Plenoptic Spherical Sampling

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Recently, the R2D2 paradigm, standing for ‘Residual-to-Residual DNN series for high-Dynamic-range imaging’, was introduced for image formation in Radio Interferometry (RI) as a learned version of the traditional algorithm CLEAN. The first incarnations of R ...

Consider the wave equation with heterogeneous coefficients in the homogenization regime. At large times, the wave interacts in a nontrivial way with the heterogeneities, giving rise to effective dispersive effects. The main achievement of the present wor ...

Situational awareness strategies are essential for the reliable and secure operation of the electric power grid which represents critical infrastructure in modern society. With the rise of converter-interfaced renewable generation and the consequent shift ...

We generalize and provide a linear algebra-based perspective on a finite element (FE) ho-mogenization scheme, pioneered by Schneider et al. (2017)[1] and Leuschner and Fritzen (2018)[2]. The efficiency of the scheme is based on a preconditioned, well-scale ...

The interior transmission eigenvalue problem is a system of partial differential equations equipped with Cauchy data on the boundary: the transmission conditions. This problem appears in the inverse scattering theory for inhomogeneous media when, for some ...

Object-centric learning has gained significant attention over the last years as it can serve as a powerful tool to analyze complex scenes as a composition of simpler entities. Well-established tasks in computer vision, such as object detection or instance ...

Polynomial neural networks (PNNs) have been recently shown to be particularly effective at image generation and face recognition, where high-frequency information is critical. Previous studies have revealed that neural networks demonstrate a spectral bias ...

We present a novel technique of neutron noise detection and experimental data interpretation developed during the EU H2020 project CORTEX aiming to improve the capabilities for identification and localization of neutron noise sources. The experimental data ...

The optimization of surface finish to improve performance, such as adhesion, friction, wear, fatigue life, or interfacial transport, occurs largely through trial and error, despite significant advancements in the relevant science. There are three central c ...

We consider distributed optimization over several devices, each sending incremental model updates to a central server. This setting is considered, for instance, in federated learning. Various schemes have been designed to compress the model updates in orde ...

As historical stone masonry structures are vulnerable and prone to damage in earthquakes, investigating their structural integrity is important to reduce injuries and casualties while preserving their historical value. Stone masonry is a composite material ...

Spectral analysis of atrial signals has been used to identify regions of interest in atrial fibrillation (AF). However, the relationship to the atrial substrate is unclear. In this study, we compare regions with dominant frequency (DF), simultaneously dete ...

We prove that every Schwartz function in Euclidean space can be completely recovered given only its restrictions and the restrictions of its Fourier transform to all origin-centered spheres whose radii are square roots of integers. In particular, the only ...

We use spherical cap harmonic (SCH) basis functions to analyse and reconstruct the morphology of scanned genus-0 rough surface patches with open edges. We first develop a novel one-to-one conformal mapping algorithm with minimal area distortion for paramet ...

In many applications, such as textiles, fibreglass, paper and several kinds of biological fibrous tissues, the main load-bearing constituents at the micro-scale are arranged as a fibre network. In these materials, rupture is usually driven by micro-mechani ...

We consider the problem of finding a saddle point for the convex-concave objective

\min_x \max_y f(x) + \langle Ax, y\rangle - g^*(y)

, where

f

is a convex function with locally Lipschitz gradient and

g

is convex and possibly non-smooth. We propose an ...

Stochastic Maximum Likelihood (SML) is a popular direction of arrival (DOA) estimation technique in array signal processing. It is a parametric method that jointly estimates signal and instrument noise by maximum likelihood, achieving excellent statistical ...

Steep streams on rough beds are generally characterised by supercritical flow conditions under which antidunes can develop and migrate over time. In this paper, we present flume experiments that we conducted to investigate the variability of antidune geome ...

Super-resolution opticalfluctuation imaging provides a resolution beyond the diffraction limitby analysing stochasticfluorescencefluctuations with higher-order statistics. Usingnthorderspatio-temporal cross-cumulants the spatial resolution and the sampling ...

Many scientific inquiries in natural sciences involve approximating a spherical field -namely a scalar quantity defined over a continuum of directions- from generalised samples of the latter (e.g. directional samples, local averages, etc). Such an approxim ...

Plenoptic Spherical Sampling

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S-R2D2: a spherical extension of the R2D2 deep neural network series paradigm for wide-field radio-interferometric imaging

A Spectral Ansatz for The Long-Time Homogenization of The Wave Equation

Functional-Basis Analysis of Non-Stationary Signals in Modern Power Grids: Theory and Implementation in Embedded Systems

An optimal preconditioned FFT-accelerated finite element solver for homogenization

Spectral analysis for transmission eigenvalue problems with and without the complementing conditions

Unsupervised Visual Entity Abstraction towards 2D and 3D Compositional Models

The spectral bias of polynomial neural networks

Characterization of High Harmonic Frequencies in Reactor Noise Experiments Within the CORTEX Project

Contact.engineering-Create, analyze and publish digital surface twins from topography measurements across many scales

Stochastic distributed learning with gradient quantization and double-variance reduction

Geometrical Treatise on the Modelling of 3D Particulate Inclusion-Matrix Microstructures with an Application to Historical Stone Masonry Walls

Correlation between conduction velocity and frequency analysis in patients with atrial fibrillation using high-density charge mapping

Fourier uniqueness and interpolation in Euclidean space

Spherical cap harmonic analysis (SCHA) for characterising the morphology of rough surface patches

Damage-driven strain localisation in networks of fibres: A computational homogenisation approach

A first-order primal-dual method with adaptivity to local smoothness

SiML: Sieved Maximum Likelihood for Array Signal Processing

The variability of antidune morphodynamics on steep slopes

Spectral cross-cumulants for multicolor super-resolved SOFI imaging

Functional Inverse Problems on Spheres: Theory, Algorithms and Applications