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Nuclear starburst discs (NSDs) are very compact star-forming regions in the centers of galaxies that have been studied as a possible origin for the absorbing gas around a central active galactic nucleus. NSDs may be most relevant at $z\sim…

Semi-supervised learning (SSL) has been proven beneficial for mitigating the issue of limited labeled data especially on the task of volumetric medical image segmentation. Unlike previous SSL methods which focus on exploring highly…

Due to their sequential nature, traditional DNA synthesis methods are expensive in terms of time and resources. They also fabricate multiple copies of the same strand, introducing redundancy. This redundancy can be leveraged to enhance the…

A new n-dimensional vector space of the DNA sequences on the Galois field of the 64 codons (GF(64)) is proposed. In this vector space gene mutations can be considered linear transformations or translations of the wild type gene. In…

The process of DNA-based data storage (DNA storage for short) can be mathematically modelled as a communication channel, termed DNA storage channel, whose inputs and outputs are sets of unordered sequences. To design error correcting codes…

DNA is a leading candidate as the next archival storage media due to its density, durability and sustainability. To read (and write) data DNA storage exploits technology that has been developed over decades to sequence naturally occurring…

Convolutional neural networks (CNNs) have achieved superior performance but still lack clarity about the nature and properties of feature extraction. In this paper, by analyzing the sensitivity of neural networks to frequencies and scales,…

From a sample of spectra of 439 white dwarfs (WDs) from the ESO-VLT Supernova-Ia Progenitor surveY (SPY), we measure the maximal changes in radial-velocity (DRVmax) between epochs (generally two epochs, separated by up to 470d), and model…

Syndrome-based neural decoding (SBND) has emerged as a promising deep learning approach for soft-decision decoding of high-rate, short-length codes. However, this approach still has substantial room for improvement. In this paper, we show…

An important first step in computational SAR modeling is to transform the compounds into a representation that can be processed by predictive modeling techniques. This is typically a feature vector where each feature indicates the presence…

Large populations may contain numerous simultaneously segregating polymorphisms subject to natural selection. Since selection acts on individuals whose fitness depends on many loci, different loci affect each other's dynamics. This leads to…

Stripped-envelope supernovae (SESNe) originate from massive stars that lose their envelopes through binary interactions or stellar winds. The connection between SESN subtypes and their progenitors remains poorly understood, as does the…

Convolutional Neural Networks (CNNs) have revolutionized the understanding of visual content. This is mainly due to their ability to break down an image into smaller pieces, extract multi-scale localized features and compose them to…

Short tandem repeats (STRs) and single nucleotide polymorphisms (SNPs) are two kinds of commonly used markers in Y chromosome studies of forensic and population genetics. There has been increasing interest in the cost saving strategy by…

Context.This is the second paper of a series aiming to determine the birth rates of supernovae in the local Universe. Aims. In this paper, we aim to estimate the SN rates in the local universe and fit the delay-time distribution of SNe Ia…

In this work, we study the binary neural networks (BNNs) of which both the weights and activations are binary (i.e., 1-bit representation). Feature representation is critical for deep neural networks, while in BNNs, the features only differ…

We present the summary of our last results on the spatial distribution and relative frequencies of Supernovae (SNe) in a large number of host galaxies from the Sloan Digital Sky Survey (SDSS). We use the locations of SNe in order to study…

Technological advances in radio telescopes and X-ray instruments over the last 20 years have greatly increased the number of known supernova remnants (SNRs) and led to a better determination of their properties. In particular, more SNRs now…

As a fundamental part of computational healthcare, Computer Tomography (CT) and Magnetic Resonance Imaging (MRI) provide volumetric data, making the development of algorithms for 3D image analysis a necessity. Despite being computationally…

A basic operation in Convolutional Neural Networks (CNNs) is spatial resizing of feature maps. This is done either by strided convolution (donwscaling) or transposed convolution (upscaling). Such operations are limited to a fixed filter…