From Random Matrices to Stochastic Operators

We propose that classical random matrix models are properly viewed as finite difference schemes for stochastic differential operators. Three particular stochastic operators commonly arise, each associated with a familiar class of local eigenvalue behavior. The stochastic Airy operator displays soft edge behavior, associated with the Airy kernel. The stochastic Bessel operator displays hard edge behavior, associated with the Bessel kernel. The article concludes with suggestions for a stochastic sine operator, which would display bulk behavior, associated with the sine kernel.Comment: 41 pages, 5 figures. Submitted to Journal of Statistical Physics. Changes in this revision: recomputed Monte Carlo simulations, added reference [19], fit into margins, performed minor editin

Presurgical thalamic hubness predicts surgical outcome in temporal lobe epilepsy.

OBJECTIVE: To characterize the presurgical brain functional architecture presented in patients with temporal lobe epilepsy (TLE) using graph theoretical measures of resting-state fMRI data and to test its association with surgical outcome. METHODS: Fifty-six unilateral patients with TLE, who subsequently underwent anterior temporal lobectomy and were classified as obtaining a seizure-free (Engel class I, n = 35) vs not seizure-free (Engel classes II-IV, n = 21) outcome at 1 year after surgery, and 28 matched healthy controls were enrolled. On the basis of their presurgical resting-state functional connectivity, network properties, including nodal hubness (importance of a node to the network; degree, betweenness, and eigenvector centralities) and integration (global efficiency), were estimated and compared across our experimental groups. Cross-validations with support vector machine (SVM) were used to examine whether selective nodal hubness exceeded standard clinical characteristics in outcome prediction. RESULTS: Compared to the seizure-free patients and healthy controls, the not seizure-free patients displayed a specific increase in nodal hubness (degree and eigenvector centralities) involving both the ipsilateral and contralateral thalami, contributed by an increase in the number of connections to regions distributed mostly in the contralateral hemisphere. Simulating removal of thalamus reduced network integration more dramatically in not seizure-free patients. Lastly, SVM models built on these thalamic hubness measures produced 76% prediction accuracy, while models built with standard clinical variables yielded only 58% accuracy (both were cross-validated). CONCLUSIONS: A thalamic network associated with seizure recurrence may already be established presurgically. Thalamic hubness can serve as a potential biomarker of surgical outcome, outperforming the clinical characteristics commonly used in epilepsy surgery centers

Airy processes and variational problems

We review the Airy processes; their formulation and how they are conjectured to govern the large time, large distance spatial fluctuations of one dimensional random growth models. We also describe formulas which express the probabilities that they lie below a given curve as Fredholm determinants of certain boundary value operators, and the several applications of these formulas to variational problems involving Airy processes that arise in physical problems, as well as to their local behaviour.Comment: Minor corrections. 41 pages, 4 figures. To appear as chapter in "PASI Proceedings: Topics in percolative and disordered systems

On the Distribution of a Second Class Particle in the Asymmetric Simple Exclusion Process

We give an exact expression for the distribution of the position X(t) of a single second class particle in the asymmetric simple exclusion process (ASEP) where initially the second class particle is located at the origin and the first class particles occupy the sites {1,2,...}

{\bf τ\tau-Function Evaluation of Gap Probabilities in Orthogonal and Symplectic Matrix Ensembles}

It has recently been emphasized that all known exact evaluations of gap probabilities for classical unitary matrix ensembles are in fact $\tau$-functions for certain Painlev\'e systems. We show that all exact evaluations of gap probabilities for classical orthogonal matrix ensembles, either known or derivable from the existing literature, are likewise $\tau$-functions for certain Painlev\'e systems. In the case of symplectic matrix ensembles all exact evaluations, either known or derivable from the existing literature, are identified as the mean of two $\tau$-functions, both of which correspond to Hamiltonians satisfying the same differential equation, differing only in the boundary condition. Furthermore the product of these two $\tau$-functions gives the gap probability in the corresponding unitary symmetry case, while one of those $\tau$-functions is the gap probability in the corresponding orthogonal symmetry case.Comment: AMS-Late

Control of Integrable Hamiltonian Systems and Degenerate Bifurcations

We discuss control of low-dimensional systems which, when uncontrolled, are integrable in the Hamiltonian sense. The controller targets an exact solution of the system in a region where the uncontrolled dynamics has invariant tori. Both dissipative and conservative controllers are considered. We show that the shear flow structure of the undriven system causes a Takens-Bogdanov birfurcation to occur when control is applied. This implies extreme noise sensitivity. We then consider an example of these results using the driven nonlinear Schrodinger equation.Comment: 25 pages, 11 figures, resubmitted to Physical Review E March 2004 (originally submitted June 2003), added content and reference

Universal Parametric Correlations of Eigenvalues of Random Matrix Ensemble

Eigenvalue correlations of random matrix ensembles as a function of an external perturbation are investigated vis the Dyson Brownian Motion Model in the situation where the level density has a hard edge singularity. By solving a linearized hydrodynamical equation, a universal dependence of the density-density correlator on the external field is found. As an application we obtain a formula for the variance of linear statistics with the parametric dependence exhibited as a Laplace transform.Comment: 23 pages, late

Edgeworth Expansion of the Largest Eigenvalue Distribution Function of GUE Revisited

We derive expansions of the resolvent Rn(x;y;t)=(Qn(x;t)Pn(y;t)-Qn(y;t)Pn(x;t))/(x-y) of the Hermite kernel Kn at the edge of the spectrum of the finite n Gaussian Unitary Ensemble (GUEn) and the finite n expansion of Qn(x;t) and Pn(x;t). Using these large n expansions, we give another proof of the derivation of an Edgeworth type theorem for the largest eigenvalue distribution function of GUEn. We conclude with a brief discussion on the derivation of the probability distribution function of the corresponding largest eigenvalue in the Gaussian Orthogonal Ensemble (GOEn) and Gaussian Symplectic Ensembles (GSEn)

Variance Calculations and the Bessel Kernel

In the Laguerre ensemble of N x N (positive) hermitian matrices, it is of interest both theoretically and for applications to quantum transport problems to compute the variance of a linear statistic, denoted var_N f, as N->infinity. Furthermore, this statistic often contains an additional parameter alpha for which the limit alpha->infinity is most interesting and most difficult to compute numerically. We derive exact expressions for both lim_{N->infinity} var_N f and lim_{alpha->infinity}lim_{N->infinity} var_N f.Comment: 7 pages; resubmitted to make postscript compatibl

A simple construction of elliptic RR-matrices

We show that Belavin's solutions of the quantum Yang--Baxter equation can be obtained by restricting an infinite $R$-matrix to suitable finite dimensional subspaces. This infinite $R$-matrix is a modified version of the Shibukawa--Ueno $R$-matrix acting on functions of two variables.Comment: 6 page