Topology-Aware Self-Training Framework Improves Semi-Supervised 3D Shape Segmentation
Researchers developed a topology-aware multi-task self-training framework that mitigates pseudo-label noise and improves semi-supervised 3D shape segmentation.
Researchers developed a topology-aware multi-task self-training framework that mitigates pseudo-label noise and improves semi-supervised 3D shape segmentation.
Penn State professor Mitchell P. Smith's new book examines how identity-based perceptions drive political divides in the United States and ...
A new case report describes how anchoring and confirmation bias led clinicians to treat a teenager's massive germ cell tumor ...
Simulations of thousands of large language model agents across five backbone models show that AI societies spontaneously form homophilous clusters ...
Researchers have developed SRRI, a source-free domain adaptation method that uses CLIP as an external verifier to de-bias pseudo-source domains ...
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© 2026 Scienmag - Science Magazine