Frequency-Domain Diffusion Adaptation Over Networks

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In the past years, many signal processing operations have been successfully adapted to the graph setting. One elegant and effective approach is to exploit the eigendecomposition of a graph shift operator (GSO), such as the adjacency or Laplacian operator, ...

We study low-rank matrix estimation for a generic inhomogeneous output channel through which the matrix is observed. This generalizes the commonly considered spiked matrix model with homogeneous noise to include for instance the dense degree-corrected stoc ...

Finding proper collective variables for complex systems and processes is one of the most challenging tasks in simulations, which limits the interpretation of experimental and simulated data and the application of enhanced sampling techniques. Here, we prop ...

Machine translation (MT) systems and language models (LMs) have demonstrated impressive performance in recent years, driven by advances in architecture, algorithms, and the scaling of parameters and training data. Despite the ability of these models to int ...

Research in speech processing has largely focused on source-system modeling, where vocal fold vibrations serve as the source and the vocal cavity's articulations as the system. Nonetheless, speech production includes a complex combination of physiological ...

Having a single neural network to handle a wide and varied range of tasks and modalities has been a long-standing goal. Such a model brings notable advantages, such as test-time computational efficiency, modality fusion, and reduced model size. Our goal in ...

This paper introduces a new method for solving the distributed AC power flow (PF) problem by further exploiting the problem formulation. We propose a new variant of the ALADIN algorithm devised specifically for this type of problem. This new variant is cha ...

We consider a least-squares/relaxation finite element method for the numerical solution of the prescribed Jacobian equation. We look for its solution via a least-squares approach. We introduce a relaxation algorithm that decouples this least-squares proble ...

In this thesis we study stability from several viewpoints. After covering the practical importance, the rich history and the ever-growing list of manifestations of stability, we study the following. (i) (Statistical identification of stable dynamical syste ...

This paper considers the multi-agent linear least-squares problem in a server-agent network architecture. The system comprises multiple agents, each with a set of local data points. The agents are connected to a server, and there is no inter-agent communic ...

In supervised learning, the regularization path is sometimes used as a convenient theoretical proxy for the optimization path of gradient descent initialized from zero. In this paper, we study a modification of the regularization path for infinite-width 2- ...

Quantitative magnetic resonance imaging (qMRI) can increase the specificity and sensitivity of conventional weighted MRI to underlying pathology by comparing meaningful physical or chemical parameters, measured in physical units, with normative values acqu ...

Orthogonal group synchronization is the problem of estimating n elements Z(1),& mldr;,Z(n) from the rxr orthogonal group given some relative measurements R-ij approximate to Z(i)Z(j)(-1). The least-squares formulation is nonconvex. To avoid its local minim ...

A new Additive-Manufacturing (AM) or 3D printing concept is proposed to improve the printing resolution for metal additive manufacturing in the frame of the SFA-AM project, Powder Focusing for Beam-Induced Laser 3D Printing. The project aims to transport s ...

We present an extreme ultraviolet (EUV) transient grating (TG) experiment of the spinel Co3O4 compound using tuneable incident energies across the Co M-2,M-3-edge and a 395 nm probe pulse, detecting both the first and the second diffraction orders (SDOs). ...

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

The performance of SwissSPAD2 (SS2), a large scale, widefield time-gated CMOS SPAD imager developed for fluorescence lifetime imaging, has recently been described in the context of visible range and fluorescence lifetime imaging microscopy (FLIM) of dyes w ...

Variance-reduced gradient estimators for policy gradient methods have been one of the main focus of research in the reinforcement learning in recent years as they allow acceleration of the estimation process. We propose a variance-reduced policy-gradient m ...

We consider increasingly complex models of matrix denoising and dictionary learning in the Bayes-optimal setting, in the challenging regime where the matrices to infer have a rank growing linearly with the system size. This is in contrast with most existin ...

The theory underlying robust distributed learning algorithms, designed to resist adversarial machines, matches empirical observations when data is homogeneous. Under data heterogeneity however, which is the norm in practical scenarios, established lower bo ...

Frequency-Domain Diffusion Adaptation Over Networks

Chattez avec Graph Search

Hilbert Transform on Graphs: Let There Be Phase

Low-rank matrix estimation with inhomogeneous noise

Inherent Structural Descriptors Via Machine Learning

From Preprocessing to Evaluation: The Role of External Knowledge in Advancing Language Models and Machine Translation

Towards Integrated Processing of Physiological Signals and Speech

Scaling the Modalities in Multimodal Foundation Models

Rapid Scalable Distributed Power Flow with Open-Source Implementation

A Least-Squares Method for the Solution of the Non-smooth Prescribed Jacobian Equation

Stability: a search for structure

Iterative pre-conditioning for expediting the distributed gradient-descent method: The case of linear least-squares problem

On the Effect of Initialization: The Scaling Path of 2-Layer Neural Networks

Variability and reproducibility of multi-echo T2 relaxometry: Insights from multi-site, multi-session and multi-subject MRI acquisitions

BENIGN LANDSCAPES OF LOW-DIMENSIONAL RELAXATIONS FOR ORTHOGONAL SYNCHRONIZATION ON GENERAL GRAPHS

Inflight Microwave Drying Process of Micro-droplets for High Resolution 3D printing

Separation of kinetic rate orders in extreme ultraviolet transient grating spectroscopy

Spectral Estimators for High-Dimensional Matrix Inference

NIR Fluorescence lifetime macroscopic imaging with a time-gated SPAD camera

Momentum-Based Policy Gradient with Second-Order Information

Statistical limits of dictionary learning: Random matrix theory and the spectral replica method

Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity