Tootfinder

Sparsity and Total Variation Constrained Multilayer Linear Unmixing for Hyperspectral Imagery
Gang Yang
https://arxiv.org/abs/2508.03403 https://arxiv.org/…

Sparsity and Total Variation Constrained Multilayer Linear Unmixing for Hyperspectral Imagery
Hyperspectral unmixing aims at estimating material signatures (known as endmembers) and the corresponding proportions (referred to abundances), which is a critical preprocessing step in various hyperspectral imagery applications. This study develops a novel approach called sparsity and total variation (TV) constrained multilayer linear unmixing (STVMLU) for hyperspectral imagery. Specifically, based on a multilayer matrix factorization model, to improve the accuracy of unmixing, a TV constraint…

Marito: Structuring and Building Open Multilingual Terminologies for South African NLP
Vukosi Marivate, Isheanesu Dzingirai, Fiskani Banda, Richard Lastrucci, Thapelo Sindane, Keabetswe Madumo, Kayode Olaleye, Abiodun Modupe, Unarine Netshifhefhe, Herkulaas Combrink, Mohlatlego Nakeng, Matome Ledwaba
https://arxiv.org/abs/2508.03529…

Marito: Structuring and Building Open Multilingual Terminologies for South African NLP
The critical lack of structured terminological data for South Africa's official languages hampers progress in multilingual NLP, despite the existence of numerous government and academic terminology lists. These valuable assets remain fragmented and locked in non-machine-readable formats, rendering them unusable for computational research and development. \emph{Marito} addresses this challenge by systematically aggregating, cleaning, and standardising these scattered resources into open, interop…

Multilevel inserting constructions for constant dimension subspace codes
Gang Wang, Xuan Gao, Sihem Mesnager, Fang-Wei Fu
https://arxiv.org/abs/2508.03196 https://

Multilevel inserting constructions for constant dimension subspace codes
Subspace codes, especially constant dimension subspace codes (CDCs), represent an intriguing domain that can be used to conduct basic coding theory investigations. They have received widespread attention due to their applications in random network coding. This paper presents inverse bilateral multilevel construction by introducing inverse bilateral identifying vectors and inverse bilateral Ferrers diagram rank-metric codes. By inserting the inverse bilateral multilevel construction into the dou…

physics_collab: Multilayer physicist collaborations (2015)
Two multiplex networks of coauthorships among the Pierre Auger Collaboration of physicists (2010-2012) and among researchers who have posted preprints on arXiv.org (all papers up to May 2014). Layers represent different categories of publication, and an edge's weight indicates the number of reports written by the authors. These layers are one-mode projections from the underlying author-paper bipartite network.
This n…

Graph theory-based automated quantum algorithm for efficient querying of acyclic and multiloop causal configurations
Salvador A. Ochoa-Oregon, Juan P. Uribe-Ram\'irez, Roger J. Hern\'andez-Pinto, Selomit Ram\'irez-Uribe, Germ\'an Rodrigo
https://arxiv.org/abs/2508.04019

Graph theory-based automated quantum algorithm for efficient querying of acyclic and multiloop causal configurations
Quantum algorithms provide a promising framework in high-energy physics, in particular, for unraveling the causal configurations of multiloop Feynman diagrams by identifying Feynman propagators with qubits, a challenge analogous to querying directed acyclic graphs in graph theory. In this paper, we present the Minimum Clique-optimised quantum Algorithm (MCA), an automated quantum algorithm designed to efficiently query the causal structures within the Loop-Tree Duality. The MCA quantum algorith…

On the Evaluation of Large Language Models in Multilingual Vulnerability Repair
Dong wang, Junji Yu, Honglin Shu, Michael Fu, Chakkrit Tantithamthavorn, Yasutaka Kamei, Junjie Chen
https://arxiv.org/abs/2508.03470

On the Evaluation of Large Language Models in Multilingual Vulnerability Repair
Various Deep Learning-based approaches with pre-trained language models have been proposed for automatically repairing software vulnerabilities. However, these approaches are limited to a specific programming language (C/C++). Recent advances in large language models (LLMs) offer language-agnostic capabilities and strong semantic understanding, exhibiting potential to overcome multilingual vulnerability limitations. Although some work has begun to explore LLMs' repair performance, their effecti…

CogBench: A Large Language Model Benchmark for Multilingual Speech-Based Cognitive Impairment Assessment
Feng Rui, Zhiyao Luo, Wei Wang, Yuting Song, Yong Liu, Tingting Zhu, Jianqing Li, Xingyao Wang
https://arxiv.org/abs/2508.03360

CogBench: A Large Language Model Benchmark for Multilingual Speech-Based Cognitive Impairment Assessment
Automatic assessment of cognitive impairment from spontaneous speech offers a promising, non-invasive avenue for early cognitive screening. However, current approaches often lack generalizability when deployed across different languages and clinical settings, limiting their practical utility. In this study, we propose CogBench, the first benchmark designed to evaluate the cross-lingual and cross-site generalizability of large language models (LLMs) for speech-based cognitive impairment assessme…

Tunable Plasmonic Absorption in Metal-Dielectric Multilayers via FDTD Simulations and an Explainable Machine Learning Approach
Emmanuel A. Bamidele
https://arxiv.org/abs/2508.04014

Tunable Plasmonic Absorption in Metal-Dielectric Multilayers via FDTD Simulations and an Explainable Machine Learning Approach
Plasmonic devices, fundamental to modern nanophotonics, exploit resonant interactions between light and free electrons in metals to achieve enhanced light trapping and electromagnetic field confinement. However, modeling their complex, nonlinear optical responses remains computationally intensive. In this work, we combine finite-difference time-domain simulations with machine learning to simulate and predict absorbed power behavior in multilayer plasmonic stacks composed of SiO2, gold, silver, …

fact check AI at SemEval-2025 Task 7: Multilingual and Crosslingual Fact-checked Claim Retrieval
Pranshu Rastogi
https://arxiv.org/abs/2508.03475 https://a…

fact check AI at SemEval-2025 Task 7: Multilingual and Crosslingual Fact-checked Claim Retrieval
SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval is approached as a Learning-to-Rank task using a bi-encoder model fine-tuned from a pre-trained transformer optimized for sentence similarity. Training used both the source languages and their English translations for multilingual retrieval and only English translations for cross-lingual retrieval. Using lightweight models with fewer than 500M parameters and training on Kaggle T4 GPUs, the method achieved 92% Succes…

Goal-Oriented Adaptive Finite Element Multilevel Quasi-{M}onte {C}arlo
Joakim Beck, Yang Liu, Erik von Schwerin, Ra\'ul Tempone
https://arxiv.org/abs/2508.02925 https://

Goal-Oriented Adaptive Finite Element Multilevel Quasi-{M}onte {C}arlo
The efficient approximation of quantity of interest derived from PDEs with lognormal diffusivity is a central challenge in uncertainty quantification. In this study, we propose a multilevel quasi-Monte Carlo framework to approximate deterministic, real-valued, bounded linear functionals that depend on the solution of a linear elliptic PDE with a lognormal diffusivity coefficient {parameterized by a 49-dimensional Gaussian random vector} and deterministic geometric singularities in bounded domai…