Wu Wen

Di Wu, Yuman Bai, Shoupeng Ren et al.

Centralized stablecoins such as USDT and USDC enforce financial sanctions through contract-layer blacklist functions, yet on public blockchains a freeze is merely an ordinary transaction that must compete for execution priority. We identify a fundamental gap between contract-layer authority and consensus-layer enforcement: when a sanctioned entity's transfer and the issuer's freeze race for inclusion in the same block, the outcome is determined not by regulatory mandate but by the economically motivated ordering decisions of block producers. We term the resulting value extraction Sanction-Evasion MEV (SE-MEV). To quantify this vulnerability, we construct the first comprehensive dataset of on-chain sanctions enforcement and evasion for Ethereum-based USDC and USDT (Nov 2017-Aug 2025), covering over $1.5 billion in frozen assets. We find that 7.3% of sanctioned USDT addresses and 18.7% of sanctioned USDC addresses were drained to zero balances before enforcement took effect, and document a clear escalation trajectory-from issuer-side out-of-gas failures, to public gas auctions, to private order flow, to direct proposer bribery. We further develop a game-theoretic model that yields three results: (i) compliant issuers cannot rationally stay outside the MEV market; (ii) fixed participation costs concentrate evasion among specialized, MEV-aware actors; and (iii) the implicit MEV tax extracted by block proposers grows without bound as regulatory penalties intensify, creating structural incentives for issuers to vertically integrate into block-building infrastructure. Our findings demonstrate that on any blockchain where ordering power is allocated by economic incentives, ordering power is sanctioning power-and contract-level authority alone cannot guarantee enforcement.

Junjie Xu, Xingjiao Wu, Tanren Yao et al.

Emotional information is essential for enhancing human-computer interaction and deepening image understanding. However, while deep learning has advanced image recognition, the intuitive understanding and precise control of emotional expression in images remain challenging. Similarly, music research largely focuses on theoretical aspects, with limited exploration of its emotional dimensions and their integration with visual arts. To address these gaps, we introduce EmoMV, an emotion-driven music-to-visual manipulation method that manipulates images based on musical emotions. EmoMV combines bottom-up processing of music elements-such as pitch and rhythm-with top-down application of these emotions to visual aspects like color and lighting. We evaluate EmoMV using a multi-scale framework that includes image quality metrics, aesthetic assessments, and EEG measurements to capture real-time emotional responses. Our results demonstrate that EmoMV effectively translates music's emotional content into visually compelling images, advancing multimodal emotional integration and opening new avenues for creative industries and interactive technologies.

An Guo, Shuoxiao Zhang, Enyi Tang et al.

With the tremendous advancement of deep learning and communication technology, Vehicle-to-Everything (V2X) cooperative perception has the potential to address limitations in sensing distant objects and occlusion for a single-agent perception system. V2X cooperative perception systems are software systems characterized by diverse sensor types and cooperative agents, varying fusion schemes, and operation under different communication conditions. Therefore, their complex composition gives rise to numerous operational challenges. Furthermore, when cooperative perception systems produce erroneous predictions, the types of errors and their underlying causes remain insufficiently explored. To bridge this gap, we take an initial step by conducting an empirical study of V2X cooperative perception. To systematically evaluate the impact of cooperative perception on the ego vehicle's perception performance, we identify and analyze six prevalent error patterns in cooperative perception systems. We further conduct a systematic evaluation of the critical components of these systems through our large-scale study and identify the following key findings: (1) The LiDAR-based cooperation configuration exhibits the highest perception performance; (2) Vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication exhibit distinct cooperative perception performance under different fusion schemes; (3) Increased cooperative perception errors may result in a higher frequency of driving violations; (4) Cooperative perception systems are not robust against communication interference when running online. Our results reveal potential risks and vulnerabilities in critical components of cooperative perception systems. We hope that our findings can better promote the design and repair of cooperative perception systems.