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For correlated real symmetric or complex Hermitian random matrices, we prove that the local eigenvalue statistics at any cusp singularity are universal. Since the density of states typically exhibits only square root edge or cubic root cusp singularities, our result completes the proof of the Wigner-Dyson-Mehta universality conjecture in all spectral regimes for a very general class of random matrices. Previously only the bulk and the edge universality were established in this generality (Alt et al. in Ann Probab 48(2):963-1001, 2020), while cusp universality was proven only for Wigner-type matrices with independent entries (Cipolloni et al. in Pure Appl Anal 1:615-707, 2019; Erdős et al. in Commun. Math. Phys. 378:1203-1278, 2018). As our main technical input, we prove an optimal local law at the cusp using the , a recursive tandem of the characteristic flow method and a Green function comparison argument. Moreover, our proof of the optimal local law holds uniformly in the spectrum, thus we also provide a significantly simplified alternative proof of the local eigenvalue universality in the previously studied bulk (Erdős et al. in Forum Math. Sigma 7:E8, 2019) and edge (Alt et al. in Ann Probab 48(2):963-1001, 2020) regimes.
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http://dx.doi.org/10.1007/s00220-025-05417-z | DOI Listing |
Nutr J
September 2025
Department of Life Sciences, Division of Food and Nutrition Science, Chalmers University of Technology, Gothenburg, 412 96, Sweden.
Background: Avenanthramides (AVAs) and Avenacosides (AVEs) are unique to oats (Avena Sativa) and may serve as biomarkers of oat intake. However, information regarding their validity as food intake biomarkers is missing. We aimed to investigate critical validation parameters such as half-lives, dose-response, matrix effects, relative bioavailability under single dose, and in relation to the abundance of Feacalibacterium prausnitzii, and under repeated dosing, to understand the potential applications of AVAs and AVEs as biomarkers of oat intake.
View Article and Find Full Text PDFJ R Soc Interface
September 2025
UK Centre for Ecology & Hydrology, Wallingford, Oxfordshire, UK.
Severe fever with thrombocytopaenia syndrome virus (SFTSV) was identified by the World Health Organization as a priority pathogen due to its high case-fatality rate in humans and rapid spread. It is maintained in nature through three transmission pathways: systemic, non-systemic and transovarial. Understanding the relative contributions of these transmission pathways is crucial for developing evidence-informed public health interventions to reduce its spillover risks to humans.
View Article and Find Full Text PDFSci Prog
September 2025
School of Information Science and Technology, Hangzhou Normal University, Hangzhou, China.
At present, significant progress has been made in the research of image encryption, but there are still some issues that need to be explored in key space, password generation and security verification, encryption schemes, and other aspects. Aiming at this, a digital image encryption algorithm was developed in this paper. This algorithm integrates six-dimensional cellular neural network with generalized chaos to generate pseudo-random numbers to generate the plaintext-related ciphers.
View Article and Find Full Text PDFInt J Hyg Environ Health
September 2025
CHU Rennes, University of Rennes, Inserm, EHESP, IRSET (Institut de Recherche en Santé, Environnement et Travail), UMR_S 1085, F-35000, Rennes, France.
Objective: To use data-driven approaches to investigate maternal multi-occupational exposures during pregnancy and their effects on intrauterine growth.
Methods: Maternal occupational exposure to 47 factors during pregnancy was evaluated with job-exposure matrices in the French ELFE cohort. The outcomes of interest were birthweight (BW), small for gestational age (SGA) and head circumference (HC).
Chaos
September 2025
Department of Economics and Business, University of Almería, 04120 Almería, Spain.
Studying and comprehending the eigenvalue distribution of the correlation matrices of stock returns is a powerful tool to delve into the complex structure of financial markets. This paper deals with the analysis of the role of eigenvalues and their associated eigenvectors of correlation matrices within the context of financial markets. We exploit the meaningfulness of Random Matrix Theory with the specific aspect of the Marchenko-Pastur distribution law to separate noise from true signal, but with a special focus on giving an interpretation of what these signals mean in the financial context.
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