Tootfinder

Unsupervised Ground Metric Learning
Janis Auffenberg, Jonas Bresch, Oleh Melnyk, Gabriele Steidl
https://arxiv.org/abs/2507.13094 https://

Unsupervised Ground Metric Learning
Data classification without access to labeled samples remains a challenging problem. It usually depends on an appropriately chosen distance between features, a topic addressed in metric learning. Recently, Huizing, Cantini and Peyré proposed to simultaneously learn optimal transport (OT) cost matrices between samples and features of the dataset. This leads to the task of finding positive eigenvectors of a certain nonlinear function that maps cost matrices to OT distances. Having this basic ide…

Gender Differences in International Research Collaboration in European Union
Elsa Fontainha, Tanya Ara\'ujo
https://arxiv.org/abs/2507.00619 https://…

Gender Differences in International Research Collaboration in European Union
This paper investigates International Research Collaboration (IRC) among European Union (EU) countries from 2011 to 2022, with emphasis on gender-based authorship patterns. Drawing from the Web of Science Social Science Citation Index (WoS-SSCI) database, a large dataset of IRC articles was constructed, annotated with categories of authorship based on gender, author affiliation, and COVID-19 subject as topic. Using network science, the study maps collaboration structures and reveals gendered di…