Related papers: CLAWDIA: A dictionary learning framework for gravi…

(abridged for arXiv) With the first direct detection of gravitational waves, the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) has initiated a new field of astronomy by providing an alternate means of sensing the…

The basic constituent of interferometric gravitational wave detectors -- the test mass to test mass interferometric link -- behaves as a differential dynamometer measuring effective differential forces, comprising an integrated measure of…

In this paper, benefiting from the strong ability of deep neural network in estimating non-linear functions, we propose a discriminative embedding function to be used as a feature extractor for clustering tasks. The trained embedding…

Many traditional algorithms applied in gravitational-wave astronomy rely on the assumption of Gaussian noise, a condition not always met. To meet this need, this study extends a robust statistical framework, advancing previous work on…

Subspace clustering techniques have shown promise in hyperspectral image segmentation. The fundamental assumption in subspace clustering is that the samples belonging to different clusters/segments lie in separable subspaces. What if this…

This paper addresses the problem of learning dictionaries for multimodal datasets, i.e. datasets collected from multiple data sources. We present an algorithm called multimodal sparse Bayesian dictionary learning (MSBDL). MSBDL leverages…

Glitches are transitory noise artifacts that degrade the detection sensitivity and accuracy of interferometric observatories such as LIGO and Virgo in gravitational wave astronomy. Reliable glitch subtraction techniques are essential for…

The observation of gravitational waves from compact binary coalescences by LIGO and Virgo has begun a new era in astronomy. A critical challenge in making detections is determining whether loud transient features in the data are caused by…

The sensitivity of wide-parameter-space searches for continuous gravitational waves is limited by computational cost. Recently it was shown that Deep Neural Networks (DNNs) can perform all-sky searches directly on (single-detector) strain…

Gravitational waves are radiative solutions of space-time dynamics predicted by Einstein's theory of General Relativity. A world-wide array of large-scale and highly sensitive interferometric detectors constantly scrutinizes the geometry of…

These lecture notes provide a brief introduction to methods used to search for a stochastic background of gravitational radiation -- a superposition of gravitational-wave signals that are either too weak or too numerous to individually…

The {\Lambda}CDM model has long served as a robust and predictive framework for cosmology, successfully explaining a wide range of observations, including the accelerated expansion of the Universe. However, discrepancies in cosmological…

Reconstructing ocean dynamics from observational data is fundamentally limited by the sparse, irregular, and Lagrangian nature of spatial sampling, particularly in subsurface and remote regions. This sparsity poses significant challenges…

A popular approach within the signal processing and machine learning communities consists in modelling signals as sparse linear combinations of atoms selected from a learned dictionary. While this paradigm has led to numerous empirical…

The quest to observe gravitational waves challenges our ability to discriminate signals from detector noise. This issue is especially relevant for transient gravitational waves searches with a robust eyes wide open approach, the so called…

Based on its great successes in inference and denosing tasks, Dictionary Learning (DL) and its related sparse optimization formulations have garnered a lot of research interest. While most solutions have focused on single layer…

This work introduces a novel deep learning-based approach for gravitational wave anomaly detection, aiming to overcome the limitations of traditional matched filtering techniques in identifying unknown waveform gravitational wave signals.…

Gravitational wave detection requires sophisticated signal processing to identify weak astrophysical signals buried in instrumental noise. Traditional matched filtering approaches face computational challenges with diverse signal…

Sparse annotation in remote sensing object detection poses significant challenges due to dense object distributions and category imbalances. Although existing Dense Pseudo-Label methods have demonstrated substantial potential in…

Sparse representations using learned dictionaries are being increasingly used with success in several data processing and machine learning applications. The availability of abundant training data necessitates the development of efficient,…