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In an open bounded interval $\Omega$, the problem \[ u_{tt} = u_{xx} - \big(f(\Theta)\big)_x, \Theta_t = \Theta_{xx} - f(\Theta) u_{xt}, \] is considered under the boundary conditions $u|_{\partial\Omega}=\Theta_x|_{\partial\Omega}=0$, and…

Analysis of PDEs · Mathematics 2026-02-06 Michael Winkler

We study the behavior of stochastic gradient descent applied to $\|Ax -b \|_2^2 \rightarrow \min$ for invertible $A \in \mathbb{R}^{n \times n}$. We show that there is an explicit constant $c_{A}$ depending (mildly) on $A$ such that $$…

Numerical Analysis · Mathematics 2020-09-03 Stefan Steinerberger

Stein's formula states that a random variable of the form $z^\top f(z) - \text{div} f(z)$ is mean-zero for functions $f$ with integrable gradient. Here, $\text{div} f$ is the divergence of the function $f$ and $z$ is a standard normal…

Statistics Theory · Mathematics 2020-02-10 Pierre C Bellec , Cun-Hui Zhang

Let $X_1,\ldots,X_n$ be a random sample from the Gamma distribution with density $f(x)=\lambda^{\alpha}x^{\alpha-1}e^{-\lambda x}/\Gamma(\alpha)$, $x>0$, where both $\alpha>0$ (the shape parameter) and $\lambda>0$ (the reciprocal scale…

Statistics Theory · Mathematics 2022-05-24 Nickos Papadatos

The spectral principle of Connes and Chamseddine is used as a starting point to define a discrete model for Euclidean quantum gravity. Instead of summing over ordinary geometries, we consider the sum over generalized geometries where…

High Energy Physics - Theory · Physics 2009-11-10 Luiz C. de Albuquerque , Jorge L. deLyra , Paulo Teotonio-Sobrinho

A novel algorithm is proposed for quantitative comparisons between compact surfaces embedded in the three-dimensional Euclidian space. The key idea is to identify those objects with the associated surface measures and compute a weak…

Numerical Analysis · Mathematics 2024-01-17 Kazuki Koga

Sample average approximation (SAA) replaces an intractable expected objective by an empirical average and is a basic device of modern stochastic optimization. We develop a rate theory for optimal values and empirical…

Optimization and Control · Mathematics 2026-04-29 Hien Duy Nguyen , Jacob Westerhout , Xin Guo

Exponential generalization bounds with near-tight rates have recently been established for uniformly stable learning algorithms. The notion of uniform stability, however, is stringent in the sense that it is invariant to the data-generating…

Machine Learning · Statistics 2022-06-09 Xiao-Tong Yuan , Ping Li

The possibility of mass in the context of scale-invariant, generally covariant theories, is discussed. Scale invariance is considered in the context of a gravitational theory where the action, in the first order formalism, is of the form $S…

High Energy Physics - Theory · Physics 2009-11-07 E. I. Guendelman

We study generalized uncertainty principle through the basic concepts of limit and Fourier transformation and analyze both the quantum theory of gravity and string theory from the perspective of complex function theory. Motivated from the…

High Energy Physics - Theory · Physics 2011-09-22 Bhupendra Nath Tiwari

We study the probabilistic sampling of a random variable, in which the variable is sampled only if it falls outside a given set, which is called the silence set. This helps us to understand optimal event-based sampling for the special case…

Optimization and Control · Mathematics 2023-03-17 Maben Rabi , Junfeng Wu , Vyoma Singh , Karl Henrik Johansson

A uniform shrinkage prior (USP) distribution on the unknown variance component of a random-effects model is known to produce good frequency properties. The USP has a parameter that determines the shape of its density function, but it has…

Methodology · Statistics 2017-12-12 Hyungsuk Tak

Method of parameterizing and smoothing the unknown underling distributions using Bernstein polynomials is proposed, verified and investigated. Any distribution with bounded and smooth enough density can be approximated by the proposed…

Methodology · Statistics 2015-06-23 Zhong Guan

We study the problem of estimating the score function of an unknown probability distribution $\rho^*$ from $n$ independent and identically distributed observations in $d$ dimensions. Assuming that $\rho^*$ is subgaussian and has a…

Statistics Theory · Mathematics 2024-06-13 Andre Wibisono , Yihong Wu , Kaylee Yingxi Yang

In this paper we study the problem of computing wavelet coefficients of compactly supported functions from their Fourier samples. For this, we use the recently introduced framework of generalized sampling. Our first result demonstrates that…

Numerical Analysis · Mathematics 2013-05-14 Ben Adcock , Anders C. Hansen , Clarice Poon

In contrast to the symmetries of translation in space, rotation in space, and translation in time, the known laws of physics are not universally invariant under transformation of scale. However, the action can be invariant under change of…

Data Analysis, Statistics and Probability · Physics 2019-11-05 Erik D. Fagerholm , W. M. C. Foulkes , Yasir Gallero-Salas , Fritjof Helmchen , Karl J. Friston , Rosalyn J. Moran , Robert Leech

We use the techniques developed in [1] to study the local average of random fields with spectral density $1/f^{\alpha}$. We study their scaling properties and show that the self-similarity of $1/f$ random fields is preserved under the local…

Adaptation and Self-Organizing Systems · Physics 2007-05-23 Hai Lin

This article investigates the averaging of a scalar degree of freedom that couples universally to matter. It quantifies the approximation of smoothing the matter distribution before solving the Klein--Gordon equation. In the case of Yukawa…

General Relativity and Quantum Cosmology · Physics 2025-05-08 Jean-Philippe Uzan , Hugo Lévy

In this paper, we study forms of the uncertainty principle suggested by problems in control theory. We obtain a version of the classical Paneah-Logvinenko-Sereda theorem for the annulus. More precisely, we show that a function with spectrum…

Classical Analysis and ODEs · Mathematics 2021-11-23 Walton Green , Benjamin Jaye , Mishko Mitkovski

Estimating associations between spatial covariates and responses - rather than merely predicting responses - is central to environmental science, epidemiology, and economics. For instance, public health officials might be interested in…

Machine Learning · Statistics 2025-11-11 David R. Burt , Renato Berlinghieri , Stephen Bates , Tamara Broderick
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