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Nagoya-shi, Japan

The Toyota Technological Institute is a university located in Nagoya, Japan. Founded in 1981 by a large endowment from Toyota Motors Corporation, it originally only accepted students with some industrial work experience.TTI has a School of Engineering, a Master's Program and a Doctoral Program. The programs consist of three areas of coursework: Mechanical Systems Engineering, Electronics & Information Science, and Materials Science & Engineering. In 2003 Toyota also opened the Toyota Technological Institute at Chicago, jointly with the University of Chicago. This campus is mainly for the Ph.D students, studying Machine Learning, Algorithms & Complexity, Computer Vision, Speech Technologies and Computational Biology.Although TTI is a new and tiny university, it has been rapidly growing its reputation, as TSU ranked TTI as the 5th best Japanese university in 2010 and 4th in 2011. In this ranking, TTI has a best employment rate among all Japanese Universities.In 2012, TTI was ranked 1st in Asia in terms of average number of publication per faculty by the QS World University Rankings. Wikipedia.

Awano H.,Toyota Technological Institute
Journal of Magnetism and Magnetic Materials | Year: 2015

Current driven magnetic domain wall (DW) motions of ferri-magnetic TbFeCo wires have been investigated. In the case of a Si substrate, the critical current density (Jc) of DW motion was successfully reduced to 3×106 A/cm2. Moreover, by using a polycarbonate (PC) substrate with a molding groove of 600 nm width, the Jc was decreased to 6×105 A/cm2. In order to fabricate a logic in memory, a current driven spin logics (AND, OR, NOT) have been proposed and successfully demonstrated under the condition of low Jc. These results indicate that TbFeCo nanowire is an excellent candidate for next generation power saving memory and logic. © 2015 The Author.

Matsui H.,Toyota Technological Institute
IEEE Transactions on Information Theory | Year: 2014

In this paper, we establish a lemma in algebraic coding theory that frequently appears in the encoding and decoding of, e.g., Reed-Solomon codes, algebraic geometry codes, and affine variety codes. Our lemma corresponds to the nonsystematic encoding of affine variety codes, and can be stated by giving a canonical linear map as the composition of an extension through linear feedback shift registers from a Gröbner basis and a generalized inverse discrete Fourier transform. We clarify that our lemma yields the error-value estimation in the fast erasure-And-error decoding of a class of dual affine variety codes. Moreover, we show that systematic encoding corresponds to a special case of erasure-only decoding. The lemma enables us to reduce the computational complexity of error-evaluation from O(n^{3}) using Gaussian elimination to O(qn^{2}) with some mild conditions on n and q , where n is the code length and q is the finite-field size. © 1963-2012 IEEE.

Tiwari J.N.,Pohang University of Science and Technology | Tiwari R.N.,Toyota Technological Institute | Kim K.S.,Pohang University of Science and Technology
Progress in Materials Science | Year: 2012

One of the biggest challenges of 21st century is to develop powerful electrochemical energy devices (EEDs). The EEDs such as fuel cells, supercapacitors, and Li-ion batteries are among the most promising candidates in terms of power-densities and energy-densities. The nanostructured materials (NSMs) have drawn intense attention to develop highly efficient EEDs because of their high surface area, novel size effects, significantly enhanced kinetics, and so on. In this review article, we briefly introduce general synthesis, fabrication and their classification as zero-dimensional (0D), one dimensional (1D), two-dimensional (2D) and three-dimensional (3D) NSMs. Subsequently, we focus an attention on recent progress in advanced NSMs as building blocks for EEDs (such as fuel cells, supercapacitors, and Li-ion batteries) based on investigations at the 0D, 1D, 2D and 3D NSMs. © 2011 Elsevier Ltd. All rights reserved.

Agency: NSF | Branch: Standard Grant | Program: | Phase: ROBUST INTELLIGENCE | Award Amount: 854.13K | Year: 2014

Sign languages are the primary means of communication for millions of Deaf people in the world, including about 350,000-500,000 American Sign Language (ASL) users in the US. While the hearing population has benefited from advances in speech technologies such as speech recognition and spoken web search, much less progress has been made for sign language interfaces. Advances depend on improved technology for analyzing sign language from video. In addition, the linguistics of sign language is less well-understood than that of spoken language. This project addresses both of these needs, with an interdisciplinary approach that will contribute to research in linguistics, language processing, computer vision, and machine learning. Applications of the work include better access to ASL social media video archives, interactive recognition and search applications for Deaf individuals, and ASL-English interpretation assistance.

This project focuses on handshape in ASL, in particular on one constrained but very practical component: fingerspelling, or the spelling out of a word as a sequence of handshapes and trajectories between them. Fingerspelling comprises up to 35% of ASL, depending on the context, and includes 72% of ASL handshapes, making it an excellent testing ground. The project addresses gaps in existing work by focusing on handshape in various conditions, including fast, highly coarticulated signing. The main project activities include development of (1) robust automatic detection and recognition of fingerspelled words using new handshape models, including segmental and multi-segmental graphical models of ASL phonological features; (2) techniques for generalizing across signers, styles, and recording conditions; (3) improved phonetics and phonology of handshape, in particular contributing to an articulatory phonology of sign; and (4) publicly released multi-speaker, multi-style fingerspelling data and associated semi-automatic annotation.

Agency: NSF | Branch: Standard Grant | Program: | Phase: ADVANCES IN BIO INFORMATICS | Award Amount: 557.03K | Year: 2016

Proteins play fundamental roles in all biological processes. Complete description of protein structures and functions is a fundamental step towards understanding biological life and has various applications. Millions of protein sequences are available, but a majority of them have no experimentally-solved structures and functions. This project aims to greatly improve RaptorX, a fully-automated web server for computational prediction of protein structure and function, with the goal to deliver a long-term sustainable web portal to facilitate transformative research in biology. This web portal shall benefit a broad range of biological and biomedical applications, such as genome annotation, understanding of disease processes, drug design, precision medicine and even biomaterial and bio-energy development. The results will be disseminated to the broader community through a variety of venues: web servers, standalone software, publications and talks. Since late in 2011, RaptorX has served >25,000 worldwide users including middle- and high-school students. The standalone programs have been downloaded by >1500 worldwide users. After this project is fulfilled, RaptorX will contribute much more to the broader community. Students involved in this project will receive training in the intersection of computer science, molecular biology, biophysics, and biochemistry. Undergraduate and underrepresented students will be recruited through summer intern programs and collaborators. The research results will be integrated into course materials and used in the Illinois online bioinformatics program.

The RaptorX web server was originally developed for only template-based protein modeling. This project will transform RaptorX by first developing a few novel and powerful deep learning (e.g., Deep Conditional Convolutional Neural Fields) and structure learning (e.g., group graphical lasso) methods to significantly improve the accuracy of protein structure and functional prediction and then conducting an efficient implementation. The resultant RaptorX will be able to perform much more accurate prediction of protein secondary and tertiary structure, solvent accessibility and disordered regions, and the quality of a theoretical protein 3D model (in the absence of natives). This project will also expand the RaptorX server to perform contact prediction and contact-assisted protein folding for proteins without good templates. The RaptorX web server is available at http://raptorx.uchicago.edu, from which users can also download the standalone programs.

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