Tootfinder

Here are the slides from my presentation at the IASSIST/CARTO conference last week.
The title is "Reproducibility through Identifiers: Persistent Identifiers (PIDs) for research facilities and instruments" but I talked about toasters and link to a novella written by Cory Doctorow (@…)

This https://arxiv.org/abs/2406.01627 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

GenBench: A Benchmarking Suite for Systematic Evaluation of Genomic Foundation Models
The Genomic Foundation Model (GFM) paradigm is expected to facilitate the extraction of generalizable representations from massive genomic data, thereby enabling their application across a spectrum of downstream applications. Despite advancements, a lack of evaluation framework makes it difficult to ensure equitable assessment due to experimental settings, model intricacy, benchmark datasets, and reproducibility challenges. In the absence of standardization, comparative analyses risk becoming b…

Getting More Out of Black Hole Superradiance: a Statistically Rigorous Approach to Ultralight Boson Constraints
Sebastian Hoof, David J. E. Marsh, J\'ulia Sisk-Reyn\'es, James H. Matthews, Christopher Reynolds
https://arxiv.org/abs/2406.10337

Getting More Out of Black Hole Superradiance: a Statistically Rigorous Approach to Ultralight Boson Constraints
Black hole (BH) superradiance can provide strong constraints on the properties of ultralight bosons (ULBs). Since most of the previous work has focused on the theoretical predictions, here we investigate the most suitable statistical framework to constrain ULB masses and self-interactions. We argue that a Bayesian approach provides a clear statistical interpretation, deals with limitations regarding the reproducibility of existing BH analyses, incorporates the full information from BH data, and…

Stable network inference in high-dimensional graphical model using single-linkage
Emilie Devijver (APTIKAL), R\'emi Molinier (IF), M\'elina Gallopin (I2BC)
https://arxiv.org/abs/2406.09865

Stable network inference in high-dimensional graphical model using single-linkage
Stability, akin to reproducibility, is crucial in statistical analysis. This paper examines the stability of sparse network inference in high-dimensional graphical models, where selected edges should remain consistent across different samples. Our study focuses on the Graphical Lasso and its decomposition into two steps, with the first step involving hierarchical clustering using single linkage.We provide theoretical proof that single linkage is stable, evidenced by controlled distances between…

Getting More Out of Black Hole Superradiance: a Statistically Rigorous Approach to Ultralight Boson Constraints
Sebastian Hoof, David J. E. Marsh, J\'ulia Sisk-Reyn\'es, James H. Matthews, Christopher Reynolds
https://arxiv.org/abs/2406.10337

Getting More Out of Black Hole Superradiance: a Statistically Rigorous Approach to Ultralight Boson Constraints
Black hole (BH) superradiance can provide strong constraints on the properties of ultralight bosons (ULBs). Since most of the previous work has focused on the theoretical predictions, here we investigate the most suitable statistical framework to constrain ULB masses and self-interactions. We argue that a Bayesian approach provides a clear statistical interpretation, deals with limitations regarding the reproducibility of existing BH analyses, incorporates the full information from BH data, and…