Tootfinder

Staggered time discretization in finitely-strained heterogeneous visco-elastodynamics with damage or diffusion in the Eulerian frame
Tom\'a\v{s} Roub\'i\v{c}ek
https://arxiv.org/abs/2510.10355 …

Staggered time discretization in finitely-strained heterogeneous visco-elastodynamics with damage or diffusion in the Eulerian frame
The semi-implicit (partly decoupled, also called staggered or fraction-step) time discretization is applied to compressible nonlinear dynamical models of viscoelastic solids in the Eulerian description, i.e.\ in the actual deforming configuration, formulated fully in terms of rates. The Kelvin-Voigt rheology and also, in the deviatoric part, the Jeffreys rheology are considered. The numerical stability and, considering the Stokes-type viscosity multipolar of the 2nd-grade, also convergence towa…

Picoflares in the Quiet Solar Corona: Solar Orbiter Observations Halfway to the Sun
O. Podladchikova, A. Warmuth, L. Harra, L. Dolla, C. Verbeeck, M. Mierla, L. Rodriguez, S. Parenti, F. Auchere, M. K. Georgoulis, S. J. Hofmeister, N. Engler, M. J. West, R. Aznar Cuadrado, A. M. Veronig, P. Antolin, S. Purkhart, D. M. Long, E. Buchlin, M. Haberreiter, M. Velli, A. N. Zhukov, H. Safari, U. Schuhle, L. Teriaca, A. F. Battaglia, E. Soubrie, V. Buchel, L. P. Chitta, S. Gissot, A. De Groof,…

Picoflares in the Quiet Solar Corona: Solar Orbiter Observations Halfway to the Sun
X-ray observations of the Sun led Eugene Parker to propose nanoflares as the basic energy-release units that heat the solar corona. Decades later, Solar Orbiter's Extreme Ultraviolet Imager (HRIEUV), operating halfway between Earth and the Sun, revealed thousands of even smaller brightenings in the quiet corona - tiny "campfires" that are smaller and far more frequent than the fundamental nanoflares observed from 1 AU. We analyze over 12,000 of these events, deriving their thermal energies us…

The Green Bank Ammonia Survey: Data Release 2
Jaime E. Pineda, Rachel K. Friesen, Erik Rosolowsky, Ana Chac\'on-Tanarro, Michael Chun-Yuan Chen, James Di Francesco, Helen Kirk, Anna Punanova, Youngmin Seo, Yancy Shirley, Adam Ginsburg, Stella S. R. Offner, Ayush Pandhi, Ayushi Singh, Feiyu Quan, H\'ector G. Arce, Paola Caselli, Spandan Choudhury, Alyssa A. Goodman, Fabian Heitsch, Peter G. Martin, Christopher D. Matzner, Philip C. Myers, Elena Redaelli, Samantha Scibelli

The Green Bank Ammonia Survey: Data Release 2
We present an overview of the final data release (DR2) from the Green Bank Ammonia Survey (GAS). GAS is a Large Program at the Green Bank Telescope to map all Gould Belt star-forming regions with $A_\mathrm{V} \gtrsim 7$~mag visible from the northern hemisphere in emission from NH$_3$ and other key molecular tracers. This final release includes the data for all the regions observed: Heiles Cloud 2 and B18 in Taurus; Barnard 1, Barnard 1-E, IC348, NGC 1333, L1448, L1451, and Per7/34 in Perseus; …

I've released 2.14.0 of build-and-inspect-python package; your friendly GitHub Action that prevents embarrassing PyPI uploading mistakes.
Main reason for a rather rushed release is that some people seem to get Python 3.14 when asking GHA for `3.x`.
https://github.com/hynek/build-an…

Release v2.14.0 · hynek/build-and-inspect-python-package
Changed Update tools such that they work on Python 3.14 (which is now 3.x on GitHub Actions). #182 The action now ignores UV_PYTHON coming from the outside. #184

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance
Haolan Zheng, Yanlai Chen, Jiequn Han, Yue Yu
https://arxiv.org/abs/2509.09611

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance
We propose a novel data-lean operator learning algorithm, the Reduced Basis Neural Operator (ReBaNO), to solve a group of PDEs with multiple distinct inputs. Inspired by the Reduced Basis Method and the recently introduced Generative Pre-Trained Physics-Informed Neural Networks, ReBaNO relies on a mathematically rigorous greedy algorithm to build its network structure offline adaptively from the ground up. Knowledge distillation via task-specific activation function allows ReBaNO to have a comp…

Boosting Data Utilization for Multilingual Dense Retrieval
Chao Huang, Fengran Mo, Yufeng Chen, Changhao Guan, Zhenrui Yue, Xinyu Wang, Jinan Xu, Kaiyu Huang
https://arxiv.org/abs/2509.09459

Boosting Data Utilization for Multilingual Dense Retrieval
Multilingual dense retrieval aims to retrieve relevant documents across different languages based on a unified retriever model. The challenge lies in aligning representations of different languages in a shared vector space. The common practice is to fine-tune the dense retriever via contrastive learning, whose effectiveness highly relies on the quality of the negative sample and the efficacy of mini-batch data. Different from the existing studies that focus on developing sophisticated model arc…

Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials
Weilong Chen, Franz G\"orlich, Paul Fuchs, Julija Zavadlav
https://arxiv.org/abs/2510.11148

Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials
Coarse-graining (CG) enables molecular dynamics (MD) simulations of larger systems and longer timescales that are otherwise infeasible with atomistic models. Machine learning potentials (MLPs), with their capacity to capture many-body interactions, can provide accurate approximations of the potential of mean force (PMF) in CG models. Current CG MLPs are typically trained in a bottom-up manner via force matching, which in practice relies on configurations sampled from the unbiased equilibrium Bo…

Detection of Quadruple Structure Near the ASCC 32 Region via Machine Learning Methods
Mohammad Noormohammadi, Atefeh Javadi, Mehdi Khakian Ghomi
https://arxiv.org/abs/2510.10296

Detection of Quadruple Structure Near the ASCC 32 Region via Machine Learning Methods
Multiple structures within stellar groups are an intriguing subject for theoretical and observational studies of stellar formation. With the accuracy and completeness of data from Gaia Data Release 3, we now have new opportunities to detect reliable members of stellar groups across a larger field of view than in previous studies. In this work, using machine learning methods and high-accuracy data, we investigate the possibility of detecting multiple structures within 500 arcmin of ASCC 32. We f…

Non-Linear Solar EUV-Driven Sodium Release from the Lunar Surface: A Contrast to the Linear PSD Model
A Devaraj, S Narendranath, Sreeja S Kartha, M Sarantos, Akhil Krishna R, Blesson Mathew, T Sivarani, S Nidhi, P Anbazhagan, G Selvakumar
https://arxiv.org/abs/2509.05644

Non-Linear Solar EUV-Driven Sodium Release from the Lunar Surface: A Contrast to the Linear PSD Model
The correlation between solar Extreme Ultra-Violet (EUV) radiation above 8.8 eV and the release of sodium from the lunar surface via photon-stimulated desorption (PSD) is investigated. We use simultaneous measurements of EUV photon flux and Na optical spectral line flux ($F_{Na}$) from the lunar exosphere. Data were acquired with the high-resolution (R$\sim$72000) Echelle Spectrograph on the 2.34-m Vainu Bappu Telescope during the lunar first quarter (January-March 2024), observing $Na\,I$ D2 a…

That moment when you realise you’ve made a mistake that’s going to cost you financially… but better discovered now than in several months from now! 🙄