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The exact identity that characterizes how accepted-state violations accumulate in the controller's multipliers is derived, and the full system leads every ablation variant in target quality, property-adjusted performance, and Pareto hypervolume.
This paper proposes BioWave, a multi-hop diffusion-wavelet model that learns both structural and semantic representations for accurate CMI prediction and achieves superior performance compared to state-of-the-art baselines, particularly in zero-shot settings.
Continual spatio-temporal forecasting supports traffic management and environmental monitoring under evolving dynamics and expanding sensor networks. However, conventional graph-based continual learning methods tie forecasting representations to the current sensor layout, so sensor expansion can alter the representatio...
ScGraphTrans, a graph neural network framework that integrates functional state pseudo-labels, graph structure learning, and graph domain adaptation to improve both cell type annotation and communication inference, is proposed, offering insights into biomarker discovery and therapeutic strategies.
Experiments on three cross-patient scRNA-seq data sets demonstrate that PathoGraph achieves stable annotation performance across 32 directed reference-to-query transfer tasks, showing competitive and stable performance compared with representative marker-based, correlation-based, and model-based annotation methods.
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