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We study an inverse source problem for the acoustic wave equation in a random waveguide. The goal is to estimate the source of waves from measurements of the acoustic pressure at a remote array of sensors. The waveguide effect is due to…

Analysis of PDEs · Mathematics 2015-02-25 Sebastian Acosta , Ricardo Alonso , Liliana Borcea

Achieving the ultimate precisions for multiple parameters simultaneously is an outstanding challenge in quantum physics, because the optimal measurements for incompatible parameters cannot be performed jointly due to the Heisenberg…

Quantum Physics · Physics 2023-10-12 Binke Xia , Jingzheng Huang , Hongjing Li , Han Wang , Guihua Zeng

Many spatial phenomena exhibit treatment interference where treatments at one location may affect the response at other locations. Because interference violates the stable unit treatment value assumption, standard methods for causal…

Methodology · Statistics 2020-07-02 Andrew Giffin , Brian Reich , Shu Yang , Ana Rappold

Curie's principle states that "when effects show certain asymmetry, this asymmetry must be found in the causes that gave rise to them". We demonstrate that symmetry equivariant neural networks uphold Curie's principle and can be used to…

Machine Learning · Computer Science 2021-01-20 Tess E. Smidt , Mario Geiger , Benjamin Kurt Miller

Modified Rayleigh conjecture (MRC) in scattering theory is proposed and justified. MRC allows one to develop numerical algorithms for solving direct scattering problems related to acoustic wave scattering by soft and hard obstacles of…

Mathematical Physics · Physics 2009-11-10 A. G. Ramm

This paper discusses the problem of adaptive estimation of a univariate object like the value of a regression function at a given point or a linear functional in a linear inverse problem. We consider an adaptive procedure originated from…

Statistics Theory · Mathematics 2009-08-26 Vladimir Spokoiny , Céline Vial

In this paper, we present a new strategy to prove the convergence of deep learning architectures to a zero training (or even testing) loss by gradient flow. Our analysis is centered on the notion of Rayleigh quotients in order to prove…

Machine Learning · Computer Science 2023-01-20 David A. R. Robin , Kevin Scaman , Marc Lelarge

A 'Winner's Curse' arises in large-scale online experimentation platforms when the same experiments are used to both select treatments and evaluate their effects. In these settings, classical difference-in-means estimators of treatment…

Methodology · Statistics 2025-11-11 Richard Mudd , Rina Friedberg , Ilya Gorbachev , Houssam Nassif , Abbas Zaidi

Predictive rate-distortion analysis suffers from the curse of dimensionality: clustering arbitrarily long pasts to retain information about arbitrarily long futures requires resources that typically grow exponentially with length. The…

Statistical Mechanics · Physics 2014-12-10 Sarah Marzen , James P. Crutchfield

We discuss a parameter estimation problem using quantum decoherece in the double-slit interferometer. We consider a particle coupled to a massive scalar field after the particle passing through the double slit and solve the dynamics…

Quantum Physics · Physics 2018-07-18 Akira Matsumura , Taishi Ikeda , Shingo Kukita

This article develops a quality notion that is complementary to the system notion. As a major consequence, it becomes clear why quality can be measured only to a certain extend based on the issues of validity and incompleteness. First,…

Software Engineering · Computer Science 2016-09-21 Johannes Reich

It is difficult to extract reliable criteria for causal locality from the limited ingredients found in textbook quantum theory. In the end, Bell humbly warned that his eponymous theorem was based on criteria that "should be viewed with the…

Quantum Physics · Physics 2024-02-28 Jacob A. Barandes

The estimation of the number of point-sources in the sky is one the oldest problems in astronomy, yet an easy and efficient method for estimating the uncertainty on these counts is still an open problem. Probabilistic cataloging solves the…

Instrumentation and Methods for Astrophysics · Physics 2025-08-01 Gabriel H. Collin

Bayesian inference is used to estimate continuous parameter values given measured data in many fields of science. The method relies on conditional probability densities to describe information about both data and parameters, yet the notion…

Methodology · Statistics 2025-03-25 Klaus Mosegaard , Andrew Curtis

We analyze the fundamental quantum limit of the resolution of an optical imaging system from the perspective of the detection problem of deciding whether the optical field in the image plane is generated by one incoherent on-axis source…

Quantum Physics · Physics 2018-12-10 Xiao-Ming Lu , Hari Krovi , Ranjith Nair , Saikat Guha , Jeffrey H. Shapiro

Calibration parameters in deterministic computer experiments are those attributes that cannot be measured or available in physical experiments. Kennedy and O'Hagan \cite{kennedy2001bayesian} suggested an approach to estimate them by using…

Methodology · Statistics 2015-08-31 Rui Tuo , C. F. Jeff Wu

Recent accounts of probability in the many worlds interpretation of quantum mechanics are vulnerable due to their dependence on probability theory per se. For this reason, the many worlds interpretation continues to suffer from the…

Quantum Physics · Physics 2025-10-06 Michael Ridley

We propose a test of the principle of relativity, involving quantum signals between two inertial frames. If the principle is upheld, classical causality will appear to be split in a dramatic and emphatic way. We discuss the existence of…

General Relativity and Quantum Cosmology · Physics 2007-05-23 George Jaroszkiewicz

We study the estimation of a single parameter characterizing families of unitary transformations acting on two systems. We consider the situation with the presence of bottleneck, i.e. only one of the systems can be measured to gather…

Quantum Physics · Physics 2020-10-06 Milajiguli Rexiti , Stefano Mancini

One of the most fundamental problems in causal inference is the estimation of a causal effect when variables are confounded. This is difficult in an observational study, because one has no direct evidence that all confounders have been…

Machine Learning · Statistics 2014-11-03 Ricardo Silva , Robin Evans
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