Tootfinder

Training Transformers for Mesh-Based Simulations
Paul Garnier, Vincent Lannelongue, Jonathan Viquerat, Elie Hachem
https://arxiv.org/abs/2508.18051 https://

Training Transformers for Mesh-Based Simulations
Simulating physics using Graph Neural Networks (GNNs) is predominantly driven by message-passing architectures, which face challenges in scaling and efficiency, particularly in handling large, complex meshes. These architectures have inspired numerous enhancements, including multigrid approaches and $K$-hop aggregation (using neighbours of distance $K$), yet they often introduce significant complexity and suffer from limited in-depth investigations. In response to these challenges, we propose a…

VoxGuard: Evaluating User and Attribute Privacy in Speech via Membership Inference Attacks
Efthymios Tsaprazlis, Thanathai Lertpetchpun, Tiantian Feng, Sai Praneeth Karimireddy, Shrikanth Narayanan
https://arxiv.org/abs/2509.18413

Large $n$-point Functions in Resonant Inflation
Paolo Creminelli, S\'ebastien Renaux-Petel, Giovanni Tambalo, Vicharit Yingcharoenrat
https://arxiv.org/abs/2508.19240 https:…

Large $n$-point Functions in Resonant Inflation
We investigate a qualitatively new regime of inflationary models with small and rapid oscillations in the potential--resonant non-Gaussianity. In contrast to the standard scenario, where most of the observable information is encoded in the power spectrum, in this regime the oscillatory signal predominantly appears in higher-order correlation functions with large $n$. This behavior emerges when the oscillation frequency $ω$ exceeds the naive cutoff of the theory, $4πf$. However, as noted by Ho…

Automated discovery of finite volume schemes using Graph Neural Networks
Paul Garnier, Jonathan Viquerat, Elie Hachem
https://arxiv.org/abs/2508.19052 https://

Automated discovery of finite volume schemes using Graph Neural Networks
Graph Neural Networks (GNNs) have deeply modified the landscape of numerical simulations by demonstrating strong capabilities in approximating solutions of physical systems. However, their ability to extrapolate beyond their training domain (\textit{e.g.} larger or structurally different graphs) remains uncertain. In this work, we establish that GNNs can serve purposes beyond their traditional role, and be exploited to generate numerical schemes, in conjunction with symbolic regression. First, …

》give man stew he eat one day. teach man to gay son he stew forever

Gwyneth Paltrow är kvinnornas Joe Rogan.
Susanne Ljung: Kaffelavemang är inte vackert https://www.dn.se/kultur/susanne-ljung-kaffelavemang-ar-inte-vackert/

Susanne Ljung: Kaffelavemang är inte vackert
Det mesta går att sälja in med sval elegans och känslan av lyx. Även kaffelavemang. Gwyneth Paltrow är kvacksalveriets okrönta drottning.

This CEO Sean Cook guy sounds like a Grade-A alpha scumbag.
Original post: https://bsky.app/profile/did:plc:vk7rduhvom3rq6dyluce5wzf/post/3lwr3pyqats25

George Pickens 2025 Highlights (So Far) | Dallas Cowboys 2025 https://www.youtube.com/watch?v=Vxroho1IDHU

ViScratch: Using Large Language Models and Gameplay Videos for Automated Feedback in Scratch
Yuan Si, Daming Li, Hanyuan Shi, Jialu Zhang
https://arxiv.org/abs/2509.11065 https:…

ViScratch: Using Large Language Models and Gameplay Videos for Automated Feedback in Scratch
Block-based programming environments such as Scratch are increasingly popular in programming education, in particular for young learners. While the use of blocks helps prevent syntax errors, semantic bugs remain common and difficult to debug. Existing tools for Scratch debugging rely heavily on predefined rules or user manual inputs, and crucially, they ignore the platform's inherently visual nature. We introduce ViScratch, the first multimodal feedback generation system for Scratch that leve…

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