Sophia University

About: Sophia University is a education organization based out in Tokyo, Japan. It is known for research contribution in the topics: Nonlinear system & Catalysis. The organization has 4986 authors who have published 7657 publications receiving 106567 citations. The organization is also known as: Jōchi Daigaku.

Rearrangement of microtubule polarity orientation during conversion of dendrites to axons in cultured pyramidal neurons.

Abstract: Axons and dendrites of neurons differ in the polarity orientation of their microtubules. Whereas the polarity orientation of microtubules in axons is uniform, with all plus ends distal, that in dendrites is nonuniform. The mechanisms responsible for establishment and maintenance of microtubule polarity orientation in neuronal processes remain unclear, however. We previously described a culture system in which dendrites of rat cortical neurons convert to axons. In the present study, we examined changes in microtubule polarity orientation in such dendrites. With the use of the hooking procedure and electron microscopy, we found that microtubule polarity orientation changed from nonuniform to uniform, with a plus end-distal arrangement, in dendrites that gave rise to axons during culture of neurons for 24 h. Microtubule polarity orientation remained nonuniform in dendrites that did not elongate. Axon regeneration at the dendritic tip thus triggered the disappearance of minus end-distal microtubules from dendrites. These minus end-distal microtubules also disappeared from dendrites during axon regeneration in the presence of inhibitors of actin polymerization, suggesting that actin-dependent transport of microtubules is not required for this process and implicating a previously unidentified mechanism in the establishment and maintenance of microtubule polarity orientation in neuronal processes. Cell Motil. Cytoskeleton 64: 347–359, 2007. ' 2007 Wiley-Liss, Inc.

Moving towards effective English language teaching in Japan: issues and challenges

Abstract: Compared with other countries in Asia, Japan is far behind in terms of introducing and delivering bilingual education, let alone effective immersion programmes. In order to make its citizens more bilingual, Japan has been introducing innovative measures including the implementation of the teaching of English in elementary education and a new curriculum guideline requirement of using English exclusively in all high school English classes. However, these innovations are met with opposition and obstacles. Before Japan can introduce effective bilingual and immersion programmes comparable to those in Europe, North America and other Asian countries, it is crucial that Japan addresses these concerns. At the same time, other linguistic resources unique to Japan are being neglected. To elaborate and explore the above issues, this article focuses on public English education and ethnic bilingual schools in Japan.

Hybrid Solar Photovoltaic/Wind Turbine Energy Generation System with Voltage-based Maximum Power Point Tracking

Abstract: This article proposes a hybrid energy system combining solar photovoltaic and wind turbine as a small-scale alternative source of electrical energy where conventional generation is not practical. A simple and cost-effective control technique has been proposed for maximum power point tracking from the photovoltaic array and wind turbine under varying climatic conditions without measuring the irradiance of the photovoltaic or the wind speed. The proposed system is attractive because of its simplicity, ease of control, and low cost. A complete description of the proposed hybrid system, along with detailed simulation results that ascertain its feasibility, are given to demonstrate the availability of the proposed system in this article. Simulation of the hybrid system under investigation was carried out using PSIM software.

Privacy Issues Regarding the Application of DNNs to Activity-Recognition using Wearables and Its Countermeasures by Use of Adversarial Training

Abstract: Deep neural networks have been successfully applied to activity recognition with wearables in terms of recognition performance. However, the black-box nature of neural networks could lead to privacy concerns. Namely, generally it is hard to expect what neural networks learn from data, and so they possibly learn features that highly discriminate user-information unintentionally, which increases the risk of information-disclosure. In this study, we analyzed the features learned by conventional deep neural networks when applied to data of wearables to confirm this phenomenon. Based on the results of our analysis, we propose the use of an adversarial training framework to suppress the risk of sensitive/unintended information disclosure. Our proposed model considers both an adversarial user classifier and a regular activity-classifier during training, which allows the model to learn representations that help the classifier to distinguish the activities but which, at the same time, prevents it from accessing user-discriminative information. This paper provides an empirical validation of the privacy issue and efficacy of the proposed method using three activity recognition tasks based on data of wearables. The empirical validation shows that our proposed method suppresses the concerns without any significant performance degradation, compared to conventional deep nets on all three tasks.