Compressive Sampling Strategies for Multichannel Signals : Theory and Applications

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In this paper, we investigate the impact of stochasticity and large stepsizes on the implicit regularisation of gradient descent (GD) and stochastic gradient descent (SGD) over diagonal linear networks. We prove the convergence of GD and SGD with macroscop ...

We present a statistical framework to benchmark the performance of reconstruction algorithms for linear inverse problems, in particular, neural-network-based methods that require large quantities of training data. We generate synthetic signals as realizati ...

The use of meteorological radars to study snowfall microphysical properties and processes is well established, in particular via a few distinct techniques: the use of radar polarimetry, of multi-frequency radar measurements, and of the radar Doppler spectr ...

Field-programmable gate arrays (FPGAs) have become critical components in many cloud computing platforms. These devices possess the fine-grained parallelism and specialization needed to accelerate applications ranging from machine learning to networking an ...

We discuss the effects of image scanning microscopy using doughnut beam illumination on the properties of signal strength and integrated intensity. Doughnut beam illumination can give better optical sectioning and background rejection than Airy disk illumi ...

We consider the problem of compressing an information source when a correlated one is available as side information only at the decoder side, which is a special case of the distributed source coding problem in information theory. In particular, we consider ...

We consider the defocusing nonlinear wave equation ❑u D jujp ⠀1u in R3 ⠂ & UOELIG;0; 1/. We prove that for any initial datum with a scaling-subcritical norm bounded by M0 the equation is globally well-posed for p D 5 C i, where i 2 .0; ...

In recent years, there has been a significant revolution in the field of deep learning, which has demonstrated its effectiveness in automatically capturing intricate patterns from large datasets. However, the majority of these successes in Computer Vision ...

The usual explanation of the efficacy of wavelet-based methods hinges on the sparsity of many real-world objects in the wavelet domain. Yet, standard wavelet-shrinkage techniques for sparse reconstruction are not competitive in practice, one reason being t ...

The emergence of deep-learning-based methods to solve image-reconstruction problems has enabled a significant increase in quality. Unfortunately, these new methods often lack reliability and explainability, and there is a growing interest to address these ...

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision- and policy-making, and more, by comprehensively m ...

Raw data associated to the manuscript ‘’Spin wave dispersion of ultra-low damping hematite (α-Fe2O3) at GHz frequencies‘’, Physical Review Materials 7, 054407(2023); doi: 10.1103/PhysRevMaterials.7.054407 Information about file fo ...

Group testing is a technique that can reduce the number of tests needed to identify infected members in a population, by pooling together multiple diagnostic samples. Despite the variety and importance of prior results, traditional work on group testing ha ...

Low magnetic damping and high group velocity of spin waves (SWs) or magnons are two crucial parameters for functional magnonic devices. Magnonics research on signal processing and wave-based computation at GHz frequencies focused on the artificial ferrimag ...

In recent years, new emerging immersive imaging modalities, e.g. light fields, have been receiving growing attention, becoming increasingly widespread over the years. Light fields are often captured through multi-camera arrays or plenoptic cameras, with th ...

Years of a fierce competition have naturally selected the fittest deep learning algorithms. Yet, although these models work well in practice, we still lack a proper characterization of why they do so. This poses serious questions about the robustness, trus ...

Forecasting is a capability inherent in humans when navigating. Humans routinely plan their paths, considering the potential future movements of those around them. Similarly, to achieve comparable sophistication and safety, autonomous systems must embrace ...

Compressive Sampling Strategies for Multichannel Signals : Theory and Applications

Chattez avec Graph Search

(S)GD over Diagonal Linear Networks: Implicit Regularisation, Large Stepsizes and Edge of Stability

A Statistical Framework to Investigate the Optimality of Signal-Reconstruction Methods

Dual-frequency spectral radar retrieval of snowfall microphysics: a physics-driven deep-learning approach

A Visionary Look at the Security of Reconfigurable Cloud Computing

Image scanning microscopy with a doughnut beam: signal strength and integrated intensity

Neural Distributed Image Compression with Cross-Attention Feature Alignment

Global Regularity For The Nonlinear Wave Equation With Slightly Supercritical Power

Deep Learning for 3D Surface Modelling and Reconstruction

The Sparsity of Cycle Spinning for Wavelet-Based Solutions of Linear Inverse Problems

A Neural-Network-Based Convex Regularizer for Inverse Problems

A comprehensive review of digital twin-part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives

Spin wave dispersion of ultra-low damping hematite (α-Fe2O3) at GHz frequencies

Community-Aware Group Testing

Spin wave dispersion of ultra-low damping hematite ( α−Fe2O3 ) at GHz frequencies

Towards learning-based denoising of light fields

The inductive bias of deep learning: Connecting weights and functions

Deep Generative Models for Autonomous Driving: from Motion Forecasting to Realistic Image Synthesis