担子菌類の基本的転写に関わるシスエレメントの解析

京都大学0048新制・課程博士博士(農学)甲第22707号農博第2423号新制||農||1080(附属図書館)学位論文||R2||N5300(農学部図書室)京都大学大学院農学研究科地域環境科学専攻(主査)教授 本田 与一, 教授 田中 千尋, 教授 吉村 剛学位規則第4条第1項該当Doctor of Agricultural ScienceKyoto UniversityDGA

Matrix of Polynomials Model based Polynomial Dictionary Learning Method for Acoustic Impulse Response Modeling

We study the problem of dictionary learning for signals that can be represented as polynomials or polynomial matrices, such as convolutive signals with time delays or acoustic impulse responses. Recently, we developed a method for polynomial dictionary learning based on the fact that a polynomial matrix can be expressed as a polynomial with matrix coefficients, where the coefficient of the polynomial at each time lag is a scalar matrix. However, a polynomial matrix can be also equally represented as a matrix with polynomial elements. In this paper, we develop an alternative method for learning a polynomial dictionary and a sparse representation method for polynomial signal reconstruction based on this model. The proposed methods can be used directly to operate on the polynomial matrix without having to access its coefficients matrices. We demonstrate the performance of the proposed method for acoustic impulse response modeling.Comment: 5 pages, 2 figure

Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices

Smart devices with built-in sensors, computational capabilities, and network connectivity have become increasingly pervasive. The crowds of smart devices offer opportunities to collectively sense and perform computing tasks in an unprecedented scale. This paper presents Crowd-ML, a privacy-preserving machine learning framework for a crowd of smart devices, which can solve a wide range of learning problems for crowdsensing data with differential privacy guarantees. Crowd-ML endows a crowdsensing system with an ability to learn classifiers or predictors online from crowdsensing data privately with minimal computational overheads on devices and servers, suitable for a practical and large-scale employment of the framework. We analyze the performance and the scalability of Crowd-ML, and implement the system with off-the-shelf smartphones as a proof of concept. We demonstrate the advantages of Crowd-ML with real and simulated experiments under various conditions

THE EFFECT OF THE FINANCIAL CRISIS ON CEO COMPENSATION IN BAD VERSUS GOOD PERFORMANCE FIRMS

This paper investigates the relation between firm performance preceding the Financial Crisis and their CEO compensation after the Crisis. We find a significant decrease in CEO compensation for firms that had bad performance prior to the Crisis, compared to those who performed well before the Crisis. This result remains after controlling for firm size, accounting performance, and year and industry fixed effects. The decrease in compensation seems to be derived from the drop in equity-based compensation. We conclude that boards are effective and considered the performance of the firm prior to the Crisis when they considered setting the compensation following the shock of the Crisis

Anisotropic Magneto-conductance of InAs Nanowire: Angle Dependent Suppression of 1D Weak Localization

The magneto-conductance of an InAs nanowire is investigated with respect to the relative orientation between external magnetic field and the nanowire axis. It is found that both the perpendicular and the parallel magnetic fields induce a positive magneto-conductance. Yet the parallel magnetic field induced longitudinal magneto-conductance has a smaller magnitude. This anisotropic magneto-transport phenomenon is studied as a function of temperature, magnetic field strength and at an arbitrary angle between the magnetic field and the nanowire. We show that the observed effect is in quantitative agreement with the suppression of one-dimensional (1D) weak localization

LED-Induced Fluorescence System for Tea Classification and Quality Assessment

A fluorescence system is developed by using several light emitting diodes (LEDs) with different wavelengths as excitation light sources. The fluorescence detection head consists of multi LED light sources and a multimode fiber for fluorescence collection, where the LEDs and the corresponding filters can be easily chosen to get appropriate excitation wavelengths for different applications. By analyzing fluorescence spectra with the principal component analysis method, the system is utilized in the classification of four types of green tea beverages and two types of black tea beverages. Qualities of the Xihu Longjing tea leaves of different grades, as well as the corresponding liquid tea samples, are studied to further investigate the ability and application of the system in the evaluation of classification/quality of tea and other foods