The first systematic review of routine computing synthesizes literature into a taxonomy of temporal, behavioral, cognitive, and variability aspects, outlining applications in health, accessibility, and adaptive support along with persistent challenges.
Hierarchical Amortized GAN for 3D High Resolution Medical Image Synthesis
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Diffusion models reconstruct high-resolution 3D cardiac ultrasound volumes from heavily undersampled elevation planes and outperform traditional interpolation and supervised deep learning baselines.
Resampling clinical time series into uniform bins for offline RL reduces performance by up to 60% and causes retrospective evaluations to overestimate returns by 1.5-3x versus unprocessed data.
FedTGNN-SS delivers strong AUROC on GDM and related datasets even at 80% label missingness per site by combining local k-NN graphs, adaptive GNNs, prototype pseudo-labeling, and privacy-safe centroid sharing.
MetaboNet is a consolidated dataset of 3135 subjects with 1228 patient-years of CGM and insulin pump data for Type 1 Diabetes research.
Mixture-of-experts fusing multiple pretrained forecasters achieves strongest performance on influenza time series, with pretraining gains largest at longer horizons when domain-aligned and LLM methods underperforming.
C2GA uses conditional VQ-VAE with decoupled local tokens and global class prototypes plus a Transformer prior to generate high-fidelity label-consistent Mel-spectrograms for respiratory sound data augmentation.
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High Volume Rate 3D Ultrasound Reconstruction with Diffusion Models
Diffusion models reconstruct high-resolution 3D cardiac ultrasound volumes from heavily undersampled elevation planes and outperform traditional interpolation and supervised deep learning baselines.