This work was done while M. Menart was at The Ohio State University.

Surgical video-language pretraining (VLP) faces unique challenges due to the knowledge domain gap and the scarcity of multi-modal data.

Only four bottles of Asahi Super Dry beer are left on the shelves of Ben Thai, a cosy restaurant in the Tokyo suburb of Sengawacho. Its owner, Sakaolath Sugizaki, expects to get a few more soon, but she says her supplier is keeping the bulk of its stock for bigger customers. That's because Asahi, the maker of Japan's best-selling beer, was forced to halt production at most of its 30 factories in the country at the end of last month after being hit by a cyber-attack. While all of its facilities in Japan - including six breweries - have now partially reopened, its computer systems are still down. That means it has to process orders and shipments manually - using pen, paper and fax machines - resulting in much fewer shipments than before the attack.

Focusing learning on these relevant actions can significantly improve training efficiency and effectiveness.

Unsupervised Graph Domain Adaptation (UGDA) involves the transfer of knowledge from a label-rich source graph to an unlabeled target graph under domain discrepancies. Despite the proliferation of methods designed for this emerging task, the lack of standard experimental settings and fair performance comparisons makes it challenging to understand which and when models perform well across different scenarios.

In addition to sequence-based approaches, structure-based encoders have emerged to leverage the 3D structural information of proteins.