Related papers: Repeating versus Nonrepeating Fast Radio Bursts: A…

Motivated by the fact that characteristics of different sound classes are highly diverse in different temporal scales and hierarchical levels, a novel deep convolutional neural network (CNN) architecture is proposed for the environmental…

Fast radio bursts (FRBs) are extremely energetic, millisecond-duration radio flashes that reach Earth from extragalactic distances. Broadly speaking, FRBs can be classified as repeating or (apparently) non-repeating. It is still unclear,…

The gravitational field of compact objects, such as primordial black holes, can create multiple images of background sources. For transients such as fast radio bursts (FRBs), these multiple images can be resolved in the time domain. Under…

Fast Radio Bursts (FRBs) are millisecond-duration transient events that are typically observed at radio wavelengths and cosmological distances but their origin remains unclear. Furthermore, most FRB origin models are related to the…

Context: Fast radio bursts (FRBs) are bright millisecond radio events of unknown extragalactic origin. Magnetars are among the main contenders. Some sources, the repeaters, produce multiple events but so far generally without the…

Low-Rank Factorization (LRF) is a widely adopted technique for compressing deep neural networks (DNNs). However, it faces several challenges, including optimal rank selection, a vast design space, long fine-tuning times, and limited…

Variations in the Faraday rotation measure (RM) of repeating fast radio bursts (FRBs) provide critical diagnostics of the dynamically evolving magneto-ionic environments surrounding their progenitors. Sudden, transient ``RM flares'' can…

Fast radio bursts (FRBs) are enigmatic millisecond-duration radio transients whose physical origins remain debated. To shed light on this, we analyze the CHIME/FRB Catalog 2. By using the probability distribution of dispersion measured (DM)…

Should we input known genome sequence features or input sequence itself in deep learning framework? As deep learning more popular in various applications, researchers often come to question whether to generate features or use raw sequences…

Fast radio bursts (FRBs) are radio signals that last milliseconds. They originate from cosmological distances and have relatively high dispersion measures (DMs), making them being excellent distance indicators. However, the origins of the…

We utilize the Quark-Novae (QN) model for Fast Radio Bursts (FRBs; Ouyed et al. 2021; arXiv:2005.09793) to evaluate its performance in reproducing the distribution and statistical properties of key observations. These include frequency,…

FRB 121102 is the only known repeating fast radio burst source. Here we analyze a wide-frequency-range (1-8 GHz) sample of high-signal-to-noise, coherently dedispersed bursts detected using the Arecibo and Green Bank telescopes. These…

Motivation: Deep learning architectures have recently demonstrated their power in predicting DNA- and RNA-binding specificities. Existing methods fall into three classes: Some are based on Convolutional Neural Networks (CNNs), others use…

Extragalactic fast radio bursts (FRBs) are a new class of astrophysical transients with unknown origins that have become a main focus of radio observatories worldwide. FRBs are highly energetic ($\sim 10^{36}$-$10^{42}$ ergs) flashes that…

In this paper, we present a sample of 21 repeating fast radio bursts (FRBs) detected by different radio instruments before September 2021. Using the Anderson--Darling test, we compared the distributions of extra-Galactic dispersion measure…

Transfer learning is a machine learning technique designed to improve generalization performance by using pre-trained parameters obtained from other learning tasks. For image recognition tasks, many previous studies have reported that, when…

Radio-Frequency (RF) imaging concerns the digital recreation of the surfaces of scene objects based on the scattered field at distributed receivers. To solve this difficult inverse scattering problems, data-driven methods are often employed…

Diffusion models have emerged as state-of-the-art generative methods for image synthesis, yet their potential as general-purpose feature encoders remains underexplored. Trained for denoising and generation without labels, they can be…

Phase-Based Ranging (PBR) offers several advantages for estimating distances between wirelessly connected devices, including high accuracy over large distances and the removal of the need for antenna arrays at each transceiver. This study…

In this work, we explore the intersection of sparse coding theory and deep learning to enhance our understanding of feature extraction capabilities in advanced neural network architectures. We begin by introducing a novel class of Deep…