Frequency-Domain Diffusion Adaptation Over Networks

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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 ...

This paper proposes closed-form expressions for the calculation of the ground-return impedance and admittance of underground cables. The proposed expressions are shown to reproduce the integral equations of Xue with great accuracy for a wide range of groun ...

There has been a recent surge of interest in the study of asymptotic reconstruction performance in various cases of generalized linear estimation problems in the teacher-student setting, especially for the case of i.i.d standard normal matrices. Here, we g ...

Context. We report the exploitation of a sample of Solar System observations based on data from the third Gaia Data Release (Gaia DR3) of nearly 157 000 asteroids. It extends the epoch astrometric solution over the time coverage planned for the Gaia DR4, w ...

Achieving reactive robot behavior in complex dynamic environments is still challenging as it relies on being able to solve trajectory optimization problems quickly enough, such that we can replan the future motion at frequencies which are sufficiently high ...

Motivated by the recent successes of neural networks that have the ability to fit the data perfectly \emph{and} generalize well, we study the noiseless model in the fundamental least-squares setup. We assume that an optimum predictor fits perfectly inputs ...

We study the problem of estimating an unknown function from noisy data using shallow ReLU neural networks. The estimators we study minimize the sum of squared data-fitting errors plus a regularization term proportional to the squared Euclidean norm of the ...

Many classical Computer Vision problems, such as essential matrix computation and pose estimation from 3D to 2D correspondences, can be tackled by solving a linear least-square problem, which can be done by finding the eigenvector corresponding to the smal ...

Conformal phased arrays can be found in many applications due to their ability to fit tridimensional surfaces and, thanks to their scanning performance, can excel planar arrays. However, most of the previously proposed analysis methods can be applied only ...

Least squares estimators, when trained on a few target domain samples, may predict poorly. Supervised domain adaptation aims to improve the predictive accuracy by exploiting additional labeled training samples from a source distribution that is close to th ...

Purpose: To develop a self-navigated motion compensation strategy for 3D radial MRI that can compensate for continuous head motion by measuring rigid body motion parameters with high temporal resolution from the central k-space acquisition point (self-enco ...

In this paper, we focus on the localization of a passive source from time difference of arrival (TDOA) measurements. TDOA values are computed with respect to pairs of fixed sensors that are required to be accurately time-synchronized. This constitutes a we ...

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 ...

Advances in high-speed imaging techniques have opened new possibilities for capturing ultrafast phenomena such as light propagation in air or through media. Capturing light in flight in three-dimensional xyt space has been reported based on various types o ...

We introduce Neural Network (NN for short) approximation architectures for the numerical solution of Boundary Integral Equations (BIEs for short). We exemplify the proposed NN approach for the boundary reduction of the potential problem in two spatial dime ...

Multi-component T2 relaxometry allows probing tissue microstructure by assessing compartment-specific T2 relaxation times and water fractions, including the myelin water fraction. Non-negative least squares (NNLS) with zero-order Tikhonov regularization is ...

This thesis focuses on two kinds of statistical inference problems in signal processing and data science. The first problem is the estimation of a structured informative tensor from the observation of a noisy tensor in which it is buried. The structure com ...

Functional time series is a temporally ordered sequence of not necessarily independent random curves. While the statistical analysis of such data has been traditionally carried out under the assumption of completely observed functional data, it may well ha ...

Frequency-Domain Diffusion Adaptation Over Networks

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

Closed-Form Expressions for the Calculation of the Ground-Return Impedance and Admittance of Underground Cables

Asymptotic Errors for Teacher-Student Convex Generalized Linear Models (Or: How to Prove Kabashima’s Replica Formula)

Gaia Focused Product Release: Asteroid orbital solution: Properties and assessment

VP-STO: Via-point-based Stochastic Trajectory Optimization for Reactive Robot Behavior

Last iterate convergence of SGD for Least-Squares in the Interpolation regime

Near-Minimax Optimal Estimation With Shallow ReLU Neural Networks

Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems

Analysis of Non-Canonical Body-Conformal Arrays with Polarization Decomposition

Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts

Rapid motion estimation and correction using self-encoded FID navigators in 3D radial MRI

TDOA Source-Localization Technique Robust to Time-Synchronization Attacks

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

Superluminal Motion-Assisted Four-Dimensional Light-in-Flight Imaging

ReLU Neural Network Galerkin BEM

Comparison of non-parametric T2 relaxometry methods for myelin water quantification

Statistical limits of high-dimensional inference problems

Sparsely Observed Functional Time Series: Theory and Applications