchmarks, obtaining promising results.
Revised:  We evaluated 3M-DNN tool on an extensive set of benchmarks, obtaining promising results.
Edit intent: fluency
Original:  Data Availability Statement: An artifact will be submitted to the AEC under EasyChair ID 60.
Edit intent: coherence
Original:  On the algorithmic side, we show how known techniques for connected component counting in constant space due to Peng and Sohler SODA `18%DIFDELCMD < ] %%% easily translate from random order streams to our model with only a small loss in parameters.
Revised:  On the algorithmic side, we show how known techniques for connected component counting in constant space due to Peng and Sohler %DIFDELCMD < ] %%% easily translate from random order streams to our model with only a small loss in parameters.
Edit intent: clarity
Original:  Our algorithm obtains an additive \varepsilon n approximation to the number of connected components in the input graph using space (1 / \varepsilon)^{O(1/\varepsilon) by building a representative sample of vertices in the graph that belong to O(1/\varepsilon)-size components to estimate the count.
Revised:  Our algorithm obtains an additive \varepsilon n approximation to the number of connected components in the input graph using space (1 / by building a representative sample of vertices in the graph that belong to O(1/\varepsilon)-size components to estimate the count.
Edit intent: coherence
Original:  On the lower bound side, we show that (1/\varepsilon)^{\Omega(1/\varepsilon)} space is necessary for finding a connected component of size O(1/\varepsilon) even in graphs where most vertices reside in such components -- this makes progress towards an open problem }    of Peng and Sohler [SODA ` 18] and constitutes our main technical contribution.
Revised:  On the lower bound side, we show that (1/\varepsilon)^{\Omega(1/\varepsilon)} space is necessary for finding a connected component of size O(1/\varepsilon) even in graphs where most vertices reside in such components -- this makes progress towards an open problem } of Peng and Sohler [SODA ' 18] and constitutes our main technical contribution.
Edit intent: fluency
Original: In this work we present a new type of efficient deep-unrolling networks for solving imaging inverse problems.
Revised: In this work we propose a new type of efficient deep-unrolling networks for solving imaging inverse problems.
Edit intent: clarity