Shiro Yano

Automated Linear Function Submission-Based Double Auction as Bottom-up Real-Time Pricing in a Regional Prosumers’ Electricity Network

A linear function submission-based double-auction (LFS-DA) mechanism for a regional electricity network is proposed in this paper. Each agent in the network is equipped with a battery and a generator. Each agent simultaneously becomes a producer and consumer of electricity, i.e., a prosumer and trades electricity in the regional market at a variable price. In the LFS-DA, each agent uses linear demand and supply functions when they submit bids and asks to an auctioneer in the regional market.The LFS-DA can achieve an exact balance between electricity demand and supply for each time slot throughout the learning phase and was shown capable of solving the primal problem of maximizing the social welfare of the network without any central price setter, e.g., a utility or a large electricity company, in contrast with conventional real-time pricing (RTP). This paper presents a clarification of the relationship between the RTP algorithm derived on the basis of a dual decomposition framework and LFS-DA. Specifically, we proved that the changes in the price profile of the LFS-DA mechanism are equal to those achieved by the RTP mechanism derived from the dual decomposition framework except for a constant factor.

Functional Connectivity Analysis of NIRS Data under Rubber Hand Illusion to Find a Biomarker of Sense of Ownership

The self-identification, which is called sense of ownership, has been researched through methodology of rubber hand illusion (RHI) because of its simple setup. Although studies with neuroimaging technique, such as fMRI, revealed that several brain areas are associated with the sense of ownership, near-infrared spectroscopy (NIRS) has not yet been utilized. Here we introduced an automated setup to induce RHI, measured the brain activity during the RHI with NIRS, and analyzed the functional connectivity so as to understand dynamical brain relationship regarding the sense of ownership. The connectivity was evaluated by multivariate Granger causality. In this experiment, the peaks of oxy-Hb on right frontal and right motor related areas during the illusion were significantly higher compared with those during the nonillusion. Furthermore, by analyzing the NIRS recordings, we found a reliable connectivity from the frontal to the motor related areas during the illusion. This finding suggests that frontal cortex and motor related areas communicate with each other when the sense of ownership is induced. The result suggests that the sense of ownership is related to neural mechanism underlying human motor control, and it would be determining whether motor learning (i.e., neural plasticity) will occur. Thus RHI with the functional connectivity analysis will become an appropriate biomarker for neurorehabilitation.

Human Visual Attention Model Based on Analysis of Magic for Smooth Human–Robot Interaction

In order to smoothly interact with humans, it is desirable that a robot can guide human attention and behaviors. In this study, we developed a model of human visual attention for guiding human attention based on an analysis of a magic trick performance. We measured human gaze points of people watching a video of a magic trick performance and compared them with the area where the magician intended to draw a spectator’s attention. The analysis showed that the relationship between the magician’s face, hands, and gaze plays an important role in guiding the spectator’s attention. On the basis of the preliminary user studies on watching the magic video, we integrated a saliency map and a manipulation map that describes the relationship between gaze and hands to develop a novel human attention model. The evaluation using the observed gaze points demonstrated that the proposed model can better explain human visual attention than the saliency map while people are watching a video of a magic trick performance.

Visual attention model for manipulating human attention by a robot

For smooth interaction between human and robot, the robot should have an ability to manipulate human attention and behaviors. In this study, we developed a visual attention model for manipulating human attention by a robot. The model consists of two modules, such as the saliency map generation module and manipulation map generation module. The saliency map describes the bottom-up effect of visual stimuli on human attention and the manipulation map describes the top-down effect of face, hands and gaze. In order to evaluate the proposed attention model, we measured human gaze points during watching a magic video, and applied the attention model to the video. Based on the result of this experiment, the proposed attention model can better explain human visual attention than the original saliency map.

Modeling of human attention based on analysis of magic

In this study, we developed a human attention model for smooth human-robot interaction. The model consists of the saliency map generation module and manipulation map generation module. The manipulation map describes top-down factors, such as human face, hands and gaze in the input image. To evaluate the proposed model, we applied the model to a magic video, and measured human gaze points during watching the video. Based on the experimental results, the proposed model can better explain human attention than the saliency map. Categories and Subject Descriptors I.2.9 [Artificial Intelligence]: Robotics General Terms Human Factors

Activeness Improves Cognitive Performance in Human-Machine Interaction

∗1Faculty of Science and Engineering, Chuo University 1-13-27 Kasuga, Bunkyo-ku, Tokyo 112-8551, Japan E-mail: tamura@mech.chuo-u.ac.jp ∗2Recruit Marketing Partners, Co., Ltd. 1-9-2 Marunouchi, Chiyoda-ku, Tokyo 100-6640, Japan ∗3Research Organization of Science and Technology, Ritsumeikan University 1-1-1 Nojihigashi, Kusatsu-shi, Shiga 525-8577, Japan ∗4Department of Neuropsychiatry, Keio University School of Medicine 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan ∗5Graduate School of Engineering, The University of Tokyo 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan

Slow dynamics perspectives on the Embodied-Brain Systems Science.

Recent researches point out the importance of the fast-slow cognitive process and learning process of self-body. Bayesian perspectives on the cognitive system also attract research attentions. The view of fast-slow dynamical system has long attracted wide range of attentions from physics to the neurobiology. In many research fields, there is a vast well-organized and coherent behavior in the multi degrees-of-freedom. This behavior matches the mathematical fact that fast-slow system is essentially described with a few variables. In this paper, we review the mathematical basis for understanding the fast-slow dynamical systems. Additionally, we review the basis of Bayesian statistics and provide a fast-slow perspective on the Bayesian inference.

An immersive virtual reality system for investigating human bodily self-consciousness

In recent research on brain-computer interfaces (BCIs), motor observation is considered to become a promising methodology for the BCI neurofeedback training, because it is believed to induce stronger bodily self-consciousness such as sense of agency, or sense of ownership, and thus it can cause a reliable electroencephalogram (EEG) feature for the BCI. However the rational relationship between the motor observation and bodily self-consciousness has not yet been clarified. To investigate whether motor observation from the first person point of view can modulate bodily self-consciousness, we developed an immersive virtual reality (VR) system, and executed a psychophysical experiment using the system. Experimental results demonstrate that the developed VR system enables subjects to have stronger bodily self-consciousness. It suggests that motor observation from the first person viewpoint contributes to neurofeedback training of motor imagery-based BCI.

Bayesian model of the Sense of Agency in normal subjects

The Sense of Agency (SoA) is the subjective sense such that I am the causal factor of the own experience [1].

Mirror descent search and its acceleration

Abstract In recent years, attention has been focused on the relationship between black-box optimization problem and reinforcement learning problem. In this research, we propose the Mirror Descent Search (MDS) algorithm which is applicable both for black box optimization problems and reinforcement learning problems. Our method is based on the mirror descent method, which is a general optimization algorithm. The contribution of this research is roughly twofold. We propose two essential algorithms, called MDS and Accelerated Mirror Descent Search (AMDS), and two more approximate algorithms: Gaussian Mirror Descent Search (G-MDS) and Gaussian Accelerated Mirror Descent Search (G-AMDS). This research shows that the advanced methods developed in the context of the mirror descent research can be applied to reinforcement learning problem. We also clarify the relationship between an existing reinforcement learning algorithm and our method. With two evaluation experiments, we show our proposed algorithms converge faster than some state-of-the-art methods.