PSP-HDC encodes a directed PSP graph into hyperdimensional vectors via a trainable encoder and graph-aligned operations to predict sheet-resistance regimes with 0.91 accuracy and intrinsic explanations.
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DyPro generates 12-step latent trajectory sequences via residual dynamic evolution from initial patient data to predict OS and DFS outcomes in CRLM.
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Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction
PSP-HDC encodes a directed PSP graph into hyperdimensional vectors via a trainable encoder and graph-aligned operations to predict sheet-resistance regimes with 0.91 accuracy and intrinsic explanations.