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Many applications like audio and image processing show that sparse representations are a powerful and efficient signal modeling technique. Finding an optimal dictionary that generates at the same time the sparsest representations of data…

Variability studies are a powerful tool for studying the structures of unresolved sources. One such type of variability study, called reverberation mapping (RM), established that the dominant source of infrared radiation from an active…

To provide constraints on their inversion, ocean sound speed profiles (SSPs) often are modeled using empirical orthogonal functions (EOFs). However, this regularization, which uses the leading order EOFs with a minimum-energy constraint on…

This paper explores machine learning (ML) models for classifying lung cancer levels to improve diagnostic accuracy and prognosis. Through parameter tuning and rigorous evaluation, we assess various ML algorithms. Techniques like minimum…

We use machine learning to perform super-resolution analysis of grossly under-resolved turbulent flow field data to reconstruct the high-resolution flow field. Two machine-learning models are developed; namely the convolutional neural…

Artificial Intelligence (Deep Learning(DL)/ Machine Learning(ML)) techniques are widely being used to address and overcome all kinds of ill-posed problems in medical imaging which was or in fact is seemingly impossible. Reducing gradient…

Anomalous sound detection (ASD) in the wild requires robustness to distribution shifts such as unseen low-SNR input mixtures of machine and noise types. State-of-the-art systems extract embeddings from an adapted audio encoder and detect…

Light field imaging has recently known a regain of interest due to the availability of practical light field capturing systems that offer a wide range of applications in the field of computer vision. However, capturing high-resolution light…

Context. Variability is a ubiquitous feature of active galactic nuclei (AGNs), and the characterisation of the variability is crucial to constrain its physical mechanism and proper applications in AGN studies. The advent of all-sky and…

Modern time-domain surveys continuously monitor large swaths of the sky to look for astronomical variability. Astrophysical discovery in such data sets is complicated by the fact that detections of real transient and variable sources are…

We develop a random forest regressor (RFR) machine learning model to trace the coronal evolution in two highly variable active galactic nuclei (AGNs) IRAS 13224-3809 and 1H 0707-495 observed with XMM-Newton, by probing the X-ray…

We present a broadband power spectral density function (PSD) measured from extensive RXTE monitoring data of the low-luminosity AGN NGC 4258, which has an accurate, maser-determined black hole mass of 3.9+/-0.1 * 10^7 solar masses. We…

Deep learning (DL) based unrolled reconstructions have shown state-of-the-art performance for under-sampled magnetic resonance imaging (MRI). Similar to compressed sensing, DL can leverage high-dimensional data (e.g. 3D, 2D+time, 3D+time)…

Super-resolution is an innovative technique that upscales the resolution of an image or a video and thus enables us to reconstruct high-fidelity images from low-resolution data. This study performs super-resolution analysis on turbulent…

We study the X-ray reverberation in active galactic nuclei (AGN) using the variational autoencoder (VAE), which is a machine-learning algorithm widely used for signal processing and feature reconstruction. While the X-ray reverberation…

AGNs are very powerful galaxies characterized by extremely bright emissions coming out from their central massive black holes. Knowing the redshifts of AGNs provides us with an opportunity to determine their distance to investigate…

We show that X-ray reverberation mapping can be used to measure the distance to type 1 active galactic nuclei (AGNs). This is because X-ray photons originally emitted from the `corona' close to the black hole irradiate the accretion disc…

Optical spectra contain a wealth of information about the physical properties and formation histories of galaxies. Often though, spectra are too noisy for this information to be accurately retrieved. In this study, we explore how machine…

We present a detailed study of the long-timescale X-ray variability of the Narrow Line Seyfert 1 (NLS1) Galaxy MCG-6-30-15, based on 8 years of frequent observations with the Rossi X-ray Timing Explorer. Combined with short timescale…

One of the hallmarks of active galactic nuclei are that they are highly variable with time. In watching the spectra vary it has been observed that the emission-lines often appear to "reverberate" -- that is they vary in response to…