KAGRA is an underground interferometric gravitational wave detector which is currently being commissioned.

KAGRA is a newly built gravitational wave observatory, a laser interferometer with a 3 km arm length, located at Kamioka, Gifu, Japan. In this series of articles we present an overview of the baseline KAGRA, for which we finished installing the designed configuration in 2019.

We provide analysis to determine the effects of gravitational waves on electromagnetic waves, using perturbation theory in general relativity. Our analysis is performed in a completely covariant manner without invoking any coordinates.For a given observer, using the geometrical-optics approach, we work out the perturbations of the phase, amplitude, frequency and polarization properties？axes of ellipse and ellipticity of light, due to gravitational waves.

Advanced LIGO and Advanced Virgo are monitoring the sky and collecting gravitational-wave strain data with sufficient sensitivity to detect signals routinely. In this paper we describe the data recorded by these instruments during their first and second observing runs.

This paper proposes a simple but effective end-to-end deep neural image decomposition network, which is called ’’scalable image decomposition’’, by decomposing and upscaling structure and texture images from the degraded input image at the same time.

The lengthy time needed for manual landmarking has delayed the widespread adoption of threedimensional (3D) cephalometry. We here propose an automatic 3D cephalometric annotation system based on multi-stage deep reinforcement learning (DRL) and volume-rendered imaging. This system considers geometrical characteristics of landmarks and simulates the sequential decision process underlying human professional landmarking patterns.

In this paper, we propose a self-representative feature extraction deep neural network for unsupervised subspace clustering to improve representativeness and clustering ability. The extensive relevant results on various data demonstrate that deep subspace clustering employing self-representative features from high-dimensional data can effectively reduce the dimension of the self-representative layer while improv- ing performance.

In this study, we aimed to assess the country-specific importation risk of COVID-19 and investigate its impact on the local transmission of COVID-19.