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Findings show that foundation models require CGM-specific adaptation for reliable forecasting and that dietary context provides clinically meaningful signals beyond CGM alone, especially during postprandial periods.
FusionVul outperforms traditional methods by integrating syntactic and structural insights, achieving unprecedented accuracy in detecting complex code vulnerabilities.
Gaze estimation models can now achieve comparable accuracy with 80-95% less labeled data, thanks to a semi-supervised approach that disentangles gaze components and learns robust representations via contrastive learning.
Achieve state-of-the-art sparse-view CT reconstruction with a diffusion model that generates visually consistent textures using fewer sampling steps, mitigating inherent randomness.
Ditch the RNNs and attention: PFGNet's frequency-guided gating achieves SOTA spatiotemporal prediction with a fully convolutional architecture, slashing parameters and FLOPs.
Diffusion models can learn surprisingly generalizable anatomical representations from unlabeled MRI data, enabling accurate multi-task diagnosis and segmentation across different joints and imaging conditions, even with limited labeled data.
