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On Runge-Kutta convolution quadrature based fractional variational integrators
Khaled Hariz, Sina Ober-Bl\"obaum, Fernando Jimenez
https://arxiv.org/abs/2510.11082 https://…

On Runge-Kutta convolution quadrature based fractional variational integrators
Lagrangian systems subject to fractional damping can be incorporated into a variational formalism. The construction can be made by doubling the state variables and introducing fractional derivatives \cite{JiOb2}. The main objective of this paper is to use the Runge-Kutta convolution quadrature (RKCQ) method for approximating fractional derivatives, combined with higher order Galerkin methods in order to derive fractional variational integrators (FVIs). We are specially interested in the CQ base…

A Higher-Order Time Domain Boundary Element Formulation based on Isogeometric Analysis and the Convolution Quadrature Method
Thomas Kramer, Benjamin Marussig, Martin Schanz
https://arxiv.org/abs/2510.06804

A Higher-Order Time Domain Boundary Element Formulation based on Isogeometric Analysis and the Convolution Quadrature Method
An isogeometric boundary element method (BEM) is presented to solve scattering problems in an isotropic homogeneous medium. We consider wave problems governed by the scalar wave equation as in acoustics and the Lamé-Navier equations for elastodynamics considering the theory of linear elasticity. The underlying boundary integral equations imply time-dependent convolution integrals and allow us to determine the sought quantities in the bounded interior or the unbounded exterior after solving for…

Convolution and Graph-based Deep Learning Approaches for Gamma/Hadron Separation in Imaging Atmospheric Cherenkov Telescopes
Abhay Mehta, Dan Parsons, Tim Lukas Holch, David Berge, Matthias Weidlich
https://arxiv.org/abs/2510.05736

Convolution and Graph-based Deep Learning Approaches for Gamma/Hadron Separation in Imaging Atmospheric Cherenkov Telescopes
The identification of $γ$-rays from the predominant hadronic-background is a key aspect in their ground-based detection using Imaging Atmospheric Cherenkov Telescopes (IACTs). While current methods are limited in their ability to exploit correlations in complex data, deep learning-based models offer a promising alternative by directly leveraging image-level information. However, several challenges involving the robustness and applicability of such models remain. Designing model architectures w…

A Wigner Matrix Based Convolution Algorithm For Matrix Elements in the LCAO Method
Tyler C. Sterling
https://arxiv.org/abs/2510.06285 https://arxiv.org/pdf…

A Wigner Matrix Based Convolution Algorithm For Matrix Elements in the LCAO Method
The linear combination of atomic orbitals (LCAO) method uses a small basis set in exchange for expensive matrix element calculations. The most efficient approximation for the matrix element calculations is the two-center approximation (2CA) in tight binding (TB). In the 2CA, a variety of matrix elements are neglected with only "two-center integrals" (2CI) remaining. The 2CI are calculated efficiently by rotating to symmetrical coordinates where the integral is parameterized. This makes TB fast …

Delighted that my watch can display the QR codes used for train travel, disappointed at the convolution in displaying the left upper wrist to a right-hand side camera, when traversing barriers.

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/5]:
- CLAReSNet: When Convolution Meets Latent Attention for Hyperspectral Image Classification
Asmit Bandyopadhyay, Anindita Das Bhattacharjee, Rakesh Das
https://arxiv.org/abs/2511.12346 https://mastoxiv.page/@arXiv_csCV_bot/115570753208147835
- Safeguarded Stochastic Polyak Step Sizes for Non-smooth Optimization: Robust Performance Without ...
Dimitris Oikonomou, Nicolas Loizou
https://arxiv.org/abs/2512.02342 https://mastoxiv.page/@arXiv_mathOC_bot/115654870924418771
- Predictive Modeling of I/O Performance for Machine Learning Training Pipelines: A Data-Driven App...
Karthik Prabhakar, Durgamadhab Mishra
https://arxiv.org/abs/2512.06699 https://mastoxiv.page/@arXiv_csPF_bot/115688618582182232
- Minimum Bayes Risk Decoding for Error Span Detection in Reference-Free Automatic Machine Translat...
Lyu, Song, Kamigaito, Ding, Tanaka, Utiyama, Funakoshi, Okumura
https://arxiv.org/abs/2512.07540 https://mastoxiv.page/@arXiv_csCL_bot/115689532163491162
- In-Context Learning for Seismic Data Processing
Fabian Fuchs, Mario Ruben Fernandez, Norman Ettrich, Janis Keuper
https://arxiv.org/abs/2512.11575 https://mastoxiv.page/@arXiv_csCV_bot/115723040285820239
- Journey Before Destination: On the importance of Visual Faithfulness in Slow Thinking
Rheeya Uppaal, Phu Mon Htut, Min Bai, Nikolaos Pappas, Zheng Qi, Sandesh Swamy
https://arxiv.org/abs/2512.12218 https://mastoxiv.page/@arXiv_csCV_bot/115729165330908574
- Non-Resolution Reasoning (NRR): A Computational Framework for Contextual Identity and Ambiguity P...
Kei Saito
https://arxiv.org/abs/2512.13478 https://mastoxiv.page/@arXiv_csCL_bot/115729234145554554
- Stylized Synthetic Augmentation further improves Corruption Robustness
Georg Siedel, Rojan Regmi, Abhirami Anand, Weijia Shao, Silvia Vock, Andrey Morozov
https://arxiv.org/abs/2512.15675 https://mastoxiv.page/@arXiv_csCV_bot/115740141862163631
- mimic-video: Video-Action Models for Generalizable Robot Control Beyond VLAs
Jonas Pai, Liam Achenbach, Victoriano Montesinos, Benedek Forrai, Oier Mees, Elvis Nava
https://arxiv.org/abs/2512.15692 https://mastoxiv.page/@arXiv_csRO_bot/115739947869830764
toXiv_bot_toot

Approximation by neural network operators of convolution type activated by deformed and parametrized half hyperbolic tangent function
Asiye Arif, Tugba Yurdakadim
https://arxiv.org/abs/2510.05980

Approximation by neural network operators of convolution type activated by deformed and parametrized half hyperbolic tangent function
Here, we introduce three kinds of neural network operators of convolution type which are activated by q-deformed and \b{eta}-parametrized half hyperbolic tangent function. We obtain quantitative convergence results to the identity operator with the use of modulus of continuity. Global smoothness preservation of our operators are also presented and the iterated versions of them are taken into the consideration.

HARP-NeXt: High-Speed and Accurate Range-Point Fusion Network for 3D LiDAR Semantic Segmentation
Samir Abou Haidar, Alexandre Chariot, Mehdi Darouich, Cyril Joly, Jean-Emmanuel Deschaud
https://arxiv.org/abs/2510.06876

HARP-NeXt: High-Speed and Accurate Range-Point Fusion Network for 3D LiDAR Semantic Segmentation
LiDAR semantic segmentation is crucial for autonomous vehicles and mobile robots, requiring high accuracy and real-time processing, especially on resource-constrained embedded systems. Previous state-of-the-art methods often face a trade-off between accuracy and speed. Point-based and sparse convolution-based methods are accurate but slow due to the complexity of neighbor searching and 3D convolutions. Projection-based methods are faster but lose critical geometric information during the 2D pro…

Leveraging Language Semantics for Collaborative Filtering with TextGCN and TextGCN-MLP: Zero-Shot vs In-Domain Performance
Andrei Chernov, Haroon Wahab, Oleg Novitskij
https://arxiv.org/abs/2510.12461 …

Leveraging Language Semantics for Collaborative Filtering with TextGCN and TextGCN-MLP: Zero-Shot vs In-Domain Performance
In recent years, various approaches have been proposed to leverage large language models (LLMs) for incorporating textual information about items into recommender systems. Existing methods primarily focus on either fine-tuning LLMs to generate recommendations or integrating LLM-based embeddings into downstream models. In this work, we follow the latter direction and propose \textbf{TextGCN}, which applies parameter-free graph convolution layers directly over LLM-based item-title embeddings, ins…

Modeling the TMD shape function in $J/\psi$ electroproduction
Miguel G. Echevarria, Raj Kishore, Samuel F. Romera
https://arxiv.org/abs/2510.11809 https://…

Modeling the TMD shape function in $J/ψ$ electroproduction
The next-to-leading order hard function for quarkonium electroproduction is calculated within the framework of transverse-momentum-dependent (TMD) factorization in the low-transverse-momentum regime. The structure of the TMD shape function in quarkonium leptoproduction is analyzed through its operator-level definition. Particular attention is given to the convolution of the unpolarized TMD gluon distribution with the TMD shape function, thereby illustrating the latter's phenomenological role. B…

Fine dissipative properties of Euler solutions with measure first derivatives
Marco Inversi
https://arxiv.org/abs/2510.10704 https://arxiv.org/pdf/2510.107…

Fine dissipative properties of Euler solutions with measure first derivatives
We study fine properties of bounded weak solutions to the incompressible Euler equations whose first derivatives, or only some combinations of them, are Radon measures. As consequences we obtain elementary proofs of the local energy conservation for solutions in BV and BD, without relying on the freedom in choosing the convolution kernel appearing in the approximation of the dissipation. The argument heavily exploits the form of the Euler nonlinearity and it does not apply to the linear transpo…

GMTHRASHpy: Forward Convolutions of Crossed Molecular Beams Experiments in Python
Kazuumi Fujioka, Rui Sun
https://arxiv.org/abs/2510.05398 https://arxiv.o…

GMTHRASHpy: Forward Convolutions of Crossed Molecular Beams Experiments in Python
We introduce GMTHRASHpy, a Python-based application to do forward convolution fits of crossed molecular beams experiments. The code is designed to be easy-to-use and widely-available, so as to be of value to anyone wanting to reproduce data or fits from these experiments. GMTHRASHpy has been benchmarked to replicate the original GMTHRASH executable for a wide variety of published reactions. The inputs and algorithm guiding it are explained. The code is open-source and can be downloaded at: gith…

SpecGuard: Spectral Projection-based Advanced Invisible Watermarking
Inzamamul Alam, Md Tanvir Islam, Khan Muhammad, Simon S. Woo
https://arxiv.org/abs/2510.07302 https://

SpecGuard: Spectral Projection-based Advanced Invisible Watermarking
Watermarking embeds imperceptible patterns into images for authenticity verification. However, existing methods often lack robustness against various transformations primarily including distortions, image regeneration, and adversarial perturbation, creating real-world challenges. In this work, we introduce SpecGuard, a novel watermarking approach for robust and invisible image watermarking. Unlike prior approaches, we embed the message inside hidden convolution layers by converting from the spa…

Quantum-Theoretical Re-interpretation of Pricing Theory
Tian Xin
https://arxiv.org/abs/2510.06287 https://arxiv.org/pdf/2510.06287

Quantum-Theoretical Re-interpretation of Pricing Theory
Motivated by Heisenberg's observable-only stance, we replace latent "information" (filtrations, hidden diffusions, state variables) with observable transitions between price states. On a discrete price lattice with a Hilbert-space representation, shift operators and the spectral calculus of the price define observable frequency operators and a translation-invariant convolution generator. Combined with jump operators that encode transition intensities, this yields a completely positive, translat…

UltraScatter: Ray-Based Simulation of Ultrasound Scattering
Felix Duelmer, Mohammad Farid Azampour, Nassir Navab
https://arxiv.org/abs/2510.10612 https://a…

UltraScatter: Ray-Based Simulation of Ultrasound Scattering
Traditional ultrasound simulation methods solve wave equations numerically, achieving high accuracy but at substantial computational cost. Faster alternatives based on convolution with precomputed impulse responses remain relatively slow, often requiring several minutes to generate a full B-mode image. We introduce UltraScatter, a probabilistic ray tracing framework that models ultrasound scattering efficiently and realistically. Tissue is represented as a volumetric field of scattering probabi…

DeRainMamba: A Frequency-Aware State Space Model with Detail Enhancement for Image Deraining
Zhiliang Zhu, Tao Zeng, Tao Yang, Guoliang Luo, Jiyong Zeng
https://arxiv.org/abs/2510.06746

DeRainMamba: A Frequency-Aware State Space Model with Detail Enhancement for Image Deraining
Image deraining is crucial for improving visual quality and supporting reliable downstream vision tasks. Although Mamba-based models provide efficient sequence modeling, their limited ability to capture fine-grained details and lack of frequency-domain awareness restrict further improvements. To address these issues, we propose DeRainMamba, which integrates a Frequency-Aware State-Space Module (FASSM) and Multi-Directional Perception Convolution (MDPConv). FASSM leverages Fourier transform to d…

Spatial two-grid compact difference scheme for two-dimensional nonlinear diffusion-wave equations with variable exponent
Hao Zhang, Kexin Li, Wenlin Qiu
https://arxiv.org/abs/2510.12188

Spatial two-grid compact difference scheme for two-dimensional nonlinear diffusion-wave equations with variable exponent
This paper presents a spatial two-grid (STG) compact difference scheme for a two-dimensional (2D) nonlinear diffusion-wave equation with variable exponent, which describes, e.g., the propagation of mechanical diffusive waves in viscoelastic media with varying material properties. Following the idea of the convolution approach, the diffusion-wave model is first transformed into an equivalent formulation. A fully discrete scheme is then developed by applying a compact difference approximation in …