Transfer learning for radio galaxy classification

In the context of radio galaxy classification, most state-of-the-art neural network algorithms have been focused on single survey data. The question of whether these trained algorithms have cross-survey identification ability or can be adapted to develop classification networks for future surveys is still unclear. One possible solution to address this issue is transfer learning, which re-uses elements of existing machine learning models for different applications. Here we present radio galaxy classification based on a 13-layer Deep Convolutional Neural Network (DCNN) using transfer learning methods between different radio surveys. We find that our machine learning models trained from a random initialization achieve accuracies comparable to those found elsewhere in the literature. When using transfer learning methods, we find that inheriting model weights pre-trained on FIRST images can boost model performance when re-training on lower resolution NVSS data, but that inheriting pre-trained model weights from NVSS and re-training on FIRST data impairs the performance of the classifier. We consider the implication of these results in the context of future radio surveys planned for next-generation radio telescopes such as ASKAP, MeerKAT, and SKA1-MID

First principles investigation of transition-metal doped group-IV semiconductors: Rx{_x}Y1−x_{1-x} (R=Cr, Mn, Fe; Y=Si, Ge)

A number of transition-metal (TM) doped group-IV semiconductors, R$_{x}$Y$_{1-x}$ (R=Cr, Mn and Fe; Y=Si, Ge), have been studied by the first principles calculations. The obtained results show that antiferromagnetic (AFM) order is energetically more favored than ferromagnetic (FM) order in Cr-doped Ge and Si with $x$=0.03125 and 0.0625. In 6.25% Fe-doped Ge, FM interaction dominates in all range of the R-R distances while for Fe-doped Ge at 3.125% and Fe-doped Si at both concentrations of 3.125% and 6.25%, only in a short R-R range can the FM states exist. In the Mn-doped case, the RKKY-like mechanism seems to be suitable for the Ge host matrix, while for the Mn-doped Si, the short-range AFM interaction competes with the long-range FM interaction. The different origin of the magnetic orders in these diluted magnetic semiconductors (DMSs) makes the microscopic mechanism of the ferromagnetism in the DMSs more complex and attractive.Comment: 14 pages, 2 figures, 6 table

Attention-gating for improved radio galaxy classification

In this work we introduce attention as a state of the art mechanism for classification of radio galaxies using convolutional neural networks. We present an attention-based model that performs on par with previous classifiers while using more than 50% fewer parameters than the next smallest classic CNN application in this field. We demonstrate quantitatively how the selection of normalisation and aggregation methods used in attention-gating can affect the output of individual models, and show that the resulting attention maps can be used to interpret the classification choices made by the model. We observe that the salient regions identified by the our model align well with the regions an expert human classifier would attend to make equivalent classifications. We show that while the selection of normalisation and aggregation may only minimally affect the performance of individual models, it can significantly affect the interpretability of the respective attention maps and by selecting a model which aligns well with how astronomers classify radio sources by eye, a user can employ the model in a more effective manner.Comment: 18 pages, 16 figures, Published in MNRA

Structured Variational Inference for Simulating Populations of Radio Galaxies

We present a model for generating postage stamp images of synthetic Fanaroff-Riley Class I and Class II radio galaxies suitable for use in simulations of future radio surveys such as those being developed for the Square Kilometre Array. This model uses a fully-connected neural network to implement structured variational inference through a variational auto-encoder and decoder architecture. In order to optimise the dimensionality of the latent space for the auto-encoder we introduce the radio morphology inception score (RAMIS), a quantitative method for assessing the quality of generated images, and discuss in detail how data pre-processing choices can affect the value of this measure. We examine the 2-dimensional latent space of the VAEs and discuss how this can be used to control the generation of synthetic populations, whilst also cautioning how it may lead to biases when used for data augmentation.Comment: 20 pages, 20 figures, accepted MNRA

Barrier-to-autointegration factor 1 protects against a basal cGAS-STING response

Although the pathogen recognition receptor pathways that activate cell-intrinsic antiviral responses are well delineated, less is known about how the host regulates this response to prevent sustained signaling and possible immune-mediated damage. Using a genome-wide CRISPR-Cas9 screening approach to identify host factors that modulate interferon-stimulated gene (ISG) expression, we identified the DNA binding protein Barrier-to-autointegration factor 1 (Banf1), a previously described inhibitor of retrovirus integration, as a modulator of basal cell-intrinsic immunity. Ablation of Banf1 by gene editing resulted in chromatin activation near host defense genes with associated increased expression of ISGs, includin

Binding of herpes simplex virus-1 US11 to specific RNA sequences

Herpes simplex virus-1 US11 is a RNA-binding protein with a novel RNA-binding domain. US11 has been reported to exhibit sequence- and conformation-specific RNA-binding, but the sequences and conformations important for binding are not known. US11 has also been described as a double-stranded RNA (dsRNA)-binding protein. To investigate the US11–RNA interaction, we performed in vitro selection of RNA aptamers that bind US11 from a RNA library consisting of >10(14) 80 base sequences which differ in a 30 base randomized region. US11 bound specifically to selected aptamers with an affinity of 70 nM. Analysis of 23 selected sequences revealed a strong consensus sequence. The US11 RNA-binding domain and ≤46 bases of selected RNA containing the consensus sequence were each sufficient for binding. US11 binding protected the consensus motif from hydroxyl radical cleavage. RNase digestions of a selected aptamer revealed regions of both single-stranded RNA and dsRNA. We observed that US11 bound two different dsRNAs in a sequence non-specific manner, but with lower affinity than it bound selected aptamers. The results define a relatively short specific sequence that binds US11 with high affinity and indicate that dsRNA alone does not confer high-affinity binding