SRast is a generalist framework using self-supervised decoupling of gene and spatial representations plus flow matching for physically consistent super-resolution of spatial transcriptomics data with strong zero-shot generalization.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
The paper identifies seven asymmetries in access to AI evidence and proposes a three-part test for courts to resolve disclosure disputes using proportionality and reasonable alternatives.
A framework models proxy-primary outcome discrepancies as random effects at the parameter level, estimated from aggregated historical observations to calibrate inferences under distribution shifts.
citing papers explorer
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Towards Universal Spatial Transcriptomics Super-Resolution: A Generalist Physically Consistent Flow Matching Framework
SRast is a generalist framework using self-supervised decoupling of gene and spatial representations plus flow matching for physically consistent super-resolution of spatial transcriptomics data with strong zero-shot generalization.
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Barriers to Evidence in AI-Related Cases and the Privatization of Proof
The paper identifies seven asymmetries in access to AI evidence and proposes a three-part test for courts to resolve disclosure disputes using proportionality and reasonable alternatives.
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Estimate Level Adjustment For Inference With Proxies Under Random Distribution Shifts
A framework models proxy-primary outcome discrepancies as random effects at the parameter level, estimated from aggregated historical observations to calibrate inferences under distribution shifts.