SkyPart achieves state-of-the-art single-pass cross-view geo-localization on SUES-200, University-1652, and DenseUAV by using prototype-based part discovery, altitude-conditioned modulation, and Kendall-weighted loss, with widening gains under weather corruptions.
Vision-based UAV self-positioning in low-altitude urban environments
4 Pith papers cite this work. Polarity classification is still indexing.
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The study decodes multi-chain transactions in U.S. Treasury RWAs and introduces a curvature-aware representation learning model that infers address roles such as institutional, retail, or bot with better performance than baselines.
Empirical analysis of 1.07 billion Ethereum transactions shows sanctions cut Tornado Cash deposits by 71% yet the mixer remained central to most security incidents, exposing three structural enforcement weaknesses.
SLEID combines Isolation Forest and iterative self-training to detect illicit accounts in large-scale Ethereum DeFi transactions, achieving better precision and F1 than baselines while using less labeled data.
citing papers explorer
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Weather-Robust Cross-View Geo-Localization via Prototype-Based Semantic Part Discovery
SkyPart achieves state-of-the-art single-pass cross-view geo-localization on SUES-200, University-1652, and DenseUAV by using prototype-based part discovery, altitude-conditioned modulation, and Kendall-weighted loss, with widening gains under weather corruptions.
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Decoding RWA Tokenized U.S. Treasuries: Functional Dissection and Address Role Inference
The study decodes multi-chain transactions in U.S. Treasury RWAs and introduces a curvature-aware representation learning model that infers address roles such as institutional, retail, or bot with better performance than baselines.
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Evasion Under Blockchain Sanctions
Empirical analysis of 1.07 billion Ethereum transactions shows sanctions cut Tornado Cash deposits by 71% yet the mixer remained central to most security incidents, exposing three structural enforcement weaknesses.
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Leveraging Ensemble-Based Semi-Supervised Learning for Illicit Account Detection in Ethereum DeFi Transactions
SLEID combines Isolation Forest and iterative self-training to detect illicit accounts in large-scale Ethereum DeFi transactions, achieving better precision and F1 than baselines while using less labeled data.