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In this paper we study a random matrix model with the chiral and flavor structure of the QCD Dirac operator and a temperature dependence given by the lowest Matsubara frequency. Using the supersymmetric method for random matrix theory, we…

High Energy Physics - Phenomenology · Physics 2009-10-28 A. D. Jackson , M. K. Şener , J. J. M. Verbaarschot

For a nonrelativistic classical particle undergoing arbitrary oscillations, the generalized effective potential Y is derived from nonlinear eigenfrequencies of the particle-field system. Specifically, the ponderomotive potential is extended…

Plasma Physics · Physics 2009-11-13 I. Y. Dodin , N. J. Fisch

We study the fundamental problem of learning a marginally stable unknown nonlinear dynamical system. We describe an algorithm for this problem, based on the technique of spectral filtering, which learns a mapping from past observations to…

Machine Learning · Computer Science 2025-08-19 Evan Dogariu , Anand Brahmbhatt , Elad Hazan

We consider the classical response of a strongly chaotic Hamiltonian system. The spectrum of such a system consists of discrete complex Ruelle-Pollicott (RP) resonances which manifest themselves in the behavior of the correlation and…

Chaotic Dynamics · Physics 2009-11-13 Sergey V. Malinin , Vladimir Y. Chernyak

Mass-conserving reaction-diffusion (MCRD) systems are widely used to model phase separation and pattern formation in cell polarity, biomolecular condensates, and ecological systems. Numerical simulations and formal asymptotic analysis…

Analysis of PDEs · Mathematics 2026-02-09 Xiaoqing He , Quan-Xing Liu , Dong Ye

We analyze a model quantum dynamical system subjected to periodic interaction with an environment, which can describe quantum measurements. Under the condition of strong classical chaos and strong decoherence due to large coupling with the…

Using the supersymmetry approach, we study spectral statistical properties of a two-dimensional quantum particle subject to a non-uniform magnetic field. We focus mainly on the problem of regularisation of the field theory. Our analysis…

Disordered Systems and Neural Networks · Physics 2009-10-31 A. V. Izyumov , B. D. Simons

We study the dynamics of a classical nonlinear oscillator subject to noise and driven by a sinusoidal force. In particular, we give an analytical identification of the mechanisms responsible for the supernarrow peaks observed recently in…

Statistical Mechanics · Physics 2016-01-20 J. Plata

A good generalization of the Euclidean dimension to disordered systems and non crystalline structures is commonly required to be related to large scale geometry and it is expected to be independent of local geometrical modifications. The…

Statistical Mechanics · Physics 2009-10-30 Raffaella Burioni , Davide Cassi

Recently it has been shown that the gross structure of the bottomonium spectrum is reproduced reasonably well within the non-relativistic boundstate theory based on perturbative QCD. In that calculation, however, the fine splittings and the…

High Energy Physics - Phenomenology · Physics 2009-11-07 S. Recksiegel , Y. Sumino

Spectral analysis plays a crucial role in high-dimensional statistics, where determining the asymptotic distribution of various spectral statistics remains a challenging task. Due to the difficulties of deriving the analytic form, recent…

Statistics Theory · Mathematics 2025-04-02 Guoyu Zhang , Dandan Jiang , Fang Yao

The magnetic line defect in the $O(N)$ model gives rise to a non-trivial one-dimensional defect conformal field theory of theoretical and experimental value. This model is considered here in $d=4-\varepsilon$ and the full spectrum of defect…

High Energy Physics - Theory · Physics 2025-08-26 Jake Belton , Nadav Drukker , Ziwen Kong , Andreas Stergiou

.Stochastic models based on random diffusivities, such as the diffusing-diffusivity approach, are popular concepts for the description of non-Gaussian diffusion in heterogeneous media. Studies of these models typically focus on the moments…

Statistical Mechanics · Physics 2020-08-26 V. Sposini , D. S. Grebenkov , R. Metzler , G. Oshanin , F. Seno

This work sets the exact equations for the quasiclassical response function and susceptibility of a Brownian particle immersed in a bath of quantum harmonic oscillators driving by nonlinear harmonic potentials. A delta force perturbation…

Quantum Physics · Physics 2020-12-09 Pedro J. Colmenares

We study the dynamics of an infinite regular lattice of classical charged oscillators. Each individual oscillator is described as a point particle subject to a harmonic restoring potential, to the retarded electromagnetic field generated by…

Optics · Physics 2010-05-12 M. Marino , A. Carati , L. Galgani

In human cells, estrogenic signals induce cyclical association and dissociation of specific proteins with the DNA in order to activate transcription of estrogen-responsive genes. These oscillations can be modeled by assuming a large number…

Cell Behavior · Quantitative Biology 2007-05-23 Vincent Lemaire , Chiu Fan Lee , Jinzhi Lei , Raphael Metivier , Leon Glass

We study the stochastic dynamics of sequences evolving by single site mutations, segmental duplications, deletions, and random insertions. These processes are relevant for the evolution of genomic DNA. They define a universality class of…

Genomics · Quantitative Biology 2009-11-11 Philipp W. Messer , Michael Lassig , Peter F. Arndt

A neuron transforms its input into output spikes, and this transformation is the basic unit of computation in the nervous system. The spiking response of the neuron to a complex, time-varying input can be predicted from the detailed…

Neurons and Cognition · Quantitative Biology 2011-12-19 Michael Famulare , Adrienne Fairhall

For a multidimensional driftless diffusion in an unbounded, smooth, sub-linear generalized parabolic domain, with oblique reflection from the boundary, we give natural conditions under which either explosion occurs, if the domain narrows…

Probability · Mathematics 2023-11-07 Mikhail V. Menshikov , Aleksandar Mijatović , Andrew R. Wade

We show that deep neural networks trained across diverse tasks exhibit remarkably similar low-dimensional parametric subspaces. We provide the first large-scale empirical evidence that demonstrates that neural networks systematically…

Machine Learning · Computer Science 2025-12-09 Prakhar Kaushik , Shravan Chaudhari , Ankit Vaidya , Rama Chellappa , Alan Yuille
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