A distribution-based adversarial attack generates quality-preserving adversarial motions for skeleton action recognition without noise perturbations, outperforming prior methods in success rate and naturalness on two datasets via a new human-aligned quality metric.
On the design fundamentals of diffusion models: A survey
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A multiscale optimization method using explicit protein backbone geometry reconstructs atomic models from cryo-EM data, showing improved RMSD and TM scores on three simulated datasets.
A diffusion-based generative ML paradigm is introduced to proactively generate and rank high-risk contingencies for voltage stability using physical information from operating points, with experiments on IEEE-6 to IEEE-118 systems.
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Quality-Preserving Imperceptible Adversarial Attack on Skeleton-based Human Action Recognition
A distribution-based adversarial attack generates quality-preserving adversarial motions for skeleton action recognition without noise perturbations, outperforming prior methods in success rate and naturalness on two datasets via a new human-aligned quality metric.
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Multiscale reconstruction of protein conformations from cryo-EM images
A multiscale optimization method using explicit protein backbone geometry reconstructs atomic models from cryo-EM data, showing improved RMSD and TM scores on three simulated datasets.
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A Diffusion-based Generative Machine Learning Paradigm for Dynamic Contingency Screening
A diffusion-based generative ML paradigm is introduced to proactively generate and rank high-risk contingencies for voltage stability using physical information from operating points, with experiments on IEEE-6 to IEEE-118 systems.