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This paper presents a latent dynamical model using a heart-rate-aware neural ODE and graph-based mesh autoencoder to model full-cycle ventricular motion from cine cardiac MRI. Applied to 72,386 UK Biobank participants, the model improves heart failure risk prediction over conventional cardiac markers.
Proposes a unified risk map modeling framework for autonomous driving that integrates traffic flow and collision risks in partially observable environments, using spatiotemporal modeling and diffusion-based scenario generation. Outperforms state-of-the-art occlusion-aware baselines on the Waymo Open Motion Dataset.