Related papers: Machine Learning applications for Cataclysmic Vari…
Task-incremental continual learning refers to continually training a model in a sequence of tasks while overcoming the problem of catastrophic forgetting (CF). The issue arrives for the reason that the learned representations are forgotten…
Conventional deep learning models have limited capacity in learning multiple tasks sequentially. The issue of forgetting the previously learned tasks in continual learning is known as catastrophic forgetting or interference. When the input…
We propose a methodology for generating time-dependent turbulent inflow data with the aid of machine learning (ML), which has a possibility to replace conventional driver simulations or synthetic turbulent inflow generators. As for the ML…
To ensure that Machine Learning (ML) models can perform a robust detection and classification of cyberattacks, it is essential to train them with high-quality datasets with relevant features. However, it can be difficult to accurately…
(abridged) We present an account of known cataclysmic variables (CVs) that were detected as X-ray sources in eROSITA X-ray surveys and have Gaia DR3 counterparts. We address standard CVs with main sequence donors and white dwarfs accreting…
Deep learning methods are powerful tools but often suffer from expensive computation and limited flexibility. An alternative is to combine light-weight models with deep representations. As successful cases exist in several visual problems,…
Continual Learning (CL, sometimes also termed incremental learning) is a flavor of machine learning where the usual assumption of stationary data distribution is relaxed or omitted. When naively applying, e.g., DNNs in CL problems, changes…
Machine Learning models in real-world applications must continuously learn new tasks to adapt to shifts in the data-generating distribution. Yet, for Continual Learning (CL), models often struggle to balance learning new tasks (plasticity)…
As a crucial component in intelligent transportation systems, traffic flow prediction has recently attracted widespread research interest in the field of artificial intelligence (AI) with the increasing availability of massive traffic…
The rapid increase in serendipitous X-ray source detections requires the development of novel approaches to efficiently explore the nature of X-ray sources. If even a fraction of these sources could be reliably classified, it would enable…
Quantum machine learning (QML) leverages the potential from machine learning to explore the subtle patterns in huge datasets of complex nature with quantum advantages. This exponentially reduces the time and resources necessary for…
During a visual search through the Kepler main-field lightcurves, we have discovered a cataclysmic variable (CV) that experienced only a single 4-day long outburst over four years, rising to three times the quiescent flux. During the four…
Large differences between the properties of the known sample of cataclysmic variable stars (CVs) and the predictions of the theory of binary star evolution have long been recognised. However, because all existing CV samples suffer from…
Automating real-time anomaly detection is essential for identifying rare transients, with modern survey telescopes generating tens of thousands of alerts per night, and future telescopes, such as the Vera C. Rubin Observatory, projected to…
This paper investigates the processing of Frequency Modulated-Continuos Wave (FM-CW) radar signals for vehicle classification. In the last years deep learning has gained interest in several scientific fields and signal processing is not one…
We report the first results from a new search for cataclysmic variables (CVs) using a combined X-ray (ROSAT) / infrared (2MASS) target selection that discriminates against background AGN. Identification spectra were obtained at the Isaac…
Continuous normalizing flows (CNFs) learn the probability path between a reference distribution and a target distribution by modeling the vector field generating said path using neural networks. Recently, Lipman et al. (2022) introduced a…
Cataclysmic variable stars (CVs) are close binary systems consisting of a white dwarf (primary) that is accreting matter from a low-mass companion star (secondary). From time to time such systems undergo large-amplitude brightenings. The…
According to standard evolutionary theory for cataclysmic variables (CVs), angular momentum loss drives CVs to initially evolve from longer to shorter orbital periods until a minimum period is reached (\approx 80 min). At roughly this…
Context. The evolution of cataclysmic variables (CVs) - interacting binaries where a low-mass donor transfers matter to a white dwarf via an accretion disk - is critically controlled by magnetic braking (MB). Significant uncertainties…