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We introduce estimatable variation neural networks (EVNNs), a class of neural networks that allow a computationally cheap estimate on the $BV$ norm motivated by the space $BMV$ of functions with bounded M-variation. We prove a universal…

Numerical Analysis · Mathematics 2025-11-14 Mária Lukáčová-Medviďová , Simon Schneider

A novel approach to Forecast Error Variance Decompositions (FEVD) in nonlinear Structural Vector Autoregressive models with Gaussian innovations is proposed, called the Hermite FEVD (HFEVD). This method employs a Hermite polynomial…

Econometrics · Economics 2025-10-03 Quinlan Lee

Accurately calculating time delays between signals is pivotal in many modern physics applications. One approach to estimating these delays is computing the cross-spectrum in the time-frequency domain. Linear time-frequency representations,…

Computational Physics · Physics 2026-03-23 L. de A. Gurgel , J. M. de Araújo , L. D. Machado , P. D. S. de Lima

We consider the asymptotics of the one-dimensional cubic nonlinear Schr\"odinger equation with an external potential $V$ that does not admit bound states. Assuming that $\jBra{x}^{2+}V(x) \in L^1$ and that $u$ is orthogonal to any…

Analysis of PDEs · Mathematics 2024-09-26 Gavin Stewart

This paper delves into an in-depth exploration of the Variable Projection (VP) algorithm, a powerful tool for solving separable nonlinear optimization problems across multiple domains, including system identification, image processing, and…

Optimization and Control · Mathematics 2025-01-08 Guangyong Chen , Peng Xue , Min Gan , Jing Chen , Wenzhong Guo , C. L. Philip. Chen

We give a variational formulation for $-\log\mathbb{E}_\nu\left[e^{-f}|\mathcal{F}_t\right]$ for a large class of measures $\nu$. We give a refined entropic characterization of the invertibility of some perturbations of the identity. We…

Probability · Mathematics 2016-12-02 Kévin Hartmann

In this paper approximation methods for infinite-dimensional Levy processes, also called (time-dependent) Levy fields, are introduced. For square integrable fields beyond the Gaussian case, it is no longer given that the one-dimensional…

Probability · Mathematics 2017-12-14 Andrea Barth , Andreas Stein

We study the asymptotic properties of the small data solutions of the Vlasov-Maxwell system in dimension three. No neutral hypothesis nor compact support assumptions are made on the data. In particular, the initial decay in the velocity…

Analysis of PDEs · Mathematics 2020-07-07 Léo Bigorgne

We consider the problem of enabling robust range estimation of eigenvalue decomposition (EVD) algorithm for a reliable fixed-point design. The simplicity of fixed-point circuitry has always been so tempting to implement EVD algo- rithms in…

Numerical Analysis · Computer Science 2017-11-30 Bibek Kabi , Anand S Sahadevan , Tapan Pradhan

For slowly varying fields the vacuum functional of a quantum field theory may be expanded in terms of local functionals. This expansion satisfies its own form of the Schr\"odinger equation from which the expansion coefficents can be found.…

High Energy Physics - Theory · Physics 2009-10-30 Paul Mansfield , Marcos Sampaio , Jiannis Pachos

By combining the Malliavin calculus with Fourier techniques, we develop a high-order asymptotic expansion theory for a sequence of vector-valued random variables. Our asymptotic expansion formulas give the development of the characteristic…

Probability · Mathematics 2019-09-20 Ciprian Tudor , Nakahiro Yoshida

Variable Elimination (VE) is a classical exact inference algorithm for probabilistic graphical models such as Bayesian Networks, computing the marginal distribution of a subset of the random variables in the model. Our goal is to understand…

Logic in Computer Science · Computer Science 2025-01-28 Thomas Ehrhard , Claudia Faggian , Michele Pagani

We discuss the gauge symmetry breaking of six dimensional theories in flux compactification with a magnetic flux background and a constant vacuum expectation value (VEV) for the scalar fields, which are zero modes of extra spatial…

High Energy Physics - Theory · Physics 2022-09-14 Kento Akamatsu , Takuya Hirose , Nobuhito Maru

The variational autoencoder (VAE) is a popular, deep, latent-variable model (DLVM) due to its simple yet effective formulation for modeling the data distribution. Moreover, optimizing the VAE objective function is more manageable than other…

Machine Learning · Computer Science 2025-01-28 Surojit Saha , Sarang Joshi , Ross Whitaker

We introduce a new class $\mathcal{FV}(\Omega,E)$ of spaces of weighted functions on a set $\Omega$ with values in a locally convex Hausdorff space $E$ which covers many classical spaces of vector-valued functions like continuous, smooth,…

Functional Analysis · Mathematics 2021-04-08 Karsten Kruse

This paper deals with determination of turbulent convection velocities from particle image velocimetry (PIV). Turbulent convection velocities are of interest because they can be used to map temporal information into space. Convection…

Fluid Dynamics · Physics 2013-11-26 Roeland de Kat , Lian Gan , James R Dawson , Bharathram Ganapathisubramani

In this work we revisit the process of constructing wave equations for the scalar and vector potentials of an electromagnetic field, and show that a wave equation with an arbitrary velocity (including a velocity higher than the velocity of…

Classical Physics · Physics 2007-05-23 Andrew E. Chubykalo , Vladimir Onoochin

We analyze the static QCD potential V_QCD(r) in the distance region 0.1 fm < r < 1 fm using perturbative QCD and OPE as basic theoretical tools. We assemble theoretical developments up to date and perform a solid and accurate analysis. The…

High Energy Physics - Phenomenology · Physics 2008-11-26 Y. Sumino

This paper introduces the Descriptive Variational Autoencoder (DVAE), an unsupervised and end-to-end trainable neural network for predicting vehicle trajectories that provides partial interpretability. The novel approach is based on the…

Machine Learning · Computer Science 2021-06-25 Marion Neumeier , Andreas Tollkühn , Thomas Berberich , Michael Botsch

Variational Bayes (VB) provides a computationally efficient alternative to Markov Chain Monte Carlo, especially for high-dimensional and large-scale inference. However, existing theory on VB primarily focuses on fixed-dimensional settings…

Statistics Theory · Mathematics 2025-08-05 Jiawei Yan , Peirong Xu , Tao Wang