With the rapid advancement of Web 3.0 technologies, public blockchain platforms are witnessing the emergence of novel services designed to enhance user privacy and anonymity. However, the powerful untraceability features inherent in these services inadvertently make them attractive tools for criminals seeking to launder illicit funds. Notably, existing de-anonymization methods face three major challenges when dealing with such transactions: highly homogenized transactional semantics, limited ability to model temporal discontinuities, and insufficient consideration of structural sparsity in account association graphs. To address these, we propose GradWATCH, designed to track anonymous accounts in Ethereum privacy-preserving services. Specifically, we first design a learnable account feature mapping module to extract informative transactional semantics from raw on-chain data. We then incorporate transaction relations into the account association graph to alleviate the adverse effects of structural sparsity. To capture temporal evolution, we further propose an edge-aware sliding-window mechanism that propagates and updates gradients at three granularities. Finally, we identify accounts controlled by the same entity by measuring their embedding distances in the learned representation space. Experimental results show that even under the conditions of unbalanced labels and sparse transactions, GradWATCH still achieves significant performance gains, with relative improvements ranging from 1.62% to 15. 22% in the MRR and from 3. 85% to 7. 31% in the F_1.

Abstract Although single‐image 3D human reconstruction has made significant progress in recent years, few of the current state‐of‐the‐art methods can accurately restore the appearance and geometric details of loose clothing. To achieve high‐quality reconstruction of a human body wearing loose clothing, we propose a learnable dynamic adjustment framework that integrates side‐view features and the uncertainty of the parametric human body model to adaptively regulate its reliability based on the clothing type. Specifically, we first adopt the Vision Transformer model as an encoder to capture the image features of the input image, and then employ SMPL‐X to decouple the side‐view body features. Secondly, to reduce the limitations imposed by the regularization of the parametric model, particularly for loose garments, we introduce a learnable coefficient to reduce the reliance on SMPL‐X. This strategy effectively accommodates the large deformations caused by loose clothing, thereby accurately expressing the posture and clothing in the image. To evaluate the effectiveness, we validate our method on the public CLOTH4D and Cape datasets, and the experimental results demonstrate better performance compared to existing approaches. The code is available at https://github.com/yyd0613/CoRe-Human .

Electroencephalogram (EEG) signals for motor imagery (MI) are easily influenced by the environment and the state of the subject, which exhibit temporal and spatial variance. And this variance is more significant across subjects and sessions, which imposes limitations on the cross-domain MI tasks. To address this problem, we propose a Multi-level Spatial-Temporal Adaptation Network (MSTAN), extracting domain-invariant multi-level spatial-temporal features to overcome domain differences. First, stacked spatial-temporal graph convolution (STGCN) layers and an attention-based readout module are designed to extract spatial-temporal patterns of EEGs at multiple levels. An adaptation scheme is then introduced to narrow domain differences: 1) Individual graph parameters for the source and target domains are designed at each STGCN layer to capture the domain-specific brain region dynamic relationships; 2) The differences of spatial-temporal features between the source and target domain are reduced by minimizing the distribution distance. Experiments are conducted to evaluate the proposed method on a public dataset and the results show that our method achieves state-of-the-art performance in cross-domain motor imagery classification.

The paper concerns the autonomous thermal control system of Nano-satellite with a study of the combination of MEMS (Micro Electro Mechanical Systems)-based efficient cooling technique and the agent-oriented intelligent control strategies issue, especially with the issue of autonomy. The particular interest and complexity are the development of the thermal control system that has the intelligent autonomous control capability to maintain the Nano-satellite optimal performance. Based on the modeling and analysis the dynamic characteristics of efficient cooling system utilizing MEMS-based micro-channel heat sink and micro louver arrays by the means of lumped parameter method, the hierarchical structure of agent-oriented intelligent control method for Nano-satellite autonomous thermal system is investigated. With the domain expert knowledge and Fuzzy reasoning, the innovative agent-oriented intelligent control strategies achieve an autonomous process to regulate several controlled variables according to the expectation of the system situations and variety tendency, the robustness of the thermal control system is accomplished as well.