Dynamic mode decomposition in vector-valued reproducing kernel Hilbert spaces for extracting dynamical structure among observables

Understanding nonlinear dynamical systems (NLDSs) is challenging in a variety of engineering and scientific fields. Dynamic mode decomposition (DMD), which is a numerical algorithm for the spectral analysis of Koopman operators, has been attracting attention as a way of obtaining global modal descriptions of NLDSs without requiring explicit prior knowledge. However, since existing DMD algorithms are in principle formulated based on the concatenation of scalar observables, it is not directly applicable to data with dependent structures among observables, which take, for example, the form of a sequence of graphs. In this paper, we formulate Koopman spectral analysis for NLDSs with structures among observables and propose an estimation algorithm for this problem. This method can extract and visualize the underlying low-dimensional global dynamics of NLDSs with structures among observables from data, which can be useful in understanding the underlying dynamics of such NLDSs. To this end, we first formulate the problem of estimating spectra of the Koopman operator defined in vector-valued reproducing kernel Hilbert spaces, and then develop an estimation procedure for this problem by reformulating tensor-based DMD. As a special case of our method, we propose the method named as Graph DMD, which is a numerical algorithm for Koopman spectral analysis of graph dynamical systems, using a sequence of adjacency matrices. We investigate the empirical performance of our method by using synthetic and real-world data.Comment: 34 pages with 4 figures, Published in Neural Networks, 201

Decentralized policy learning with partial observation and mechanical constraints for multiperson modeling

Extracting the rules of real-world multi-agent behaviors is a current challenge in various scientific and engineering fields. Biological agents independently have limited observation and mechanical constraints; however, most of the conventional data-driven models ignore such assumptions, resulting in lack of biological plausibility and model interpretability for behavioral analyses. Here we propose sequential generative models with partial observation and mechanical constraints in a decentralized manner, which can model agents' cognition and body dynamics, and predict biologically plausible behaviors. We formulate this as a decentralized multi-agent imitation-learning problem, leveraging binary partial observation and decentralized policy models based on hierarchical variational recurrent neural networks with physical and biomechanical penalties. Using real-world basketball and soccer datasets, we show the effectiveness of our method in terms of the constraint violations, long-term trajectory prediction, and partial observation. Our approach can be used as a multi-agent simulator to generate realistic trajectories using real-world data.Comment: 17 pages with 7 figures and 4 tables, accepted in Neural Network

Suppressive Effect of Wild Saccharomyces cerevisiae and Saccharomyces paradoxus Strains on Ige Production by Mouse Spleen Cells

The genus Saccharomyces includes industrial yeasts that are used for bread and alcoholic beverage production. Saccharomyces strains isolated from natural resources, referred to as “wild” yeasts, are used for making products with strain-specific flavors that are different from those of the “domesticated” industrial yeasts. The physiological effects of wild yeast are poorly understood. In this study, we isolated 2 Saccharomyces cerevisiae strains (S02 − 03) and 5 Saccharomyces paradoxus strains (P01 − 02, S01, S04 − 05) from natural resources in the Kiso area and investigated the effect of these fungal strains on IgE production by mouse spleen cells. Culturing spleen cells with heat-killed yeasts resulted in elevated IFN-γ and IL-12 levels followed by significant reduction in IgE levels. The S03 and P01 strains induced IL-12 p40 and IL-10 expression in RAW264 cells. Thus, wild strains of S. cerevisiae and S. paradoxus regulate macrophage cytokine production to improve the Th1/Th2 immune balance and suppress IgE production.ArticleFOOD SCIENCE AND TECHNOLOGY RESEARCH. 19(6):1019-1027 (2013)journal articl

Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations

Modeling of real-world biological multi-agents is a fundamental problem in various scientific and engineering fields. Reinforcement learning (RL) is a powerful framework to generate flexible and diverse behaviors in cyberspace; however, when modeling real-world biological multi-agents, there is a domain gap between behaviors in the source (i.e., real-world data) and the target (i.e., cyberspace for RL), and the source environment parameters are usually unknown. In this paper, we propose a method for adaptive action supervision in RL from real-world demonstrations in multi-agent scenarios. We adopt an approach that combines RL and supervised learning by selecting actions of demonstrations in RL based on the minimum distance of dynamic time warping for utilizing the information of the unknown source dynamics. This approach can be easily applied to many existing neural network architectures and provide us with an RL model balanced between reproducibility as imitation and generalization ability to obtain rewards in cyberspace. In the experiments, using chase-and-escape and football tasks with the different dynamics between the unknown source and target environments, we show that our approach achieved a balance between the reproducibility and the generalization ability compared with the baselines. In particular, we used the tracking data of professional football players as expert demonstrations in football and show successful performances despite the larger gap between behaviors in the source and target environments than the chase-and-escape task.Comment: 14 pages, 5 figure

Localized turbulence structures in transitional rectangular-duct flow

Direct numerical simulations of transitional flow in a rectangular duct of cross-sectional aspect ratio A≡s/h=1–9 (s and h being the duct half-span and half-height, respectively) have been performed in the Reynolds number range Re≡ubh/ν=650–1500 (ub and ν being the bulk velocity and the kinematic viscosity, respectively) in order to investigate the dependence on the aspect ratio of spatially localized turbulence structures. It was observed that the lowest Reynolds number ReT, estimated in a specific way, for localized (transiently sustaining) turbulence decreases monotonically from ReT=730 for A=1 (square duct) with increasing aspect ratio, and for A=5 it nearly attains a minimal value ReT≈670 that is consistent with the onset Reynolds number of turbulent spots in a plane channel (A=∞). Turbulent states consist of localized structures that undergo a fundamental change around A=4. At Re=ReT turbulence for A=1–3 is streamwise-localized similar to turbulent puffs in pipe flow, while for A=5–9 turbulence at Re=ReT is also localized in the spanwise direction, similar to turbulent spots in plane channel flow. This structural change in turbulent states at Re=ReT is attributed to the exclusion of turbulence from the vicinity of the duct sidewalls in the case of a wide duct with A≳4: here the friction length on the sidewalls is so long that the size (around 100 times the friction length) of a self-sustaining minimal flow unit of streamwise vortices and streaks is larger than the duct height and, therefore, it cannot be accommodated

Factors Influencing Breast Density in Japanese Women Aged 40-49 in Breast Cancer Screening Mammography

A relatively large number of women in their 40s with high-density breasts, in which it can be difficult to detect lesions, are encountered in mammography cancer screenings in Japan. Here, we retrospectively investigated factors related to breast density. Two hundred women (40-49 years old) were examined at the screening center in our hospital. Multivariate analysis showed that factors such as small abdominal circumference, high HDL cholesterol, and no history of childbirth were related to high breast density in women in their 40s undergoing mammography. Other non-mammographic screening methods should be considered in women with abdominal circumferences ＜76cm, HDL-C >53mg/dl, and no history of childbirth, as there is a strong possibility of these women having high-density breasts that can make lesion detection difficult