SWORD detects change points in dynamic graphs by averaging Chebyshev moments of the normalized Laplacian over two time windows and using L1 distance, improving mean F1 from 0.27 to 0.79 over prior spectral methods on real benchmarks.
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EAPFusion uses self-evolving intrinsic priors to produce dynamic, scene-adaptive convolution kernels and channel-mixing fusion for infrared-visible images, reporting state-of-the-art results and downstream gains.
New knot classification benchmark and topology-aware supervision methods yield small specificity gains but confirm that appearance bias remains the dominant failure mode.
Semantic-aware random convolution and intensity-based source matching enable effective single-source domain generalization for medical image segmentation, outperforming prior methods and sometimes matching in-domain performance.
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Semantic-aware Random Convolution and Source Matching for Domain Generalization in Medical Image Segmentation
Semantic-aware random convolution and intensity-based source matching enable effective single-source domain generalization for medical image segmentation, outperforming prior methods and sometimes matching in-domain performance.