業績

2017年度

国際学術誌(英文)

Makoto Uemura, Ryosuke Itoh, Ioannis Liodakis, Dmitry Blinov, Masanori Nakayama, Longyin Xu, Naoko Sawada, Hsiang-Yun Wu, Issei Fujishiro
Optical polarization variations in the blazar PKS 1749+096
PASJ, 2017
PASJ2917
We report on the variation in the optical polarization of the blazar PKS 1749+096 observed in 2008–2015. The degree of polarization (PD) tends to increase in short flares having a time-scale of a few days. The object favors a polarization angle (PA) of 40∘–50∘ at the flare maxima, which is close to the position angle of the jet (20∘–40∘). Three clear polarization rotations were detected in the negative PA direction associated with flares. In addition, a rapid and large decrease in the PA was observed in the other two flares, while another two flares showed no large PA variation. The light curve maxima of the flares possibly tend to lag behind the PD maxima and color-index minima. The PA became −50∘ to −20∘ in the decay phase of active states, which is almost perpendicular to the jet position angle. We propose a scenario to explain these observational features, where transverse shocks propagate along curved trajectories. The favored PA at the flare maxima suggests that the observed variations were governed by the variations in the Doppler factor, δ. Based on this scenario, the minimum viewing angle of the source, θmin=4.8∘–6.6∘, and the location of the source, Δr≳0.1pc, from the central black hole were estimated. In addition, the acceleration of electrons by the shock and synchrotron cooling would have a time-scale similar to that of the change in δ. The combined effect of the variation in δ and acceleration/cooling of electrons is probably responsible for the observed diversity of the polarization variations in the flares.

国内学術誌(和文)

国際会議録論文

Naoko Sawada, Masanori NakayamaHsiang-Yun Wu, Makoto Uemura, Issei Fujishiro
TimeTubes: Visual Fusion and Validation for Ameliorating Uncertainties of Blazar Datasets from Different Observatories
in Proceedings of the Computer Graphics International Conference,
Article No. 14, Yokohama (Japan), 2017
CGI2017Sawada
Astronomers have been observing blazars to solve the mystery of the relativistic jet. A technique called TimeTubes uses a 3D volumetric tube to visualize the time-dependent multivariate observed datasets and allows astronomers to interactively analyze the dynamic behavior of and relationship among those variables. However, the observed datasets themselves exhibit uncertainty due to their errors and missing periods, whereas periods interpolated by TimeTubes result in a different type of uncertainty. In this paper, we present a technique for ameliorating such data- and mapping-inherent uncertainties: visual fusion of datasets for the same blazar from two different observatories. Visual data fusion with Time-Tubes enables astronomers to validate the datasets in a meticulous manner.

Hsiang-Yun Wu, Yusuke Niibe, Kazuho Watanabe, Shigeo Takahashi, Makoto Uemura, Issei Fujishiro
Making Many-to-Many Parallel Coordinate Plots Scalable by Asymmetric Biclustering
in Proceedings of IEEE Pacific Visualization 2017, Seoul (Korea), 2017
many-to-many2017
Datasets obtained through recently advanced measurement techniques tend to possess a large number of dimensions. This leads to explosively increasing computation costs for analyzing such datasets, thus making formulation and verification of scientific hypotheses very difficult. Therefore, an efficient approach to identifying feature subspaces of target datasets, that is, the subspaces of dimension variables or subsets of the data samples, is required to describe the essence hidden in the original dataset. This paper proposes a visual data mining framework for supporting semiautomatic data analysis that builds upon asymmetric biclustering to explore highly correlated feature subspaces. For this purpose, a variant of parallel coordinate plots, many-to-many parallel coordinate plots, is extended to visually assist appropriate selections of feature subspaces as well as to avoid intrinsic visual clutter. In this framework, biclustering is applied to dimension variables and data samples of the dataset simultaneously and asymmetrically. A set of variable axes are projected to a single composite axis while data samples between two consecutive variable axes are bundled using polygonal strips. This makes the visualization method scalable and enables it to play a key role in the framework. The effectiveness of the proposed framework has been empirically proven, and it is remarkably useful for many-to-many parallel coordinate plots.

国際会議ポスタ(査読付)

Naoko Sawada, Masanori NakayamaHsiang-Yun Wu, Makoto Uemura, Issei Fujishiro
TimeTubes: Visual Fusion for Ameliorating Uncertainty of Blazar Datasets from Different Observatories
IEEE Pacific Visualization 2017 Poster Session
SidebySideCaption
Blazars are active galactic nuclei whose relativistic jets ejected from the central black hole are pointing toward the Earth. Astronomers have attempted to classify blazars, whereas it is difficult to analyze the time-dependent multivariate datasets with the conventional visualization methods, such as animeted scatterplot matrices. In our previous study, a new visualization scheme, called TimeTubes, was proposed, which allows the astronomers to analyze dynamic changes in and feature causality among the multiple time-varying variables. In this poster, we present a core idea to ameliorate data-inherent and mapping-inherent uncertainty through visual fusion of datasets for the same blazar from two different observatories.

国内会議

中田 聖人藤代 一成
器官の準解剖学的データを用いた手のモデリング
Visual Computing/グラフィクスとCAD 合同シンポジウム2017(ポスタ発表),2017年6月
学会ページへ

篠﨑 紗衣子中山 雅紀藤代 一成
異方性をもつ紙繊維シートの破断シミュレーション
Visual Computing/グラフィクスとCAD 合同シンポジウム2017(ポスタ発表),2017年6月
学会ページへ

堀井 絵里藤代 一成
音源に同期する運指に注目した吹奏アニメーションの自動生成
Visual Computing/グラフィクスとCAD 合同シンポジウム2017(ポスタ発表),2017年6月
学会ページへ

口頭発表

Naoko Sawada, Masanori Nakayama, Hsiang-Yun Wu, Makoto Uemura, Issei Fujishiro
TimeTubes: Visual Fusion for Detailed and Precise Analysis of Time-Varying Multi-Dimensional Datasets
in Proceedings of the International Meeting on “High-Dimensional Data-Driven Science”, September 2017