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Kernel density estimation is a technique for approximating probability distributions. Here, it is applied to the calculation of mutual information on a metric space. This is motivated by the problem in neuroscience of calculating the mutual…

Information Theory · Computer Science 2014-05-20 R. Joshua Tobin , Conor J. Houghton

Anderson localization problem for non-interacting two-dimensional electron gas subject to strong magnetic field, disordered potential and spin-orbit coupling is studied numerically on a square lattice. The nature of the corresponding…

Mesoscale and Nanoscale Physics · Physics 2014-11-19 C. Wang , Ying Su , Y. Avishai , Yigal Meir , X. R. Wang

We prove that the local eigenvalue statistics for $d=1$ random band matrices with fixed bandwidth and, for example, Gaussian entries, is given by a Poisson point process and we identify the intensity of the process. The proof relies on an…

Mathematical Physics · Physics 2020-09-01 Benjamin Brodie , Peter D. Hislop

We use the regularized kernel polynomial method (RKPM) to numerically study the effect disorder on a single layer of graphene. This accurate numerical method enables us to study very large lattices with millions of sites, and hence is…

Strongly Correlated Electrons · Physics 2009-08-31 Mohsen Amini , S. A. Jafari , F. Shahbazi

We revisit the problem of one-dimensional Anderson localization, by providing perturbative expression for Lyapunov exponent of Anderson model with next-nearest-neighbor (nnn) hopping. By comparison with exact numerical results, we discuss…

Disordered Systems and Neural Networks · Physics 2013-11-08 Reza Sepehrinia

We study the quantum Hall plateau transition on rectangular tori. As the aspect ratio of the torus is increased, the two-dimensional critical behavior, characterized by a subthermodynamic number of topological states in a vanishing energy…

Disordered Systems and Neural Networks · Physics 2020-02-28 Matteo Ippoliti , R. N. Bhatt

We demonstrate that a weak disorder in atomic positions introduces spatially localized optical modes in a dense three-dimensional ensemble of immobile two-level atoms arranged in a diamond lattice and coupled by the electromagnetic field.…

Disordered Systems and Neural Networks · Physics 2020-10-21 S. E. Skipetrov

We consider a noninteracting disordered system designed to model particle diffusion, relaxation in glasses, and impurity bands of semiconductors. Disorder originates in the random spatial distribution of sites. We find strong numerical…

Disordered Systems and Neural Networks · Physics 2015-03-17 Jacob J. Krich , Alán Aspuru-Guzik

Semicontinuous outcomes occur frequently in health services, insurance, and cost studies. Standard nonparametric density estimators are not well suited to such data because they do not naturally accommodate the mixed structure, the…

Methodology · Statistics 2026-05-06 Guanjie Lyu , Frédéric Ouimet , Cindy Feng

Spectral statistics of disordered systems encode Thouless and Heisenberg time scales whose ratio determines whether the system is chaotic or localized. Identifying similarities between system size and disorder strength scaling of Thouless…

Disordered Systems and Neural Networks · Physics 2020-05-13 Piotr Sierant , Dominique Delande , Jakub Zakrzewski

We observe a singularity in the electronic properties of the Anderson Model of Localization with bounded diagonal disorder, which is clearly distinct from the well-established mobility edge (localization-delocalization transition) that…

Disordered Systems and Neural Networks · Physics 2015-05-28 S. Johri , R. N. Bhatt

We investigate the asymptotic mean squared error of kernel estimators of the intensity function of a spatial point process. We show that when $n$ independent copies of a point process in $\mathbb R^d$ are superposed, the optimal bandwidth…

Statistics Theory · Mathematics 2019-04-11 M. N. M. van Lieshout

We study Anderson localization of single particles in continuous, correlated, one-dimensional disordered potentials. We show that tailored correlations can completely change the energy-dependence of the localization length. By considering…

Disordered Systems and Neural Networks · Physics 2013-03-28 Marie Piraud , Laurent Sanchez-Palencia

We prove that a strongly disordered two-dimensional system localizes with a localization length given analytically. We get a scaling law with a critical exponent is $\nu=1$ in agreement with the Chayes criterion $\nu\ge 1$. The case we are…

Disordered Systems and Neural Networks · Physics 2013-05-21 Marco Frasca

We examine the localization properties of the 2D Anderson Hamiltonian with off-diagonal disorder. Investigating the behavior of the participation numbers of eigenstates as well as studying their multifractal properties, we find states in…

Disordered Systems and Neural Networks · Physics 2009-10-30 Andrzej Eilmes , Rudolf A. Roemer , Michael Schreiber

We calculate the corrections to the thermodynamic limit of the critical density for jamming in the Kob-Andersen and Fredrickson-Andersen kinetically-constrained models, and find them to be finite-density corrections, and not finite-size…

Statistical Mechanics · Physics 2014-08-15 Eial Teomy , Yair Shokef

This paper studies Kernel Density Estimation for a high-dimensional distribution $\rho(x)$. Traditional approaches have focused on the limit of large number of data points $n$ and fixed dimension $d$. We analyze instead the regime where…

Machine Learning · Computer Science 2024-10-21 Giulio Biroli , Marc Mézard

The sharply quantized transport observed in the integer quantum Hall effect can be explained via a simple one-dimensional model with a time-periodic, adiabatically varying potential in which electronic charge is pumped from one side of the…

Optics · Physics 2021-08-05 Marius Jürgensen , Sebabrata Mukherjee , Mikael C. Rechtsman

We study the critical behaviour of Anderson localized modes near intersecting flat and dispersive bands in the quasi-one-dimensional diamond ladder with weak diagonal disorder $W$. The localization length $\xi$ of the flat band states…

Disordered Systems and Neural Networks · Physics 2013-12-23 Daniel Leykam , Sergej Flach , Omri Bahat-Treidel , Anton S. Desyatnikov

In this paper we revisit the kernel density estimation problem: given a kernel $K(x, y)$ and a dataset of $n$ points in high dimensional Euclidean space, prepare a data structure that can quickly output, given a query $q$, a…

Data Structures and Algorithms · Computer Science 2020-11-16 Moses Charikar , Michael Kapralov , Navid Nouri , Paris Siminelakis