Biosignal Generation and Latent Variable Analysis with Recurrent Generative Adversarial Networks

The effectiveness of biosignal generation and data augmentation with biosignal generative models based on generative adversarial networks (GANs), which are a type of deep learning technique, was demonstrated in our previous paper. GAN-based generative models only learn the projection between a random distribution as input data and the distribution of training data.Therefore, the relationship between input and generated data is unclear, and the characteristics of the data generated from this model cannot be controlled. This study proposes a method for generating time-series data based on GANs and explores their ability to generate biosignals with certain classes and characteristics. Moreover, in the proposed method, latent variables are analyzed using canonical correlation analysis (CCA) to represent the relationship between input and generated data as canonical loadings. Using these loadings, we can control the characteristics of the data generated by the proposed method. The influence of class labels on generated data is analyzed by feeding the data interpolated between two class labels into the generator of the proposed GANs. The CCA of the latent variables is shown to be an effective method of controlling the generated data characteristics. We are able to model the distribution of the time-series data without requiring domain-dependent knowledge using the proposed method. Furthermore, it is possible to control the characteristics of these data by analyzing the model trained using the proposed method. To the best of our knowledge, this work is the first to generate biosignals using GANs while controlling the characteristics of the generated data

Low m/n Mode Behavior of MHD Plasma in LHD

Behaviors of low poloidal (m) and toroidal (n) Fourier modes in the Large Helical Device (LHD) are investigated by means of direct numerical simulations (DNS) of fully three-dimensional, nonlinear and compressiblemagnetohydrodynamics (MHD) equations. Starting from an ideal equilibrium with the position of vacuum magneticaxis Rax = 3.6 m and β0 = 4% finite pressure, a m/n = 2/1 mode grows in the DNS. Fluid motions on poloidal sectionsare governed by the two pairs of anti-parallel vortex pairs associated with the m/n = 2/1 modes. The vortex pairstransport plasma pressure from the core to edge region and bring about large pressure deformations. It is also shown that the toroidal part in the kinetic energy and the enstrophy are comparable to the poloidal parts of them. The numerical results demonstrate importance of investigating three-dimensional behaviors of MHD plasmas in LHD

ベトナム中部の伝統的木造建築の設計方法の特質

VĂN HÓA - LỊCH SỬ HUẾ QUA GÓC NHÌN LÀNG XÃ PHỤ CẬN VÀ QUAN HỆ VỚI BÊN NGOÀI Session 3: Văn hoá - lịch sử Huế trong mối quan hệ với bên ngoài　フエの文化と歴史：周辺集落と外部との関係からの視点より　Session 3: Culture - history of Hue in relationship with the outside regions　外との関係におけるフエの歴史・文

Nonlinear simulation of resistive ballooning modes in the Large Helical Device

Nonlinear simulations of a magnetohydrodynamic (MHD) plasma in full three-dimensional geometry of the Large Helical Device (LHD) [O. Motojima et al., Phys. Plasmas 6, 1843 (1999)] are conducted. A series of simulations shows growth of resistive ballooning instability, for which the growth rate is seen to be proportional to the one-third power of the resistivity. Nonlinear saturation of the excited mode and its slow decay are observed. Distinct ridge/valley structures in the pressure are formed in the course of the nonlinear evolution. The compressibility and the viscous heating, as well as the thermal conduction, are shown to be crucial to suppress the pressure deformations. Indication of a pressure-driven relaxation phenomenon that leads to an equilibrium with broader pressure profile is observed