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We consider a dilute version of the Wigner ensemble of nxn random matrices $H$ and study the asymptotic behavior of their moments $M_{2s}$ in the limit of infinite $n$, $s$ and $\rho$, where $\rho$ is the dilution parameter. We show that in…

Mathematical Physics · Physics 2017-09-19 O. Khorunzhiy

Consider a stationary, linear Hilbert space valued process. We establish Berry-Essen type results with optimal convergence rates under sharp dependence conditions on the underlying coefficient sequence of the linear operators. The case of…

Probability · Mathematics 2017-08-01 Moritz Jirak

A new type of dependent thinning for point processes in continuous space is proposed, which leverages the advantages of determinantal point processes defined on finite spaces and, as such, is particularly amenable to statistical, numerical,…

Machine Learning · Computer Science 2019-06-19 Bartłomiej Błaszczyszyn , Paul Keeler

The characterization of quantum processes is a key tool in quantum information processing tasks for several reasons: on one hand, it allows to acknowledge errors in the implementations of quantum algorithms; on the other, it allows to…

Quantum Physics · Physics 2018-12-12 Ignacio Perito , Augusto Roncaglia , Ariel Bendersky

Many economic and causal parameters depend on nonparametric or high dimensional first steps. We give a general construction of locally robust/orthogonal moment functions for GMM, where moment conditions have zero derivative with respect to…

In this article, we present a unified algebraic-combinatorial framework for computing explicit, piecewise rational, and combinatorially indexed parametric formulas for volumes and higher moments of slices and slabs of polyhedral norm balls.…

Combinatorics · Mathematics 2026-03-27 Marie-Charlotte Brandenburg , Jesús A. De Loera , Yu Luo , Chiara Meroni

We study the distribution of work induced by the two-point measurement protocol for a driven open quantum system. We first derive a general form for the generating function of work for the total system, bearing in mind that the Hamiltonian…

Quantum Physics · Physics 2014-10-01 S. Suomela , P. Solinas , J. P. Pekola , J. Ankerhold , T. Ala-Nissila

This paper proposes a semidefinite programming based method for estimating moments of a stochastic hybrid system (SHS). For polynomial SHSs -- which consist of polynomial continuous vector fields, reset maps, and transition intensities --…

Optimization and Control · Mathematics 2018-02-02 Khem Raj Ghusinga , Andrew Lamperski , Abhyudai Singh

We study three instances of log-correlated processes on the interval: the logarithm of the Gaussian unitary ensemble (GUE) characteristic polynomial, the Gaussian log-correlated potential in presence of edge charges, and the Fractional…

Mathematical Physics · Physics 2016-06-14 Yan V. Fyodorov , Pierre Le Doussal

By taking the need for quantum reference frames into account, it is shown that Hilbert-space factorization is a dissipative process requiring on the order of kT to reduce by one bit an observer's uncertainty in the provenance of a…

Quantum Physics · Physics 2014-02-07 Chris Fields

A moment approach for orbit determinations of astrometric binaries from astrometric observations alone has been recently studied for a low signal-to-noise ratio (Iwama et al. 2013, PASJ, 65, 2). With avoiding a direct use of the…

High Energy Astrophysical Phenomena · Physics 2015-06-22 Kei Yamada , Masaki Yamaguchi , Hideki Asada , Naoteru Gouda

In this PhD thesis we propose an algorithmic approach to the study of the Hilbert scheme. Developing algorithmic methods, we also obtain general results about Hilbert schemes. In Chapter 1 we discuss the equations defining the Hilbert…

Algebraic Geometry · Mathematics 2012-02-21 Paolo Lella

A hybrid stochastic-deterministic approach for computing the second-order perturbative contribution $E^{(2)}$ within multireference perturbation theory (MRPT) is presented. The idea at the heart of our hybrid scheme --- based on a…

Chemical Physics · Physics 2018-05-31 Yann Garniron , Anthony Scemama , Pierre-François Loos , Michel Caffarel

The Huber loss is a robust loss function used for a wide range of regression tasks. To utilize the Huber loss, a parameter that controls the transitions from a quadratic function to an absolute value function needs to be selected. We…

Machine Learning · Statistics 2020-11-20 Gregory P. Meyer

We study moments of characteristic polynomials of truncated Haar distributed matrices from the three classical compact groups O(N), U(N) and Sp(2N). For finite matrix size we calculate the moments in terms of hypergeometric functions of…

Mathematical Physics · Physics 2021-11-16 Alexander Serebryakov , Nick Simm , Guillaume Dubach

To improve accuracy and speed of regressions and classifications, we present a data-based prediction method, Random Bits Regression (RBR). This method first generates a large number of random binary intermediate/derived features based on…

Machine Learning · Statistics 2016-11-04 Yi Wang , Yi Li , Momiao Xiong , Li Jin

We introduce a framework to derive quantitative central limit theorems in the context of non-linear approximation of Gaussian random variables taking values in a separable Hilbert space. In particular, our method provides an alternative to…

Probability · Mathematics 2020-11-25 Solesne Bourguin , Simon Campese

Non-orthogonal bases of projectors on coherent states are introduced to expand hermitean operators acting on the Hilbert space of a spin s. It is shown that the expectation values of a hermitean operator A in a family of (2s+1)(2s+1)…

Quantum Physics · Physics 2015-06-26 Jean-Pierre Amiet , Stefan Weigert

A moment approach for orbit determinations of an astrometric binary with low signal-to-noise ratio from astrometric observations alone is proposed, especially aiming at a close binary system with a short orbital period such as Cyg-X1 and…

Solar and Stellar Astrophysics · Physics 2015-06-11 Hirofumi Iwama , Hideki Asada , Kei Yamada

When solving partial differential equations numerically, usually a high order spatial discretisation is needed. Model order reduction (MOR) techniques are often used to reduce the order of spatially-discretised systems and hence reduce…

Optimization and Control · Mathematics 2017-09-19 Martin Redmann