Tootfinder

Latent Representation Learning of Multi-scale Thermophysics: Application to Dynamics in Shocked Porous Energetic Material
Shahab Azarfar, Joseph B. Choi, Phong CH. Nguyen, Yen T. Nguyen, Pradeep Seshadri, H. S. Udaykumar, Stephen Baek
https://arxiv.org/abs/2506.12996

Latent Representation Learning of Multi-scale Thermophysics: Application to Dynamics in Shocked Porous Energetic Material
Coupling of physics across length and time scales plays an important role in the response of microstructured materials to external loads. In a multi-scale framework, unresolved (subgrid) meso-scale dynamics is upscaled to the homogenized (macro-scale) representation of the heterogeneous material through closure models. Deep learning models trained using meso-scale simulation data are now a popular route to assimilate such closure laws. However, meso-scale simulations are computationally taxing,…

Solar cells on cattle holding pens seems like a great idea! Probably tons of buildings and farms that would be fine with solar cells on top.
I'm no (food) scientist but I am a lot more confident that it doesn't pose a risk to the food source. But I'm less confident of growing crops under solar cells or solar cells floating on aquatic farms. Likely need significant maintenance over the years to prevent leaching and it's always hard for politicians to fix old instead of …

On Trustworthy Rule-Based Models and Explanations
Mohamed Siala, Jordi Planes, Joao Marques-Silva
https://arxiv.org/abs/2507.07576 https://

On Trustworthy Rule-Based Models and Explanations
A task of interest in machine learning (ML) is that of ascribing explanations to the predictions made by ML models. Furthermore, in domains deemed high risk, the rigor of explanations is paramount. Indeed, incorrect explanations can and will mislead human decision makers. As a result, and even if interpretability is acknowledged as an elusive concept, so-called interpretable models are employed ubiquitously in high-risk uses of ML and data mining (DM). This is the case for rule-based ML models,…

Azerbaijan gives eight journalists prison sentences ranging from 7.5 to 15 years, as part of a series of media trials set to obliterate independent reporting (Committee to Protect Journalists)
https://cpj.org/2025/06/8-journalists-g…

8 journalists given lengthy jail terms as Azerbaijan crushes free press - Committee to Protect Journalists
New York, June 23, 2025— Eight Azerbaijani journalists have received prison sentences ranging from 7 ½ to 15 years, as part of an ongoing series of media trials likely to obliterate independent reporting in the Caucasus nation. In a closed-door trial on Monday, columnist and peace activist Bahruz Samadov was sentenced by a court in...

The Scintillator Surface Detector of the Pierre Auger Observatory
The Pierre Auger Collaboration
https://arxiv.org/abs/2507.07762 https://

The Scintillator Surface Detector of the Pierre Auger Observatory
Data collected so far by the Pierre Auger Observatory have enabled major advances in ultra-high energy cosmic ray physics and demonstrated that improved determination of masses of primary cosmic-ray particles, preferably on an event-by-event basis, is necessary for understanding their origin and nature. Improvement in primary mass measurements was the main motivation for the upgrade of the Pierre Auger Observatory, called AugerPrime. As part of this upgrade, scintillator detectors are added to …

Models for differential cross section in proton-proton scattering and their implications at ISR and LHC energies
Muhammad Saad Ashraf, Nosheen Akbar, Sarwat Zahra
https://arxiv.org/abs/2506.05532

Models for differential cross section in proton-proton scattering and their implications at ISR and LHC energies
Few composite exponential models for the differential cross section are proposed to analyse the proton-proton ($pp$) elastic scattering at several energies. These proposed models are fitted to the data for $pp$ elastic differential cross section reported at CERN-ISR, LHC, and extrapolated energies of other models. These models have produced important features including dip-bump structure and shrinkage of the forward peak. Position of the dip is also well produced by our models for all the data …

Adaptive Attention Residual U-Net for curvilinear structure segmentation in fluorescence microscopy and biomedical images
Achraf Ait Laydi, Louis Cueff, Mewen Crespo, Yousef El Mourabit, H\'el\`ene Bouvrais
https://arxiv.org/abs/2507.07800

Adaptive Attention Residual U-Net for curvilinear structure segmentation in fluorescence microscopy and biomedical images
Segmenting curvilinear structures in fluorescence microscopy remains a challenging task, particularly under noisy conditions and in dense filament networks commonly seen in vivo. To address this, we created two original datasets consisting of hundreds of synthetic images of fluorescently labelled microtubules within cells. These datasets are precisely annotated and closely mimic real microscopy images, including realistic noise. The second dataset presents an additional challenge, by simulating…

Replaced article(s) found for cs.CR. https://arxiv.org/list/cs.CR/new
[1/1]:
- OblivIO: Securing reactive programs by oblivious execution with bounded traffic overheads
Jeppe Fredsgaard Blaabjerg, Aslan Askarov

Probing Quantum Spin Systems with Kolmogorov-Arnold Neural Network Quantum States
Mahmud Ashraf Shamim, Eric Reinhardt, Talal Ahmed Chowdhury, Sergei Gleyzer, Paulo T Araujo
https://arxiv.org/abs/2506.01891

Probing Quantum Spin Systems with Kolmogorov-Arnold Neural Network Quantum States
Neural Quantum States (NQS) are a class of variational wave functions parametrized by neural networks (NNs) to study quantum many-body systems. In this work, we propose SineKAN, the NQS ansatz based on Kolmogorov-Arnold Networks (KANs), to represent quantum mechanical wave functions as nested univariate functions. We show that \sk wavefunction with learnable sinusoidal activation functions can capture the ground state energies, fidelities and various correlation functions of the 1D Transverse-F…

The Phases of Chaos
Tarek Anous, Diego M. Hofman
https://arxiv.org/abs/2506.20542 https://arxiv.org/pdf/2506.20542

The Phases of Chaos
We develop a novel physical picture to understand certain universal properties of the GUE matrix model which are typically ascribed to quantum chaos, i.e. the ramp and the plateau. We argue that these features should instead be associated with a pattern of spontaneous (or weak explicit) symmetry breaking. In this language, the GUE matrix model corresponds to an effective theory that describes the symmetry-broken phase, and where the Hermitian matrix of the GUE should be understood as a massive …