Domain Generalization Strategy to Train Classifiers Robust to Spatial-Temporal Shift

Deep learning-based weather prediction models have advanced significantly in recent years. However, data-driven models based on deep learning are difficult to apply to real-world applications because they are vulnerable to spatial-temporal shifts. A weather prediction task is especially susceptible to spatial-temporal shifts when the model is overfitted to locality and seasonality. In this paper, we propose a training strategy to make the weather prediction model robust to spatial-temporal shifts. We first analyze the effect of hyperparameters and augmentations of the existing training strategy on the spatial-temporal shift robustness of the model. Next, we propose an optimal combination of hyperparameters and augmentation based on the analysis results and a test-time augmentation. We performed all experiments on the W4C22 Transfer dataset and achieved the 1st performance.Comment: Core Transfer Track 1st place solution in Weather4Cast competition at NeuIPS2

Simple Baseline for Weather Forecasting Using Spatiotemporal Context Aggregation Network

Traditional weather forecasting relies on domain expertise and computationally intensive numerical simulation systems. Recently, with the development of a data-driven approach, weather forecasting based on deep learning has been receiving attention. Deep learning-based weather forecasting has made stunning progress, from various backbone studies using CNN, RNN, and Transformer to training strategies using weather observations datasets with auxiliary inputs. All of this progress has contributed to the field of weather forecasting; however, many elements and complex structures of deep learning models prevent us from reaching physical interpretations. This paper proposes a SImple baseline with a spatiotemporal context Aggregation Network (SIANet) that achieved state-of-the-art in 4 parts of 5 benchmarks of W4C22. This simple but efficient structure uses only satellite images and CNNs in an end-to-end fashion without using a multi-model ensemble or fine-tuning. This simplicity of SIANet can be used as a solid baseline that can be easily applied in weather forecasting using deep learning.Comment: 1st place solution for stage1 and Core Transfer in the Weather4Cast competition on NeurIPS 2

Nuclear starburst activity induced by elongated bulges in spiral galaxies

We study the effects of bulge elongation on the star formation activity in the centers of spiral galaxies using the data from the Sloan Digital Sky Survey Data Release 7. We construct a volume-limited sample of face-on spiral galaxies with $M_r < -$19.5 mag at 0.02 $\leq z <$ 0.055 by excluding barred galaxies, where the aperture of the SDSS spectroscopic fibre covers the bulges of the galaxies. We adopt the ellipticity of bulges measured by Simard et al. (2011) who performed two-dimensional bulge+disc decompositions using the SDSS images of galaxies, and identify nuclear starbursts using the fibre specific star formation rates derived from the SDSS spectra. We find a statistically significant correlation between bulge elongation and nuclear starbursts in the sense that the fraction of nuclear starbursts increases with bulge elongation. This correlation is more prominent for fainter and redder galaxies, which exhibit higher ratios of elongated bulges. We find no significant environmental dependence of the correlation between bulge elongation and nuclear starbursts. These results suggest that non-axisymmetric bulges can efficiently feed the gas into the centre of galaxies to trigger nuclear starburst activity.Comment: 9 pages, 7 figures, accepted for publication in MNRA

Auto-Guiding System for CQUEAN (Camera for QUasars in EArly uNiverse)

To perform imaging observation of optically red objects such as high redshift quasars and brown dwarfs, the Center for the Exploration of the Origin of the Universe (CEOU) recently developed an optical CCD camera, Camera for QUasars in EArly uNiverse(CQUEAN), which is sensitive at 0.7-1.1 um. To enable observations with long exposures, we developed an auto-guiding system for CQUEAN. This system consists of an off-axis mirror, a baffle, a CCD camera, a motor and a differential decelerator. To increase the number of available guiding stars, we designed a rotating mechanism for the off-axis guiding camera. The guiding field can be scanned along the 10 acrmin ring offset from the optical axis of the telescope. Combined with the auto-guiding software of the McDonald Observatory, we confirmed that a stable image can be obtained with an exposure time as long as 1200 seconds.Comment: Accepted for publication in Journal of Korean Astronomical Society (JKAS

Prediction of Cancer Patient Outcomes Based on Artificial Intelligence

Knowledge-based outcome predictions are common before radiotherapy. Because there are various treatment techniques, numerous factors must be considered in predicting cancer patient outcomes. As expectations surrounding personalized radiotherapy using complex data have increased, studies on outcome predictions using artificial intelligence have also increased. Representative artificial intelligence techniques used to predict the outcomes of cancer patients in the field of radiation oncology include collecting and processing big data, text mining of clinical literature, and machine learning for implementing prediction models. Here, methods of data preparation and model construction to predict rates of survival and toxicity using artificial intelligence are described

Camera for QUasars in EArly uNiverse (CQUEAN)

We describe the overall characteristics and the performance of an optical CCD camera system, Camera for QUasars in EArly uNiverse (CQUEAN), which is being used at the 2.1 m Otto Struve Telescope of the McDonald Observatory since 2010 August. CQUEAN was developed for follow-up imaging observations of red sources such as high redshift quasar candidates (z >= 5), Gamma Ray Bursts, brown dwarfs, and young stellar objects. For efficient observations of the red objects, CQUEAN has a science camera with a deep depletion CCD chip which boasts a higher quantum efficiency at 0.7 - 1.1 um than conventional CCD chips. The camera was developed in a short time scale (~ one year), and has been working reliably. By employing an auto-guiding system and a focal reducer to enhance the field of view on the classical Cassegrain focus, we achieve a stable guiding in 20 minute exposures, an imaging quality with FWHM >= 0.6" over the whole field (4.8' * 4.8'), and a limiting magnitude of z = 23.4 AB mag at 5-sigma with one hour total integration time.Comment: Accepted for publication in PASP. 26 pages including 5 tables and 24 figure

Photometric Redshifts in the North Ecliptic Pole Wide Field based on a Deep Optical Survey with Hyper Suprime-Cam

The $AKARI$ space infrared telescope has performed near- to mid-infrared (MIR) observations on the North Ecliptic Pole Wide (NEPW) field (5.4 deg$^2$) for about one year. $AKARI$ took advantage of its continuous nine photometric bands, compared with NASA's $Spitzer$ and WISE space telescopes, which had only four filters with a wide gap in the MIR. The $AKARI$ NEPW field lacked deep and homogeneous optical data, limiting the use of nearly half of the IR sources for extra-galactic studies owing to the absence of photometric redshifts (photo-zs). To remedy this, we have recently obtained deep optical imaging over the NEPW field with 5 bands ($g$, $r$, $i$, $z$, and $Y$) of the Hyper Suprime-Camera (HSC) on the Subaru 8m telescope. We optically identify AKARI-IR sources along with supplementary $Spitzer$ and WISE data as well as pre-existing optical data. In this work, we derive new photo-zs using a $\chi^2$ template-fitting method code ($Le$ $Phare$) and reliable photometry from 26 selected filters including HSC, $AKARI$, CFHT, Maidanak, KPNO, $Spitzer$ and WISE data. We take 2026 spectroscopic redshifts (spec-z) from all available spectroscopic surveys over the NEPW to calibrate and assess the accuracy of the photo-zs. At z < 1.5, we achieve a weighted photo-z dispersion of $\sigma_{\Delta{z/(1+z)}}$ = 0.053 with $\eta$ = 11.3% catastrophic errors.Comment: 20 pages, 13 figures, accepted for publication in MNRAS. For summary video, please see http://youtu.be/hjNJRCoBIg

The Effect of Galaxy Interactions on Molecular Gas Properties

© 2018. The American Astronomical Society. All rights reserved.Galaxy interactions are often accompanied by an enhanced star formation rate (SFR). Since molecular gas is essential for star formation, it is vital to establish whether and by how much galaxy interactions affect the molecular gas properties. We investigate the effect of interactions on global molecular gas properties by studying a sample of 58 galaxies in pairs and 154 control galaxies. Molecular gas properties are determined from observations with the JCMT, PMO, and CSO telescopes and supplemented with data from the xCOLD GASS and JINGLE surveys at 12CO(1-0) and 12CO(2-1). The SFR, gas mass (), and gas fraction (f gas) are all enhanced in galaxies in pairs by ∼2.5 times compared to the controls matched in redshift, mass, and effective radius, while the enhancement of star formation efficiency (SFE ≡SFR/) is less than a factor of 2. We also find that the enhancements in SFR, and f gas, increase with decreasing pair separation and are larger in systems with smaller stellar mass ratio. Conversely, the SFE is only enhanced in close pairs (separation <20 kpc) and equal-mass systems; therefore, most galaxies in pairs lie in the same parameter space on the SFR- plane as controls. This is the first time that the dependence of molecular gas properties on merger configurations is probed statistically with a relatively large sample and a carefully selected control sample for individual galaxies. We conclude that galaxy interactions do modify the molecular gas properties, although the strength of the effect is dependent on merger configuration.Peer reviewedFinal Accepted Versio

Extinction-free Census of AGNs in the AKARI/IRC North Ecliptic Pole Field from 23-band Infrared Photometry from Space Telescopes

In order to understand the interaction between the central black hole and the whole galaxy or their co-evolution history along with cosmic time, a complete census of active galactic nuclei (AGN) is crucial. However, AGNs are often missed in optical, UV and soft X-ray observations since they could be obscured by gas and dust. A mid-infrared (mid-IR) survey supported by multiwavelength data is one of the best ways to find obscured AGN activities because it suffers less from extinction. Previous large IR photometric surveys, e.g., WISE and Spitzer, have gaps between the mid-IR filters. Therefore, star forming galaxy (SFG)-AGN diagnostics in the mid-IR were limited. The AKARI satellite has a unique continuous 9-band filter coverage in the near to mid-IR wavelengths. In this work, we take advantage of the state-of-the-art spectral energy distribution (SED) modelling software, CIGALE, to find AGNs in mid-IR. We found 126 AGNs in the NEP-Wide field with this method. We also investigate the energy released from the AGN as a fraction of the total IR luminosity of a galaxy. We found that the AGN contribution is larger at higher redshifts for a given IR luminosity. With the upcoming deep IR surveys, e.g., JWST, we expect to find more AGNs with our method