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EnergyMamba proposes a novel spatiotemporal framework combining a graph-enhanced selective state space model and adaptive conformalized quantile regression for accurate and reliable energy consumption prediction with uncertainty estimates, achieving improvements on real-world datasets from Florida, New York, and California.
CHAM-net introduces a contrastive hierarchical adaptive meta-network that captures site-specific and cross-year dynamics for robust global methane flux prediction, outperforming baseline methods on simulation and observational datasets.
Investigates neural integral-operator-based models for fMRI encoding and decoding tasks, focusing on the role of nonlocal spatiotemporal context and showing that larger temporal windows improve performance across datasets.