Yuji Matsumoto

Bio: Yuji Matsumoto is an academic researcher from Tohoku University. The author has contributed to research in topics: Thin film & Pulsed laser deposition. The author has an hindex of 60, co-authored 788 publications receiving 19287 citations. Previous affiliations of Yuji Matsumoto include National Institute for Materials Science & Nara Institute of Science and Technology.

Room-Temperature Ferromagnetism in Transparent Transition Metal-Doped Titanium Dioxide

Abstract: Dilute magnetic semiconductors and wide gap oxide semiconductors are appealing materials for magnetooptical devices. From a combinatorial screening approach looking at the solid solubility of transition metals in titanium dioxides and of their magnetic properties, we report on the observation of transparent ferromagnetism in cobalt-doped anatase thin films with the concentration of cobalt between 0 and 8%. Magnetic microscopy images reveal a magnetic domain structure in the films, indicating the existence of ferromagnetic long-range ordering. The materials remain ferromagnetic above room temperature with a magnetic moment of 0.32 Bohr magnetons per cobalt atom. The film is conductive and exhibits a positive magnetoresistance of 60% at 2 kelvin.

Applying Conditional Random Fields to Japanese Morphological Analysis

Abstract: This paper presents Japanese morphological analysis based on conditional random fields (CRFs). Previous work in CRFs assumed that observation sequence (word) boundaries were fixed. However, word boundaries are not clear in Japanese, and hence a straightforward application of CRFs is not possible. We show how CRFs can be applied to situations where word boundary ambiguity exists. CRFs offer a solution to the long-standing problems in corpus-based or statistical Japanese morphological analysis. First, flexible feature designs for hierarchical tagsets become possible. Second, influences of label and length bias are minimized. We experiment CRFs on the standard testbed corpus used for Japanese morphological analysis, and evaluate our results using the same experimental dataset as the HMMs and MEMMs previously reported in this task. Our results confirm that CRFs not only solve the long-standing problems but also improve the performance over HMMs and MEMMs.

Statistical Dependency Analysis with Support Vector Machines

Abstract: In this paper, we propose a method for analyzing word-word dependencies using deterministic bottom-up manner using Support Vector machines. We experimented with dependency trees converted from Penn treebank data, and achieved over 90% accuracy of word-word dependency. Though the result is little worse than the most up-to-date phrase structure based parsers, it looks satisfactorily accurate considering that our parser uses no information from phrase structures.

High throughput fabrication of transition-metal-doped epitaxial ZnO thin films: A series of oxide-diluted magnetic semiconductors and their properties

Abstract: Combinatorial laser molecular-beam epitaxy method was employed to fabricate epitaxial ZnO thin films doped with all the 3d transition metal (TM) ions in a high throughput fashion The solubility behavior of TM ions was discussed from the viewpoints of the ionic radius and valence state The magneto-optical responses coincident with absorption spectra were observed for Mn- and Co-doped samples Cathodoluminescence spectra were studied for Cr-, Mn-, Fe-, and Co-doped samples, among which Cr-doped ZnO showed two sharp peaks at 297 eV and 371 eV, respectively, at the expense of the exciton emission peak of pure ZnO at 325 eV Different magnetoresistance behavior was observed for the samples codoped with n-type carriers Ferromagnetism was not observed for Cr- to Cu-doped samples down to 3 K

Chunking with support vector machines

Abstract: We apply Support Vector Machines (SVMs) to identify English base phrases (chunks). SVMs are known to achieve high generalization performance even with input data of high dimensional feature spaces. Furthermore, by the Kernel principle, SVMs can carry out training with smaller computational overhead independent of their dimensionality. We apply weighted voting of 8 SVMs-based systems trained with distinct chunk representations. Experimental results show that our approach achieves higher accuracy than previous approaches.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

A comprehensive review of zno materials and devices

Abstract: The semiconductor ZnO has gained substantial interest in the research community in part because of its large exciton binding energy (60meV) which could lead to lasing action based on exciton recombination even above room temperature. Even though research focusing on ZnO goes back many decades, the renewed interest is fueled by availability of high-quality substrates and reports of p-type conduction and ferromagnetic behavior when doped with transitions metals, both of which remain controversial. It is this renewed interest in ZnO which forms the basis of this review. As mentioned already, ZnO is not new to the semiconductor field, with studies of its lattice parameter dating back to 1935 by Bunn [Proc. Phys. Soc. London 47, 836 (1935)], studies of its vibrational properties with Raman scattering in 1966 by Damen et al. [Phys. Rev. 142, 570 (1966)], detailed optical studies in 1954 by Mollwo [Z. Angew. Phys. 6, 257 (1954)], and its growth by chemical-vapor transport in 1970 by Galli and Coker [Appl. Phys. ...

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Spintronics: a spin-based electronics vision for the future.

Abstract: This review describes a new paradigm of electronics based on the spin degree of freedom of the electron. Either adding the spin degree of freedom to conventional charge-based electronic devices or using the spin alone has the potential advantages of nonvolatility, increased data processing speed, decreased electric power consumption, and increased integration densities compared with conventional semiconductor devices. To successfully incorporate spins into existing semiconductor technology, one has to resolve technical issues such as efficient injection, transport, control and manipulation, and detection of spin polarization as well as spin-polarized currents. Recent advances in new materials engineering hold the promise of realizing spintronic devices in the near future. We review the current state of the spin-based devices, efforts in new materials fabrication, issues in spin transport, and optical spin manipulation.

Opinion Mining and Sentiment Analysis

Abstract: An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object. This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include material on summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.