相关论文: Advancing Gamma-Ray Burst Identification through T…
Since their discoveries in 1967, Gamma-Ray Bursts (GRBs) continue to be one of the most researched objects in astrophysics. Multi-messenger observations are key to gaining a deeper understanding of these events. In order to facilitate such…
Over the past decade, deep learning research has been accelerated by increasingly powerful hardware, which facilitated rapid growth in the model complexity and the amount of data ingested. This is becoming unsustainable and therefore…
Fermi Gamma-ray Space Telescope has detected a diverse range of gamma-ray transients since its launch in 2008. Over the years, Fermi has accumulated an extensive public archive of transient events. Traditional classification methods for…
Skin Cancer is one of the most deathful of all the cancers. It is bound to spread to different parts of the body on the off chance that it is not analyzed and treated at the beginning time. It is mostly because of the abnormal growth of…
Gamma-ray bursts provide what is probably one of the messiest of all astrophysical data sets. Burst class properties are indistinct, as overlapping characteristics of individual bursts are convolved with effects of instrumental and sampling…
Gamma-ray Bursts (GRBs) are relativistic cosmological beacons of transient high energy radiation whose afterglows span the electromagnetic spectrum. Theoretical expectations of correlated neutrino and/or gravitational wave (GW) emission…
Gamma-ray bursts (GRBs) are short and intense flashes at the cosmological distances, which are the most luminous explosions in the Universe. The high luminosities of GRBs make them detectable out to the edge of the visible universe. So,…
We explore large-scale cosmic structure using the spatial distribution of 542 gamma-ray bursts (GRBs) having accurately measured positions and spectroscopic redshifts. Prominent cosmological clusters are identified in both the northern and…
Seam carving is a representative content-aware image retargeting approach to adjust the size of an image while preserving its visually prominent content. To maintain visually important content, seam-carving algorithms first calculate the…
Global Navigation Satellite System (GNSS) signals are subject to different kinds of events causing significant errors in positioning. This work explores the application of Machine Learning (ML) methods of anomaly detection applied to GNSS…
Convolutional Neural Networks (CNNs) have gained a significant attraction in the recent years due to their increasing real-world applications. Their performance is highly dependent to the network structure and the selected optimization…
Graph signal recovery (GSR) is a fundamental problem in graph signal processing, where the goal is to reconstruct a complete signal defined over a graph from a subset of noisy or missing observations. A central challenge in GSR is that the…
Gamma-ray bursts (GRBs) are traditionally classified into long (lGRBs) and short (sGRBs) durations based on their $T_{90}$, with lGRBs widely used as tracers of the cosmic star formation rate (SFR) due to their observed association with…
An important part of breast cancer staging is the assessment of the sentinel axillary node for early signs of tumor spreading. However, this assessment by pathologists is not always easy and retrospective surveys often requalify the status…
Apparently, Gamma-Ray Bursts (GRBs) are all but standard candles. Their emission is collimated into a cone and the received flux depends on the cone aperture angle. Fortunately we can derive the aperture angle through an achromatic…
In this work, we study the transfer learning problem under high-dimensional generalized linear models (GLMs), which aim to improve the fit on target data by borrowing information from useful source data. Given which sources to transfer, we…
For successful applications of machine learning in materials informatics, it is necessary to overcome the inaccuracy of predictions ascribed to insufficient amount of data. In this study, we propose a transfer learning using a crystal graph…
There is no consensus on the emission mechanism of $\gamma$-ray bursts (GRBs). A synchrotron model can produce $\gamma$-ray spectra with the empirical Band function form, from a piece-wise two-power-law electron energy distribution (2EPLS).…
We review recent results on the high-redshift universe and the cosmic evolution obtained using Gamma Ray Bursts (GRBs) as tracers of high-redshift galaxies. Most of the results come from photometric and spectroscopic observations of GRB…
The empirical classification of gamma-ray bursts (GRBs) into long and short GRBs based on their durations is already firmly established. This empirical classification is generally linked to the physical classification of GRBs originating…