Plenary Lecture in Symposium: Numerical Simulation and Visual Analytics of Nonlinear Problems
Visual Causality Exploration and its applications
Koji Koyamada, Professor
Academic Center for Computing and Media Studies, Kyoto University, Japan
In the big data era, it is expected that every citizen can access the open data and participate in a scientific research. For realizing such a data science, visualization techniques will play an important role since they will enable the big data to be transmitted to the brain efficiently.
It is highly expected for visualization to facilitate an exploration of a causality in a data science field. Although it is possible to calculate a correlation between data items (variables) by using a statistic method, the causality is a feature which domain experts can clarify by making the best of their professional knowledge. There are several examples of illogically inferring causation from correlation. That is why a visualization plays an important role in the scientific discovery.
In this talk, we would like to introduce our activities on visual causality exploration. They include an interactive specification of a latent variable which explains several observable variables by using a causality graph in a phenotypic expression network, and an interactive exploration of a causality between two time-varying variables defined on computational grids.
Prof. Koji Koyamada is currently a professor at the Academic Center for Computing and Media Studies, Kyoto University, Japan. His research interest includes modeling & simulation and visualization. He is an associate member of the Science Council of Japan, a former president of the Visualization Society Japan, and a former president of Japan Society of Simulation Technology. He received the IEMT/IMC outstanding paper award in 1998, the VSJ contribution award in 2009 and the VSJ outstanding paper award in 2010. He received his B.S., M.S. and Ph.D. degrees in electronic engineering from Kyoto University, Japan in 1983, 1985 and 1994, respectively, and worked for IBM Japan from 1985 to 1998. From 1998 to 2001 he was an associate professor at the Iwate Prefectural University, Japan. From 2001 to 2003, he was an associate professor at Kyoto University, Japan.
Plenary Lecture in Symposium on Simulation Technology for Safe, Secure and Resilient Society
Cyber-physical System and Industrial Applications of Large-Scale Graph Analysis and Optimization Problem
Katsuki Fujisawa, Professor
Institute of Mathematics for Industry, Kyushu University
Artificial Intelligence Research Center, Advanced Industrial Science and Technology
Global Scientific Information and Computing Center, Tokyo Institute of Technology
Research Center for Statistical Machine Learning, The Institute of Statistical Mathematics
In this talk, we present our ongoing research project. We have started the research project for developing the Urban OS (Operating System) on a large-scale city from 2013. The Urban OS, which is regarded as one of emerging applications of cyber-physical system (CPS), gathers big data sets of people and transportation movements by utilizing different sensor technologies and storing them to the cloud storage system. We have another research project whose objective is to develop advanced computing and optimization infrastructures for extremely large-scale graphs on post peta-scale supercomputers. For example, our project team was a winner of the 8th and 10th to 14th Graph500 benchmark. The Urban OS employs the graph analysis system developed by this research project and provides a feedback to a predicting and controlling center to optimize many social systems and services.
Katsuki Fujisawa has been a Full Professor at the Institute of Mathematics for Industry (IMI) of Kyushu University. He had also been a research director of the JST (Japan Science and Technology Agency) CREST (Core Research for Evolutional Science and Technology) post-Peta High Performance Computing from 2011 to 2017. He received his Ph. D. from the Tokyo Institute of Technology in 1998. The objective of the JST CREST project is to develop an advanced computing and optimization infrastructure for extremely large-scale graphs on post peta-scale supercomputers. His project team has challenged the Graph500 benchmark, which is designed to measure the performance of a computer system for applications that require irregular memory and network access patterns. In 2014 to 2016, his project team was a winner of the 8th and 10th to 14th Graph500 benchmark. In 2017, He received the Prize for Science and Technology (Research Category), Commendation for Science and Technology by the Minister of Education、Culture、Sports、Science and Technology, Japan
Invited Talk #1 in Symposium: New Development of Simulational Science in Statistical Physics
Machining learning the quantum phase transitions in random systems
Physics Division, Sophia University
Quantum phase transition is a zero temperature phase transition caused by the change of quantum fluctuations with varying the parameter(s) of Hamiltonian. In the presence of randomness such as random potentials and lattice defects, the system shows various quantum phases. Here we apply the convolutional neural network to draw the phase diagram.
Tomi Ohtsuki, Doctor of Science (University of Tokyo, 1989), is Professor of physics at Sophia University, Tokyo, where he conducts theoretical and computational researches in condensed matter physics. His recent research focuses on quantum transport phenomena such as the Anderson transition, conductance fluctuations, Hall and spin Hall effects in nanoscale systems. His research has been published by Physical Review Letters, Physical Review B, Physics Reports, Journal of the Physical Society of Japan, and others.
Invited Talk in Symposium: Numerical Simulation and Visual Analytics of Nonlinear Problems
Energy-Based Multiscale Modeling of Magnetic Material
Tetsuji Matsuo, Professor
The development of magnetic-material simulator is a challenging task because of its multiscale nature where domain-wall behavior in nm-scale affects macroscopic property in mm/cm-scale. Based on mesoscopic magnetic-domain modeling in crystal-grain scale, we developed a physical multiscale model of magnetic material. It is an energy-based model that can take into account influence of physical factors in their energy forms. For example, the magneto-mechanical interaction can be analyzed by including the magneto-elastic energy as an energy component. The multi-scale model successfully reconstructed stress-dependent properties of silicon steel and magnetization properties of a thin-film magneto-impedance element. Magnetization analysis [Fig. 1(a)] of silicon steel under mechanical stress predicts the stress dependence of hysteresis loss [Fig. 1(b)], which quantitatively agrees with the measured loss.
Tetsuji Matsuo received the B.E., M.E., and Dr. Eng. degrees from Kyoto University, Japan, in 1986, 1988 and 1991, respectively. He became a Research Associate, a Lecturer, and an Associate Professor at Kyoto University in 1991, 2001, and 2003, respectively. He is currently a Professor in the Department of Electrical Engineering, the Graduate School of Engineering, Kyoto University. His current research interests include computational electromagnetics and magnetic material modeling.
Invited Talk in Symposium on Simulation Technology for Safe, Secure and Resilient Society
Factors of Security Breach
Cybersecurity Advisory, KPMG Consulting
Today we hear Cyber attacks news all around the world. And in Japan, we recently had major PII breach in June, 2015. In this talk, I will examine the factors of Security Breach focusing on 3 major factors why breach happens when Cyber attacks hits corporations. Companies can make effort to protect against from Cyber attacks but with attackers growing sophisticated more and more there is no single bullet for Cyber attacks. By focusing on the factors of security breach we can help the corporations to limit the damage of security breach at minimum if not none. Three 3 major factors: People, Process, and Technology.
Ms. Yamashita is a Senior Manager in KPMG Consulting Japan. She is a member of the Security Advisory Group where she focuses on security advisory and security program assessments. Prior to joining KPMG, Ms. Yamashita spent 10 years in Microsoft engineering Windows. Then Ms. Yamashita has gained 7 years of experience in security field where she was Threat research engineer, Sales Engineer, then consultant providing security transformation services to global corporations.
Tutorial in Symposium: Numerical Simulation and Visual Analytics of Nonlinear Problems
Introduction to Visualization System, KVS
College of Information Science & Engineering, Ritsumeikan University
Kyoto Visualization System (KVS) is a multi-platform, open source C++ Toolkit for developing scientific visualization applications. KVS provides useful classes to quickly implement surface rendering, volume rendering, particle-based rendering, and others. KVS users are required to have knowledge on C++ languages, but not on OpenGL, GPU, or details of volume-rendering algorithms. In this tutorial, we show how to implement visualization applications using KVS by examples such as volume rendering, surface rendering and fused visualization (These figures show examples of particle-based rendering created by using KVS).
Kyoko Hasegawa received her PhD in engineering from the University of Tsukuba, Japan, in 2004. She has been a lecture at College of Information Science and Engineering, Ritsumeikan University, Japan, since 2017.
Kyoko Hasegawa has received many academic awards. For example, Best Paper Award in Asia Simulation Conference 2012 (on visualization of surgery simulation), Best Art Work Award from the Visualization Society of Japan in 2014 (on visualization of cultural assets), Best Paper Award from Japan Society for Simulation Technology in 2015 (on visualization of large-scale particle fluid simulation).
Tutorial in JSST Student Research Symposium
Construction of muscle activity model based on Bayesian network and kinematic evaluation
Tokyo Denki University
We build foot muscular activity model based on Bayesian network and evaluate kinematic aspect. We aimed to enable quantitative selection of lower foot orthoses based on a patient’s muscular activity in the lower foot. Physical models require three dimensional motion analy-sis and force plate. These measurement systems cannot be used clinically, so they are not suitable for making these clinical use. Therefore, we chose Bayesian network to construct a model for estimating the muscular activity from parameters that can be measured easily, such as joint angle and sole pressure. Here, we verify the kinematic validity of the model parent node for foot muscles.
Jun Inoue received him PhD in engineering from the Waseda University, Japan, in 2013. He is currently an assistant professor at Tokyo Denki University. His current research interests include development of welfare equipment, human motion analysis, construction of muscle activity model using information engineering approach.