Related papers: Periodicity detection in AGN with the boosted tree…
Automatic objective non-invasive detection of pathological voice based on computerized analysis of acoustic signals can play an important role in early diagnosis, progression tracking and even effective treatment of pathological voices. In…
We present a morphological classification of 14,245 radio active galactic nuclei (AGNs) into six types, i.e., typical Fanaroff--Riley Class I / II (FRI/II), FRI/II-like bent-tailed, X-shaped radio galaxy, and ringlike radio galaxy, by…
Verifiable learning advocates for training machine learning models amenable to efficient security verification. Prior research demonstrated that specific classes of decision tree ensembles -- called large-spread ensembles -- allow for…
The study of X-ray time-lag spectra in active galactic nuclei (AGN) is currently an active research area, since it has the potential to illuminate the physics and geometry of the innermost region (i.e. close to the putative super-massive…
The F -GAMMA program is a coordinated effort of several observing facilities to understand the AGN/blazar phenomenon via a multi-frequency monitoring approach, especially in the era of Fermi-GST. Some 60 prominent sources are monitored…
The COVID 19 pandemic and ongoing political and regional conflicts have a highly detrimental impact on the global supply chain, causing significant delays in logistics operations and international shipments. One of the most pressing…
Based on the long-term light curves collected from the Catalina Sky Survey (CSS) (from 2005 to 2013) and the All-Sky Automated Survey for Supernovae (ASAS-SN) (from 2014 to 2018), optical quasi-periodic oscillations (QPOs) about 300 days…
FRB180301 is one of the most actively repeating fast radio bursts (FRBs) which has shown polarization angle changes in its radio burst emission, an indication for their likely origin in the magnetosphere of a highly-magnetized neutron star.…
Detection of periodic patterns of interest within noisy time series data plays a critical role in various tasks, spanning from health monitoring to behavior analysis. Existing learning techniques often rely on labels or clean versions of…
Logs are valuable information for oil and gas fields as they help to determine the lithology of the formations surrounding the borehole and the location and reserves of subsurface oil and gas reservoirs. However, important logs are often…
The research paper presents a novel approach to optimizing the tensile stress of Triply Periodic Minimal Surface (TPMS) structures through machine learning and Simulated Annealing (SA). The study evaluates the performance of Random Forest,…
We investigate the potential of large X-ray-selected AGN samples for detecting baryonic acoustic oscillations (BAO). Though AGN selection in X-ray band is very clean and efficient, it does not provide redshift information, and thus needs to…
Among the various types of cyberattacks, identifying zero-day attacks is problematic because they are unknown to security systems as their pattern and characteristics do not match known blacklisted attacks. There are many Machine Learning…
We tested the effectiveness of radio selection to discover heavily obscured AGNs, particularly at high-z, and we measured their abundance for the first time from a radio perspective. We consider the radio sources detected in the J1030…
In the current landscape of noisy intermediate-scale quantum (NISQ) computing, the inherent noise presents significant challenges to achieving high-fidelity long-range entanglement. Furthermore, this challenge is amplified by the limited…
We present some first results on the variability, polarization and general properties of radio sources selected in a blind survey at 20 GHz, the highest frequency at which a sensitive radio survey has been carried out over a large area of…
For many years, it has been claimed that the Galactic ridge X-ray emission at the Galactic Center (GC) is truly diffuse in nature. However, with the advancement of modern X-ray satellites, it has been found that most of the diffuse emission…
The Variable Bandpass Periodic Block Bootstrap(VBPBB) is an innovative method for time series with periodically correlated(PC) components. This method applies bandpass filters to extract specific PC components from datasets, effectively…
We present a detailed and systematic investigation of correlated spectral and timing properties of the Z source GX 349+2, using a huge amount (about 40 ks) of good quality data from September 29, 1998 to October 13, 1998, obtained with the…
A search is made for periodic modulation in the X-ray flux from the low mass X-ray binary GX13+1 using Rossi X-ray Timing Explorer All Sky Monitor data collected over a period of almost seven years. From a filtered data set, which excludes…