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Cluster Aggregated GAN (CAG): A Cluster-Based Hybrid Model for Appliance Pattern Generation
arXiv:2512.22287v4 Announce Type: replace Abstract: Synthetic appliance data are essential for developing non-intrusive load monitoring algorithms and enabling privacy preserving energy research, yet the scarcity of labeled datasets remains a significant barrier. Recent GAN-based methods have demonstrated the feasibility of…
Open datasets and machine learning for two-phase heat transfer: a review following a spatial-temporal taxonomy
arXiv:2605.23037v2 Announce Type: replace Abstract: Two-phase heat transfer underpins boiling, condensation, immersion cooling, flow boiling, energy conversion, and electronics thermal management, but its coupled interfacial physics make data reuse and model comparison difficult. This narrative review synthesizes open datasets, machine-learning…
SW-ProxyCE: Zero-Query Adversarial Transfer from Public EEG Encoders to Private Downstream Models
arXiv:2608.16931v1 Announce Type: new Abstract: Electroencephalography (EEG) foundation models have recently emerged as a promising paradigm for EEG decoding by learning reusable representations from large-scale heterogeneous neural recordings. However, the open release of EEG foundation encoders, while facilitating downstream developments,…
Diffusion Models for Smarter UAVs: Decision-Making and Modeling
arXiv:2501.05819v2 Announce Type: replace Abstract: Uncrewed Aerial Vehicles (UAVs) are increasingly used in modern communication networks. However, challenges in decision-making and digital modeling continue to hinder their rapid development. Reinforcement Learning (RL) algorithms face limitations such as low sample efficiency…