Related papers: A Deep Learning Approach to Quasar Continuum Predi…

This work proposes QNet, a novel sequence encoder model that entirely inferences on the quantum computer using a minimum number of qubits. Let $n$ and $d$ represent the length of the sequence and the embedding size, respectively. The…

Deep neural networks have been shown to achieve exceptional performance for computer vision tasks like image recognition, segmentation, and reconstruction or denoising. Here, we evaluate the ultimate performance limits of deep convolutional…

We present J-H-K' photometry for a sample of 45 high redshift quasars found by the Sloan Digital Sky Survey. The sample was originally selected on the basis of optical colors and spans a redshift range from 3.6 to 5.03. Our photometry…

We seek to remove foreground contaminants from 21cm intensity mapping observations. We demonstrate that a deep convolutional neural network (CNN) with a UNet architecture and three-dimensional convolutions, trained on simulated…

In this work, we build a generic architecture of Convolutional Neural Networks to discover empirical properties of neural networks. Our first contribution is to introduce a state-of-the-art framework that depends upon few hyper parameters…

This study introduces simple yet effective continuous- and discrete-variable quantum neural network (QNN) models as a transfer-learning approach for forecasting tasks. The CV-QNN features a single quantum layer with two qubits to establish…

We investigate the variety in quasar UV spectra (1020-1600A) with emphasis on the weak emission lines in the Ly alpha forest region using principal component analysis (PCA). This paper is a continuation of Suzuki et al. (2005, Paper I), but…

Deep convolutional neural networks (DCNNs) are an influential tool for solving various problems in the machine learning and computer vision fields. In this paper, we introduce a new deep learning model called an Inception- Recurrent…

Proceeding of the "HACK100" Conference, 6-10 June 2022, Trieste, Italy - In recent years, quasars have been shown to be reliable standardizable candles, thanks to the non-linear relation between their X-rays and ultraviolet luminosity.…

Deep learning (DL) has recently been proposed as a novel approach for 21cm foreground removal. Before applying DL to real observations, it is essential to assess its consistency with established methods, its performance across various…

We report the first results of an observational program designed to determine the luminosity density of high redshift quasars (z > 5 quasars) using deep multi-colour CCD data. We report the discovery and spectra of 3 i < 21.5 high redshift…

Near-IR photometry for 20 radio-loud z>3 quasars, 16 of which are radio- selected, are presented. These data sample the rest-frame optical/UV continuum, which is commonly interpreted as emission from an accretion disk. In a previous study,…

In this paper, we propose two hybrid quantum-inspired neural networks with adaptive residual and dense connections respectively for pattern recognition. We explain the frameworks of the symmetrical circuit models in the quantum-inspired…

We introduce deep learning technique to predict the beam propagation factor M^2 of the laser beams emitting from few-mode fiber for the first time, to the best of our knowledge. The deep convolutional neural network (CNN) is trained with…

We present the first discoveries from a survey of $z\gtrsim6$ quasars using imaging data from the DECam Legacy Survey (DECaLS) in the optical, the UKIRT Deep Infrared Sky Survey (UKIDSS) and a preliminary version of the UKIRT Hemisphere…

Quantum Recurrent Neural Networks (QRNNs) are robust candidates for modelling and predicting future values in multivariate time series. However, the effective implementation of some QRNN models is limited by the need for mid-circuit…

Recently, quantum convolutional neural networks (QCNNs) are proposed, harnessing the power of quantum computing for faster training compared to the classical counterparts. However, this framework for deep learning also relies on multiple…

Deep neural networks have achieved remarkable success in computer vision tasks. Existing neural networks mainly operate in the spatial domain with fixed input sizes. For practical applications, images are usually large and have to be…

Intrusion Detection Systems (IDSs) must maintain high detection sensitivity while operating under strict false-positive constraints, a challenge intensified by class imbalance and heterogeneous IoT traffic. This work investigates whether…

The observed lensed fraction of high-redshift quasars $(\sim0.2\%)$ is significantly lower than previous theoretical predictions $(\gtrsim4\%)$. We revisit the lensed fraction of high-redshift quasars predicted by theoretical models, where…