wanyu/IteraTeR_v2 huggingface dataset:
Original:  In this article, we generate reversible adversarial examples for local visual adversarial perturbation , and use reversible data embedding technology to embed the information needed to restore the original image into the adversarial examples to generate examples that are both adversarial and reversible . Experiments on ImageNet dataset show that our method can restore the original image losslessly while ensuring the attack capability .
Revised:  In this article, we generate reversible adversarial examples for local visual adversarial perturbation , and use reversible data embedding technology to embed the information needed to restore the original image into the adversarial examples to generate examples that are both adversarial and reversible . Experiments on ImageNet dataset show that our method can restore the original image losslessly while ensuring the attack performance .
Edit intent: clarity
Original:  The proofs are divided  into components of increasing level of abstraction , using type-theoretic notions of signatures and functors (i.e., dependent \Sigma and \Pi types) as interfaces.
Revised:  The proofs are divided by signatures into components of increasing level of abstraction , using type-theoretic notions of signatures and functors (i.e., dependent \Sigma and \Pi types) as interfaces.
Edit intent: clarity
Original:  In this paper, we study two subtree counting statistics, the number of cherries and that of pitchforks for Ford's alpha model, a one-parameter family of random phylogenetic tree models which includes as specific instances of both the uniform and the Yule models, two tree models commonly used in phylogenetics.
Revised:  In this paper, we study two subtree counting statistics, the number of cherries and that of pitchforks for Ford's \alpha model, a one-parameter family of random phylogenetic tree models which includes as specific instances of both the uniform and the Yule models, two tree models commonly used in phylogenetics.
Edit intent: fluency
Original:  In this paper, we study two subtree counting statistics, the number of cherries and that of pitchforks for Ford's alpha model, a one-parameter family of random phylogenetic tree models which includes as specific instances of both the uniform and the Yule models, two tree models commonly used in phylogenetics.
Revised:  In this paper, we study two subtree counting statistics, the number of cherries and that of pitchforks for Ford's alpha model, a one-parameter family of random phylogenetic tree models that includes the uniform and the Yule models, two tree models commonly used in phylogenetics.
Edit intent: clarity
Original:  Based on a  version of the extended P\'olya urn models , in which negative entries are permitted for their replacement matrices, we obtain the strong laws of large numbers and the central limit theorems for the joint distribution of these two count statistics for the Ford model.
Revised:  Based on a version of the extended P\'olya urn models in which negative entries are permitted for their replacement matrices, we obtain the strong laws of large numbers and the central limit theorems for the joint distribution of these two count statistics for the Ford model.
Edit intent: fluency
Original:  Based on a  version of the extended P\'olya urn models , in which negative entries are permitted for their replacement matrices, we obtain the strong laws of large numbers and the central limit theorems for the joint distribution of these two count statistics for the Ford model.
Revised:  Based on a version of the extended P\'olya urn models , in which negative entries are permitted for their replacement matrices, we obtain the strong law of large numbers and the central limit theorems for the joint distribution of these two count statistics for the Ford model.
Edit intent: fluency
Original:  Based on a  version of the extended P\'olya urn models , in which negative entries are permitted for their replacement matrices, we obtain the strong laws of large numbers and the central limit theorems for the joint distribution of these two count statistics for the Ford model.
Revised:  Based on a version of the extended P\'olya urn models , in which negative entries are permitted for their replacement matrices, we obtain the strong laws of large numbers and the central limit theorem for the joint distribution of these two count statistics for the Ford model.
Edit intent: fluency
Original:  Furthermore, we derive a recursive formula for computing the exact joint distribution of these two statistics , which leads to higher order asymptotic expansions of their marginal and joint moments.\le \le 
Revised:  Furthermore, we derive a recursive formula for computing the exact joint distribution of these two statistics , which leads to higher order asymptotic expansions of their marginal and joint moments.\le \alpha\le 
Edit intent: coherence
Original: Domain generalization is an important problemwhich has gain much attention recently .
Revised: Optimizing the performance of classifiers on samples from unseen domains remains a challenging problem .
Edit intent: clarity
Original:  While most existing studies  focus on learning domain-invariant feature representations, some researchers try ensemble learning of multi experts and demonstrate promising performance.
Revised:  While most existing studies focus on learning domain-invariant feature representations, multi-expert frameworks have been proposed as a possible solution and have demonstrated promising performance.
Edit intent: clarity
Original:  However, in existing multi-expert learning frameworks , the source domain knowledge has not yet been much explored , resulting in sub-optimal performance.
Revised:  However, current multi-expert learning frameworks , the source domain knowledge has not yet been much explored , resulting in sub-optimal performance.
Edit intent: clarity
Original:  However, in existing multi-expert learning frameworks , the source domain knowledge has not yet been much explored , resulting in sub-optimal performance.
Revised:  However, in existing multi-expert learning frameworks , the source domain knowledge during inference , resulting in sub-optimal performance.
Edit intent: clarity
Original:  In this paper , we propose to adapt Transformers for the purpose of dynamically decoding source domain knowledge for domain generalization.
Revised:  In this work , we propose to adapt Transformers for the purpose of dynamically decoding source domain knowledge for domain generalization.
Edit intent: clarity
Original:  Specifically, we build one domain-specific local expert per source domain , and one domain-agnostic feature branch as query.
Revised:  Specifically, we build one domain-specific local expert per source domain and one domain-agnostic feature branch as query.
Edit intent: fluency
Original:  Then, all local-domain features will be encoded by Transformer encoders, as source domain knowledge in memory.
Revised:  A Transformer encoder encodes all domain-specific features as source domain knowledge in memory.
Edit intent: clarity
Original:  While in the Transformer decoders , the domain-agnostic query will interact with the memory in the cross-attention module, where similar domains with the input will contribute more in the attention output.
Revised:  In the Transformer decoder , the domain-agnostic query will interact with the memory in the cross-attention module, where similar domains with the input will contribute more in the attention output.
Edit intent: coherence
Original:  While in the Transformer decoders , the domain-agnostic query will interact with the memory in the cross-attention module, where similar domains with the input will contribute more in the attention output.
Revised:  While in the Transformer decoders , the domain-agnostic query interacts with the memory in the cross-attention module, where similar domains with the input will contribute more in the attention output.
Edit intent: clarity
Original:  While in the Transformer decoders , the domain-agnostic query will interact with the memory in the cross-attention module, where similar domains with the input will contribute more in the attention output.
Revised:  While in the Transformer decoders , the domain-agnostic query will interact with the memory in the cross-attention module, and domains that are similar to the input will contribute more in the attention output.
Edit intent: coherence
Original:  While in the Transformer decoders , the domain-agnostic query will interact with the memory in the cross-attention module, where similar domains with the input will contribute more in the attention output.
Revised:  While in the Transformer decoders , the domain-agnostic query will interact with the memory in the cross-attention module, where similar domains with the input will contribute more to the attention output.
Edit intent: fluency
Original:  This way, the source domain knowledge will be dynamically decoded for the inference of the current input from unseen domain.
Revised:  Thus, source domain knowledge will be dynamically decoded for the inference of the current input from unseen domain.
Edit intent: coherence
Original:  This way, the source domain knowledge will be dynamically decoded for the inference of the current input from unseen domain.
Revised:  This way, the source domain knowledge gets dynamically decoded for the inference of the current input from unseen domain.
Edit intent: clarity
Original:  This way, the source domain knowledge will be dynamically decoded for the inference of the current input from unseen domain.
Revised:  This way, the source domain knowledge will be dynamically decoded for inference of the current input from unseen domain.
Edit intent: fluency
Original:  Therefore, this mechanism makes the proposed method well generalizable to unseen domains.
Revised:  This mechanism enables the proposed method well generalizable to unseen domains.
Edit intent: coherence
Original:  Therefore, this mechanism makes the proposed method well generalizable to unseen domains.
Revised:  Therefore, this mechanism makes the proposed method to generalize well to unseen domains.
Edit intent: coherence
Original:  The proposed method is evaluated on three benchmarks in the domain generalization field .
Revised:  The proposed method has been evaluated on three benchmarks in the domain generalization field .
Edit intent: clarity
Original:  The comparison with the state-of-the-art methods shows that the proposed method achieves the best performance , outperforming the others with a clear gap .
Revised:  The comparison with the state-of-the-art methods .
Edit intent: clarity
Original:  Furthermore, the emotion strength of synthesized speech can be controlled flexibly using a strength descriptor, which is obtained by an emotion attribute ranking function.
Revised:  The emotion strength of synthesized speech can be controlled flexibly using a strength descriptor, which is obtained by an emotion attribute ranking function.
Edit intent: coherence
Original:  However, a trained ranking function on specific data has poor generalization, which limit its applicability for more realistic cases.
Revised:  However, a trained ranking function on specific data has poor generalization, which limits its applicability for more realistic cases.
Edit intent: fluency
Original:  Conventional architectures, such as cellular architectures, are not able to satisfy these requirements simultaneously, and are thus no longer suitable for the 6G era.
Revised:  Conventional architectures, such as cellular architectures, are not able to satisfy these requirements simultaneously, and are thus no longer suitable for the future era.
Edit intent: clarity
Original:  In this paper, we propose a capacity-centric (C^2) architecture for 6G .
Revised:  In this paper, we propose a capacity-centric (C^2) architecture for future wireless communication networks .
Edit intent: clarity
Original:  More importantly, different from the widely adopted conventional wisdom that basestation distributions dominate architecture designs, we find that the C^2 architecture is independent of base-station distributions, and instead user-side information should be the focus in 6G architecture designs.
Revised:  More importantly, different from the widely adopted conventional wisdom that base-station distributions dominate architecture designs, we find that the C^2 architecture is independent of base-station distributions, and instead user-side information should be the focus in 6G architecture designs.
Edit intent: fluency
Original:  More importantly, different from the widely adopted conventional wisdom that basestation distributions dominate architecture designs, we find that the C^2 architecture is independent of base-station distributions, and instead user-side information should be the focus in 6G architecture designs.
Revised:  More importantly, different from the widely adopted conventional wisdom that basestation distributions dominate architecture designs, we find that the C^2 architecture is independent of base-station distributions, and instead user-side information should be the focus in future wireless network architecture designs.
Edit intent: clarity
Original:  To handle these issues , we propose Motion-Guided Pseudo Siamese Network for Indoor Video Head Detection (MGPSN ), an end-to-end model to learn the robust head motion features.
Revised:  To handle these issues , we propose Motion-aware Pseudo Siamese Network for Indoor Video Head Detection (MGPSN ), an end-to-end model to learn the robust head motion features.
Edit intent: clarity
Original:  To handle these issues , we propose Motion-Guided Pseudo Siamese Network for Indoor Video Head Detection (MGPSN ), an end-to-end model to learn the robust head motion features.
Revised:  To handle these issues , we propose Motion-Guided Pseudo Siamese Network (MPSN ), an end-to-end model to learn the robust head motion features.
Edit intent: coherence
Original:  MGPSN integrates spatial-temporal information on pixel level, guiding the model to extract effective head features  . Experiments show that MGPSN is able to suppress static objects and enhance motion instances.
Revised:  MGPSN integrates spatial-temporal information on pixel level, guiding the model to extract effective head features . 
Edit intent: coherence
Original:  Compared with previous methods, it achieves state-of-the-art performance on the crowd Brainwash dataset.
Revised:  Compared with prior methods, it achieves state-of-the-art performance on the crowd Brainwash dataset.
Edit intent: fluency
Original:  Compared with previous methods, it achieves state-of-the-art performance on the crowd Brainwash dataset.
Revised:  Compared with previous methods, it achieves superior performance on the crowd Brainwash dataset.
Edit intent: clarity
Original:  We conduct a theoretical analysis of Bias and Variance of these methods in order to understand tradeoffs and verify the originally claimed properties.
Revised:  We conduct a theoretical analysis of bias and variance of these methods in order to understand tradeoffs and verify the originally claimed properties.
Edit intent: fluency
Original:  The presented theoretical results are mainly negative, showing limitations of these methods and in some cases revealing serious issues.
Revised:  The presented theoretical results allow for better understanding of these methods and in some cases revealing serious issues.
Edit intent: clarity
Original:  The presented theoretical results are mainly negative, showing limitations of these methods and in some cases revealing serious issues.
Revised:  The presented theoretical results are mainly negative, showing limitations of these methods and in some cases reveal serious issues.
Edit intent: fluency
Original:  We present a mapping between ARPABET/ pinyin-> SAMPA/ SAMPA-SC->phonological featuresin this paper, and tested whether native, non-native, and code-switched speech could be successfully generated using this mapping .
Revised:  We present a mapping of ARPABET/ pinyin-> SAMPA/ SAMPA-SC->phonological featuresin this paper, and tested whether native, non-native, and code-switched speech could be successfully generated using this mapping .
Edit intent: clarity
Original:  We present a mapping between ARPABET/ pinyin-> SAMPA/ SAMPA-SC->phonological featuresin this paper, and tested whether native, non-native, and code-switched speech could be successfully generated using this mapping .
Revised:  We present a mapping between ARPABET/ pinyin to SAMPA/ SAMPA-SC->phonological featuresin this paper, and tested whether native, non-native, and code-switched speech could be successfully generated using this mapping .
Edit intent: fluency
Original:  We present a mapping between ARPABET/ pinyin-> SAMPA/ SAMPA-SC->phonological featuresin this paper, and tested whether native, non-native, and code-switched speech could be successfully generated using this mapping .
Revised:  We present a mapping between ARPABET/ pinyin-> SAMPA/ SAMPA-SC and then to phonological features. We tested whether this mapping could lead to the successful generation of native, non-native, and code-switched speech could be successfully generated using this mapping .
Edit intent: clarity
Original:  The results proved that phonological features can be a feasible input system, although it needs further investigation to improve model performance.
Revised:  The results proved that phonological features could be used as a feasible input system, although it needs further investigation to improve model performance.
Edit intent: clarity
Original:  The results proved that phonological features can be a feasible input system, although it needs further investigation to improve model performance.
Revised:  The results proved that phonological features can be a feasible input system, although further investigation is needed to improve model performance.
Edit intent: clarity
Original:  The relationships between weighted conditional knowledge bases and MLPs extend to the proposed gradual semantics , which captures the stationary states of MPs, so that a dee neural network can as well be seen as a weighted argumentation graph  .
Revised:  The relationships between weighted conditional knowledge bases and MLPs extend to the proposed gradual semantics to capture the stationary states of MPs, so that a dee neural network can as well be seen as a weighted argumentation graph .
Edit intent: coherence
Original:  However, due to the Boltzmann equation's high dimensionality , high-resolution simulations are often difficult to achieve numerically.
Revised:  However, due to the high dimensionality of the Boltzmann equation , high-resolution simulations are often difficult to achieve numerically.
Edit intent: clarity
Original:  Numerical simulations on the smooth and discontinuous initial condition problem , Sod shock tube problem, and the shock structure problems demonstrate satisfactory numerical performances of the proposed invariance preserving neural closure method.
Revised:  Numerical simulations on the smooth and discontinuous initial condition problems , Sod shock tube problem, and the shock structure problems demonstrate satisfactory numerical performances of the proposed invariance preserving neural closure method.
Edit intent: fluency
Original: Many force-gradient explicit symplectic integration algorithms have been designed for the Hamiltonian H=T (p)+V(q) with a kinetic energy T(p)=p^2/2 in the existing references.
Revised: Many force-gradient explicit symplectic integration algorithms have been designed for the Hamiltonian H=T (p)+V(q) with kinetic energy T(p)=p^2/2 in the existing references.
Edit intent: fluency
Original:  They are explicit symplectic integrators with time reversibility and time symmetry .
Revised:  They are explicit symplectic integrators with symmetry or time-reversibility .
Edit intent: clarity
Original:  The optimized methods have better accuracies than the corresponding non-optimized methods . Among the tested symplectic methods, the two extended optimized seven-stage fourth-order methods of Omelyan, Mryglod and Folk exhibit the best numerical performance.
Revised:  The optimized methods have better accuracies than the corresponding non-optimized counterparts . Among the tested symplectic methods, the two extended optimized seven-stage fourth-order methods of Omelyan, Mryglod and Folk exhibit the best numerical performance.
Edit intent: clarity
Original:  This paper advocates for leveraging theories from cognitive science, using cognitive load theory as an example , to design more effective programming languages and reasoning assistants.
Revised:  This paper advocates for leveraging theories from cognitive science, focusing on cognitive load theory as an example , to design more effective programming languages and reasoning assistants.
Edit intent: clarity
Original:  This paper advocates for leveraging theories from cognitive science, using cognitive load theory as an example , to design more effective programming languages and reasoning assistants.
Revised:  This paper advocates for leveraging theories from cognitive science, using cognitive load theory , to design more effective programming languages and reasoning assistants.
Edit intent: clarity
Original:  We will release the code and trained models  URL
Revised:  We will release the code and trained models at URL
Edit intent: fluency
Original:  Most DML frameworks apply L2 normalization to feature vectors, and these feature vectors are non-sparse.
Edit intent: coherence
Original:  In this paper, we propose the new proxy-based loss and the new DML performance metric.
Revised:  However, conventional proxy-based loss and the new DML performance metric.
Edit intent: clarity
Original:  This study contributes two following: (1) we propose multi-proxies anchor (MPA ) loss , and we show the effectiveness of the multi-proxies approach on proxy-based loss  . (2) we establish the good stability and flexible normalized discounted cumulative gain ( nDCG@k ) metric as the effective DML performance metric .
Revised:  This study contributes two following: (1) MPA loss . (2) we establish the good stability and flexible normalized discounted cumulative gain ( nDCG@k ) metric as the effective DML performance metric .
Edit intent: coherence
Original:  Finally, we demonstrate MPA loss's effectiveness, and MPA loss achieves new state-of-the-art performance on two datasets for fine-grained images.
Revised:  Finally, we demonstrate MPA loss's effectiveness, and MPA loss achieves the highest accuracy on two datasets for fine-grained images.
Edit intent: clarity
Original: Even though industrial manipulators are widely used in modern manufacturing processes , deployment in unstructured environments remains an open problem.
Revised: Robotic manipulators are widely used in modern manufacturing processes , deployment in unstructured environments remains an open problem.
Edit intent: clarity
Original: Even though industrial manipulators are widely used in modern manufacturing processes , deployment in unstructured environments remains an open problem.
Revised: Even though industrial manipulators are widely used in modern manufacturing processes . However, their deployment in unstructured environments remains an open problem.
Edit intent: coherence
Original:  To deal with  variety, complexity  and uncertainty of real world manipulation tasksa general framework is essential .
Revised:  To deal with the variety, complexity and uncertainty of real world manipulation tasksa general framework is essential .
Edit intent: fluency
Original:  To deal with  variety, complexity  and uncertainty of real world manipulation tasksa general framework is essential .
Revised:  To deal with variety, complexity , and uncertainty of real world manipulation tasksa general framework is essential .
Edit intent: fluency
Original:  While reinforcement learning has shown great results for single-arm robotic manipulation in recent years , research focusing on dual-arm manipulation is still rare.
Revised:  While reinforcement learning has shown great results for single-arm robotic manipulation . However , research focusing on dual-arm manipulation is still rare.
Edit intent: coherence