Tootfinder

Dolphins' Tyreek Hill explains his unique reaction to graphic knee injury and why he's unsure of NFL future

https://www.cbssports.com/nfl/news/dolp…

TF-DWGNet: A Directed Weighted Graph Neural Network with Tensor Fusion for Multi-Omics Cancer Subtype Classification
Tiantian Yang, Zhiqian Chen
https://arxiv.org/abs/2509.16301

TF-DWGNet: A Directed Weighted Graph Neural Network with Tensor Fusion for Multi-Omics Cancer Subtype Classification
Integration and analysis of multi-omics data provide valuable insights for cancer subtype classification. However, such data are inherently heterogeneous, high-dimensional, and exhibit complex intra- and inter-modality dependencies. Recent advances in graph neural networks (GNNs) offer powerful tools for modeling such structure. Yet, most existing methods rely on prior knowledge or predefined similarity networks to construct graphs, which are often undirected or unweighted, failing to capture t…

On the Parallel Complexity of Identifying Groups and Quasigroups via Decompositions
Dan Johnson, Michael Levet, Petr Vojt\v{e}chovsk\'y, Brett Widholm
https://arxiv.org/abs/2508.06478

On the Parallel Complexity of Identifying Groups and Quasigroups via Decompositions
In this paper, we investigate the computational complexity of isomorphism testing for finite groups and quasigroups, given by their multiplication tables. We crucially take advantage of their various decompositions to show the following: - We first consider the class $\mathcal{C}$ of groups that admit direct product decompositions, where each indecompsable factor is $O(1)$-generated, and either perfect or centerless. We show any group in $\mathcal{C}$ is identified by the $O(1)$-dimensional c…

Behind the Blog: Exercises in OSINT and Storage Pains https://www.404media.co/behind-the-blog-exercises-in-osint-and-storage-pains/

Restrictions of PCBNs for integration-free computations
Alexis Derumigny, Niels Horsman, Dorota Kurowicka
https://arxiv.org/abs/2510.03518 https://arxiv.or…

Restrictions of PCBNs for integration-free computations
The pair-copula Bayesian Networks (PCBN) are graphical models composed of a directed acyclic graph (DAG) that represents (conditional) independence in a joint distribution. The nodes of the DAG are associated with marginal densities, and arcs are assigned with bivariate (conditional) copulas following a prescribed collection of parental orders. The choice of marginal densities and copulas is unconstrained. However, the simulation and inference of a PCBN model may necessitate possibly high-dimen…

Component groups for non-supercuspidal $L$-parameters for $p$-adic $\mathrm{SL}_3$
Kwangho Choiy, Doyon Kim, Razan Taha, Pan Yan
https://arxiv.org/abs/2507.20946 https://…

Component groups for non-supercuspidal $L$-parameters for $p$-adic $\mathrm{SL}_3$
We explicitly classify all the component groups associated to the non-supercuspidal, tempered $L$-parameters of $\mathrm{SL}_3(F)$ for a $p$-adic field $F$ of characteristic $0$ by direct case-by-case computations in $\mathrm{PGL}_3(\mathbb{C})$, following earlier work for $\mathrm{SL}_2$ by Labesse-Langlands and Shelstad.

Desiderata for a biomedical knowledge network: opportunities, challenges and future Directions
Chunlei Wu, Hongfang Liu, Jason Flannick, Mark A. Musen, Andrew I. Su, Lawrence Hunter, Thomas M. Powers, Cathy H. Wu
https://arxiv.org/abs/2509.22424

Desiderata for a biomedical knowledge network: opportunities, challenges and future Directions
Knowledge graphs, collectively as a knowledge network, have become critical tools for knowledge discovery in computable and explainable knowledge systems. Due to the semantic and structural complexities of biomedical data, these knowledge graphs need to enable dynamic reasoning over large evolving graphs and support fit-for-purpose abstraction, while establishing standards, preserving provenance and enforcing policy constraints for actionable discovery. A recent meeting of leading scientists di…