Tootfinder

Solving Package Management via Hypergraph Dependency Resolution
Ryan Gibb, Patrick Ferris, David Allsopp, Michael Winston Dales, Mark Elvers, Thomas Gazagnaire, Sadiq Jaffer, Thomas Leonard, Jon Ludlam, Anil Madhavapeddy
https://arxiv.org/abs/2506.10803

Solving Package Management via Hypergraph Dependency Resolution
Package managers are everywhere, with seemingly every language and operating system implementing their own solution. The lack of interoperability between these systems means that multi-lingual projects are unable to express precise dependencies across language ecosystems, and external system and hardware dependencies are typically implicit and unversioned. We define HyperRes, a formal system for describing versioned dependency resolution using a hypergraph that is expressive enough to model man…

Benchmarking Waitlist Mortality Prediction in Heart Transplantation Through Time-to-Event Modeling using New Longitudinal UNOS Dataset
Yingtao Luo, Reza Skandari, Carlos Martinez, Arman Kilic, Rema Padman
https://arxiv.org/abs/2507.07339

Benchmarking Waitlist Mortality Prediction in Heart Transplantation Through Time-to-Event Modeling using New Longitudinal UNOS Dataset
Decisions about managing patients on the heart transplant waitlist are currently made by committees of doctors who consider multiple factors, but the process remains largely ad-hoc. With the growing volume of longitudinal patient, donor, and organ data collected by the United Network for Organ Sharing (UNOS) since 2018, there is increasing interest in analytical approaches to support clinical decision-making at the time of organ availability. In this study, we benchmark machine learning models …