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We analyze the localization behavior in a non-Hermitian system subject to a quasiperiodic onsite potential. We characterize localization transitions using multiple quantitative indicators, including inverse participation ratio (IPR),…

Mesoscale and Nanoscale Physics · Physics 2026-01-01 Yu-Peng Wang , Chuo-Kai Chang , Ryo Okugawa , Chen-Hsuan Hsu

Recent advances in spin-dependent optical lattices [Meng et al., Nature \textbf{615}, 231 (2023)] have enabled the experimental implementation of two superimposed three-dimensional lattices, presenting new opportunities to investigate…

Quantum Gases · Physics 2025-04-04 Ce Wang , Chao Gao , Zhe-Yu Shi

Anderson localization physics features three fundamental types of eigenstates: extended, localized, and critical, with the third one exhibiting the exotic properties in-between the former two. Confirming the presence of critical states is…

The nematode Caenorhabditis elegans is a well-known model organism used to investigate fundamental questions in biology. Motility assays of this small roundworm are designed to study the relationships between genes and behavior. Commonly,…

Computer Vision and Pattern Recognition · Computer Science 2015-05-19 Raphael Sznitman , Manaswi Gupta , Gregory D. Hager , Paulo E. Arratia , Josue Sznitman

This paper concerns the asymmetric transport observed along interfaces separating two-dimensional bulk topological insulators modeled by (continuous) differential Hamiltonians and how such asymmetry persists after numerical discretization.…

Mathematical Physics · Physics 2024-01-01 Solomon Quinn , Guillaume Bal

Objective: Modelling the associations from high-throughput experimental molecular data has provided unprecedented insights into biological pathways and signalling mechanisms. Graphical models and networks have especially proven to be useful…

Machine Learning · Statistics 2013-04-24 Marco Scutari , Radhakrishnan Nagarajan

It is generally accepted that statistics of energy levels in closed chaotic quantum systems is adequately described by the theory of Random Hermitian Matrices. Much less is known about properties of "resonances" - generic features of open…

chao-dyn · Physics 2007-05-23 Yan V. Fyodorov

We have investigated the valence transition in the extended periodic Anderson model (EPAM) within the framework of dynamical mean field theory. Previous theoretical studies have indicated the presence of this transition in parameter ranges…

Strongly Correlated Electrons · Physics 2021-07-12 Pramod Kumar , N. S. Vidhyadhiraja

Almost periodic particle chains exhibit peculiar propagation properties that are not observed in perfectly periodic ones. Furthermore, since they inherently support non-negligible long-range interactions and radiation through the…

Strongly Correlated Electrons · Physics 2015-06-19 Yarden Mazor , Ben Z. Steinberg

Precise tracking and measurement of the energy carried by the individual magnetohydrodynamic (MHD) modes has important implications and utility in astrophysical and laboratory plasmas. Previously, this was only achievable in limited linear…

Solar and Stellar Astrophysics · Physics 2025-02-25 Abbas Raboonik , David I. Pontin , Lucas A. Tarr

Sil, Maiti, and Chakrabarti (SMC) (Phys. Rev. Lett. 101, 076803 (2008), arXiv:0801.2670) introduce an aperiodic two-leg ladder network composed of atomic sites with on-site potentials distributed according to a quasiperiodic Aubry-Andre…

Disordered Systems and Neural Networks · Physics 2014-02-13 Sergej Flach , Carlo Danieli

Despite the successes of machine learning methods in physical sciences, prediction of the Hamiltonian, and thus electronic properties, is still unsatisfactory. Here, based on graph neural network architecture, we present an extendable…

Materials Science · Physics 2023-01-12 Mao Su , Ji-Hui Yang , Hong-Jun Xiang , Xin-Gao Gong

A central challenge in dynamic network analysis is to represent temporal evolution in a way that is both geometrically meaningful and statistically identifiable. One approach embeds a sequence of network snapshots as trajectories in a…

Machine Learning · Statistics 2026-05-12 Haruka Ezoe , Ryohei Hisano

We investigate localization properties in a family of deterministic (i.e. no disorder) nearest neighbor tight binding models with quasiperiodic onsite modulation. We prove that this family is self-dual under a generalized duality…

Disordered Systems and Neural Networks · Physics 2015-04-16 Sriram Ganeshan , J. H. Pixley , S. Das Sarma

We examine the localization properties of the three-dimensional (3D) Anderson Hamiltonian with off-diagonal disorder using the transfer-matrix method (TMM) and finite-size scaling (FSS). The nearest-neighbor hopping elements are chosen…

Disordered Systems and Neural Networks · Physics 2007-05-23 P. Cain , R. A. Roemer , M. Schreiber

We determine the location $\lambda_c$ of the mobility edge in the spectrum of the hermitian Wilson operator on quenched ensembles. We confirm a theoretical picture of localization proposed for the Aoki phase diagram. When $\lambda_c>0$ we…

High Energy Physics - Lattice · Physics 2011-07-19 Maarten Golterman , Yigal Shamir , Benjamin Svetitsky

We propose a class of general non-Hermitian quasiperiodic lattice models with exponential hoppings and analytically determine the genuine complex mobility edges by solving its dual counterpart exactly utilizing Avila's global theory. Our…

Disordered Systems and Neural Networks · Physics 2024-10-31 Li Wang , Jiaqi Liu , Zhenbo Wang , Shu Chen

A Hamiltonian is said to be quasi-exactly solvable (QES) if some of the energy levels and the corresponding eigenfunctions can be calculated exactly and in closed form. An entirely new class of QES Hamiltonians having sextic polynomial…

Quantum Physics · Physics 2009-11-11 Carl M. Bender , Maria Monou

Embedded random matrix ensembles are generic models for describing statistical properties of finite isolated interacting quantum many-particle systems. For the simplest spinless systems, with say $m$ particles in $N$ single particle states…

Quantum Physics · Physics 2015-04-06 V. K. B. Kota , Manan Vyas

In networks of dynamic systems, one challenge is to identify the interconnection structure on the basis of measured signals. Inspired by a Bayesian approach in [1], in this paper, we explore a Bayesian model selection method for identifying…

Systems and Control · Computer Science 2019-03-18 Shengling Shi , Giulio Bottegal , Paul M. J. Van den Hof