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We investigate performance bounds for feedback control of distributed plants where the controller can be centralized (i.e. it has access to measurements from the whole plant), but sensors only measure differences between neighboring…

Optimization and Control · Mathematics 2013-09-26 Alain Sarlette , Rodolphe Sepulchre

We have measured the spatial and spectral dependence of the microwave field inside an open absorbing waveguide filled with randomly juxtaposed dielectric slabs in the spectral region in which the average level spacing exceeds the typical…

Disordered Systems and Neural Networks · Physics 2009-11-11 Patrick Sebbah , Bing Hu , Jerome M. Klosner , Azriel Z. Genack

In many physical situations involving diverse length scales, waves or rays representing them travel through media characterized by spatially smooth, random, modest refactive index variations. "Primary" diffraction (by individual…

Classical Physics · Physics 2021-12-14 Eric J Heller , Ragnar Fleischmann , Tobias Kramer

Observing a load process above high thresholds, modeling it as a pulse process with random occurrence times and magnitudes, and extrapolating life-time maximum or design loads from the data is a common task in structural reliability…

Applications · Statistics 2015-07-28 Baidurya Bhattacharya

We show that transverse-magnetic (TM) leaky modes can propagate further than transverse electric (TE) modes in real-index dielectric waveguides. We compute the density of states and find that while the TE spectrum contains only overlapping…

Optics · Physics 2018-04-26 Adi Pick , Nimrod Moiseyev

This study aims to develop the limit theorems on the sample autocovariances and sample autocorrelations for certain stationary infinitely divisible processes. We consider the case where the infinitely divisible process has heavy tail…

Probability · Mathematics 2014-06-17 Takashi Owada

Recent results from compressive sampling (CS) have demonstrated that accurate reconstruction of sparse signals often requires far fewer samples than suggested by the classical Nyquist--Shannon sampling theorem. Typically, signal…

Fluid Dynamics · Physics 2014-04-24 Gudmundur F. Adalsteinsson , Nicholas K. -R. Kevlahan

Statistical properties of the front of a semi-infinite system of single-file diffusion (one dimensional system where particles cannot pass each other, but in-between collisions each one independently follow diffusive motion) are…

Statistical Mechanics · Physics 2007-05-23 Sanjib Sabhapandit

Diffusion models have demonstrated state-of-the-art performance across vision, language, and scientific domains. Despite their empirical success, prior theoretical analyses of the sample complexity suffer from poor scaling with input data…

Machine Learning · Computer Science 2026-04-14 Mudit Gaur , Prashant Trivedi , Sasidhar Kunapuli , Amrit Singh Bedi , Vaneet Aggarwal

Despite their widespread use in practice, the asymptotic properties of Bayesian penalized splines have not been investigated so far. We close this gap and study posterior concentration rates for Bayesian penalized splines in a Gaussian…

Statistics Theory · Mathematics 2022-03-24 Paul Bach , Nadja Klein

Phase transitions not allowed in equilibrium steady states may happen however at the fluctuating level. We observe for the first time this striking and general phenomenon measuring current fluctuations in an isolated diffusive system. While…

Statistical Mechanics · Physics 2011-10-31 Pablo I. Hurtado , Pedro L. Garrido

We study partial sums limits of linear random fields $X$ on $\mathbb{Z}^2 $ with spectral density $f({\mathbf x}) $ tending to $\infty,\, 0$ or to both (along different subsequences) as ${\mathbf x} \to (0,0)$. The above behaviors are…

Probability · Mathematics 2023-01-06 Donatas Surgailis

The transmission of a wave through a randomly chosen `pile of plates' typically decreases exponentially with the number of plates, a phenomenon closely related to Anderson localisation. In apparent contradiction we construct disordered…

Classical Physics · Physics 2017-05-01 Chris King , Simon Horsley , Tom Philbin

Learning discrete distributions from i.i.d. samples is a well-understood problem. However, advances in generative machine learning prompt an interesting new, non-i.i.d. setting: after receiving a certain number of samples, an estimated…

Information Theory · Computer Science 2026-01-06 Millen Kanabar , Michael Gastpar

It is becoming more and more clear that complex networks present remarkable large fluctuations. These fluctuations may manifest differently according to the given model. In this paper we re-consider hidden variable models which turn out to…

Disordered Systems and Neural Networks · Physics 2014-02-19 Massimo Ostilli

Optimal Dirichlet boundary control for a fractional/normal evolution with a final observation is considered. The unique existence of the solution and the first-order optimality condition of the optimal control problem are derived. The…

Numerical Analysis · Mathematics 2020-07-20 Qin Zhou , Binjie Li

The problem of decentralized detection in a sensor network subjected to a total average power constraint and all nodes sharing a common bandwidth is investigated. The bandwidth constraint is taken into account by assuming non-orthogonal…

Information Theory · Computer Science 2007-07-13 Sudharman K. Jayaweera

Multiple stochastic signals possess inherent statistical correlations, yet conventional sampling methods that process each channel independently result in data redundancy. To leverage this correlation for efficient sampling, we model…

Signal Processing · Electrical Eng. & Systems 2025-09-18 Lin Jin , Hang Sheng , Hui Feng , Bo Hu

The standard small-time functional central limit theorem of semimartingales has been established in (Gerhold, S., Kleinert, M., Porkert, P., and Shkolnikov, M. (2015). Small time central limit theorems for semimartingales with applications.…

Probability · Mathematics 2026-05-18 Pietro Maria Sparago

Diffusion Models (DMs) iteratively denoise random samples to produce high-quality data. The iterative sampling process is derived from Stochastic Differential Equations (SDEs), allowing a speed-quality trade-off chosen at inference. Another…

Machine Learning · Computer Science 2024-09-27 Mattias Cross , Anton Ragni
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