Tootfinder

Sam Altman says "intelligence and energy", the "limiters on human progress", will be abundant in the 2030s, and superintelligence should be widely distributed (Sam Altman)
https://blog.samaltman.com/the-gentle-singularity

Improving elliptic curve rank classification using multi-value and learned Mestre-Nagao sums
Zvonimir Bujanovi\'c, Matija Kazalicki, Domagoj Vlah
https://arxiv.org/abs/2506.07967

Improving elliptic curve rank classification using multi-value and learned Mestre-Nagao sums
Determining the rank of an elliptic curve E/Q remains a central challenge in number theory. Heuristics such as Mestre--Nagao sums are widely used to estimate ranks, but there is considerable room for improving their predictive power. This paper introduces two novel methods for enhancing rank classification using Mestre--Nagao sums. First, we propose a ``multi-value'' approach that simultaneously uses two distinct sums, S_0 and S_5, evaluated over multiple ranges. This multi-sum perspective sign…

This https://arxiv.org/abs/2504.01483 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGR_…

GarmageNet: A Multimodal Generative Framework for Sewing Pattern Design and Generic Garment Modeling
Realistic digital garment modeling remains a labor-intensive task due to the intricate process of translating 2D sewing patterns into high-fidelity, simulation-ready 3D garments. We introduce GarmageNet, a unified generative framework that automates the creation of 2D sewing patterns, the construction of sewing relationships, and the synthesis of 3D garment initializations compatible with physics-based simulation. Central to our approach is Garmage, a novel garment representation that encodes e…

Simultaneous Segmentation of Ventricles and Normal/Abnormal White Matter Hyperintensities in Clinical MRI using Deep Learning
Mahdi Bashiri Bawil, Mousa Shamsi, Abolhassan Shakeri Bavil
https://arxiv.org/abs/2506.07123

Simultaneous Segmentation of Ventricles and Normal/Abnormal White Matter Hyperintensities in Clinical MRI using Deep Learning
Multiple sclerosis (MS) diagnosis and monitoring rely heavily on accurate assessment of brain MRI biomarkers, particularly white matter hyperintensities (WMHs) and ventricular changes. Current segmentation approaches suffer from several limitations: they typically segment these structures independently despite their pathophysiological relationship, struggle to differentiate between normal and pathological hyperintensities, and are poorly optimized for anisotropic clinical MRI data. We propose a…

This https://arxiv.org/abs/2405.03437 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCE_…

pyCFS-data: Data Processing Framework in Python for openCFS
Many numerical simulation tools have been developed and are on the market, but there is still a strong need for appropriate tools capable of simulating multi-field problems, especially in aeroacoustics. Therefore, openCFS provides an open-source framework for implementing partial differential equations using the finite element method. Since 2000, the software has been developed continuously. The result is openCFS (before 2020, known as CFS++ Coupled Field Simulations written in C++). In this pa…

Simulation Everywhere: An Evolutionary Expansion of Discrete-Event Modeling and Simulation research and practice
Ikpe Justice Akpan, Godwin E. Etti
https://arxiv.org/abs/2506.05698

Simulation Everywhere: An Evolutionary Expansion of Discrete-Event Modeling and Simulation research and practice
Simulation was launched in the 1950s, nicknamed a tool of "last resort." Over the years, this Operations Research (OR) method has made significant progress, and utilizing the accelerated advances in computer science (hardware and software, processing speed, and advanced information visualization capabilities) to improve simulation usability in research and practice. After overcoming the initial obstacles and the scare of outliving its usefulness in the 2000s, computer simulation has remained a …

Physics-Informed Neural Operators for Generalizable and Label-Free Inference of Temperature-Dependent Thermoelectric Properties
Hyeonbin Moon, Songho Lee, Wabi Demeke, Byungki Ryu, Seunghwa Ryu
https://arxiv.org/abs/2506.08057

Physics-Informed Neural Operators for Generalizable and Label-Free Inference of Temperature-Dependent Thermoelectric Properties
Accurate characterization of temperature-dependent thermoelectric properties (TEPs), such as thermal conductivity and the Seebeck coefficient, is essential for reliable modeling and efficient design of thermoelectric devices. However, their nonlinear temperature dependence and coupled transport behavior make both forward simulation and inverse identification difficult, particularly under sparse measurement conditions. In this study, we develop a physics-informed machine learning approach that e…

Dark matter spiral arms in Milky Way-like halos: #DarkMatter trailing galaxy arms: https://arstechnica.com/science/2025/06/simulations-find-ghostly-whirls-of-dark-matter-trailing-galaxy-arms/

This https://arxiv.org/abs/2506.04120 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Splatting Physical Scenes: End-to-End Real-to-Sim from Imperfect Robot Data
Creating accurate, physical simulations directly from real-world robot motion holds great value for safe, scalable, and affordable robot learning, yet remains exceptionally challenging. Real robot data suffers from occlusions, noisy camera poses, dynamic scene elements, which hinder the creation of geometrically accurate and photorealistic digital twins of unseen objects. We introduce a novel real-to-sim framework tackling all these challenges at once. Our key insight is a hybrid scene represen…

Face to face with solar telescopes, meteorites and exoplanets: For the 6th Night of Science on June 21st 2025 in Göttingen, the MPI for Solar System Research is offering a full program of hands-on activities, lectures, guided tours, and unique exhibits.
Infos by MPS: https://www.mps.mpg.de/night-of-science-20