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This paper proposes FaStR, a method that factorizes the transition kernel in reinforcement learning using CP decomposition into separate state, action, and next-state encoders, improving sample efficiency especially in high-dimensional locomotion tasks.
I²RiMA is a novel intra-inter Riemannian manifold attention network for EEG-based mental stress detection. It constructs frequency-specific spatial covariance and uses temporal attention to improve cross-subject stress classification, achieving up to 82.78% balanced accuracy.