Tootfinder

Meta-Metrics and Best Practices for System-Level Inference Performance Benchmarking
Shweta Salaria, Zhuoran Liu, Nelson Mimura Gonzalez
https://arxiv.org/abs/2508.10251 https://…

Meta-Metrics and Best Practices for System-Level Inference Performance Benchmarking
Benchmarking inference performance (speed) of Foundation Models such as Large Language Models (LLM) involves navigating a vast experimental landscape to understand the complex interactions between hardware and software components. However, evaluating every possible test configuration is impractical, unfeasible and unnecessary. To address this challenge, we introduce FMwork, a comprehensive and methodical approach to creating a controlled testing environment that accurately reflects and characte…

Quantifying Resolution Limits in Pedestal Profile Measurements with Gaussian Process Regression
Norman M. Cao, David R. Hatch, Craig Michoski, Todd A. Oliver, David Eldon, Andrew Oakleigh Nelson, Matthew Waller
https://arxiv.org/abs/2507.05067

Quantifying Resolution Limits in Pedestal Profile Measurements with Gaussian Process Regression
Edge transport barriers (ETBs) in magnetically confined fusion plasmas, commonly known as pedestals, play a crucial role in achieving high confinement plasmas. However, their defining characteristic, a steep rise in plasma pressure over short length scales, makes them challenging to diagnose experimentally. In this work, we use Gaussian Process Regression (GPR) to develop first-principles metrics for quantifying the spatiotemporal resolution limits of inferring differentiable profiles of temper…

Counting linear congruence systems with a fixed number of solutions
Marcus Nilsson
https://arxiv.org/abs/2507.04688 https://arxiv.org…

Counting linear congruence systems with a fixed number of solutions
For a prime p and a positive integer s consider a homogeneous linear system over the ring Z_p^s (the ring of integers modulo p^s) described by an n x m-matrix. The possible number of solutions to such a system is p^j, where j=0,1,..., sm. We study the problem of how many n x m -matrices over Z_p^s there are given that we have exactly p^j homogeneous solutions. For the case s=1 (when Z_p^s is a field) George von Landsberg proved a general formula in 1893. However, there seems to be few published…

Countering Privacy Nihilism
Severin Engelmann, Helen Nissenbaum
https://arxiv.org/abs/2507.18253 https://arxiv.org/pdf/2507.18253

Countering Privacy Nihilism
Of growing concern in privacy scholarship is artificial intelligence (AI), as a powerful producer of inferences. Taken to its limits, AI may be presumed capable of inferring "everything from everything," thereby making untenable any normative scheme, including privacy theory and privacy regulation, which rests on protecting privacy based on categories of data - sensitive versus non-sensitive, private versus public. Discarding data categories as a normative anchoring in privacy and data protecti…

„Sinnloses Töten“ - Selenskyj zeigt persönlichen Brief von Trump #News #Nachrichten …

Skyrmion Lattice Domain Formation in a Non-Flat Energy Landscape
Raphael Gruber, Jan Roth\"orl, Simon M. Fr\"ohlich, Maarten A. Brems, Tobias Sparmann, Fabian Kammerbauer, Maria-Andromachi Syskaki, Elizabeth M. Jefremovas, Sachin Krishnia, Asle Sudb{\o}, Peter Virnau, Mathias Kl\"aui
https://arxiv.org/abs/2508.12988

Skyrmion Lattice Domain Formation in a Non-Flat Energy Landscape
Magnetic skyrmions are chiral spin structures with non-trivial topology that comprise two-dimensional quasi-particles and are promising information carriers for data storage and processing devices. Skyrmion lattices in magnetic thin films exhibit Kosterlitz-Thouless-Halperin-Nelson-Young (KTHNY) phase transitions and have garnered significant interest for studying emergent 2D phase behavior. In experimental skyrmion lattices, the main factor limiting the quasi-long-range order in thin films has…

Granulation signatures in 3D hydrodynamical simulations: evaluating background model performance using a Bayesian nested sampling framework
Jens R. Larsen, Mia S. Lundkvist, Guy R. Davies, Martin B. Nielsen, Hans-G\"unter Ludwig, Yixiao Zhao, Luisa F. Rodr\'iguez D\'iaz, Hans Kjeldsen
https://arxiv.org/abs/2507.1169…

Granulation signatures in 3D hydrodynamical simulations: evaluating background model performance using a Bayesian nested sampling framework
Understanding the granulation background signal is of vital importance when interpreting the asteroseismic diagnostics of solar-like oscillators. Various descriptions exist in the literature for modelling the surface manifestation of convection, the choice of which affects our interpretations. We aim to evaluate the performance of and preference for various granulation background models for a suite of 3D hydrodynamical simulations of convection across the HR diagram, thereby expanding the numbe…

RNAMunin: A Deep Machine Learning Model for Non-coding RNA Discovery
Lauren Lui, Torben Nielsen
https://arxiv.org/abs/2507.11950 https://

RNAMunin: A Deep Machine Learning Model for Non-coding RNA Discovery
Functional annotation of microbial genomes is often biased toward protein-coding genes, leaving a vast, unexplored landscape of non-coding RNAs (ncRNAs) that are critical for regulating bacterial and archaeal physiology, stress response and metabolism. Identifying ncRNAs directly from genomic sequence is a paramount challenge in bioinformatics and biology, essential for understanding the complete regulatory potential of an organism. This paper presents RNAMunin, a machine learning (ML) model th…