Details of the Researcher

PHOTO

Motoki Shiga
Section
Unprecedented-scale Data Analytics Center
Job title
Professor
Degree

  • 博士（工学）（岐阜大学）

  • 修士（工学）（岐阜大学）

Research History 13

  • 2024/05 - Present
    National Institute for Materials Science　Photoemission Spectroscopy Group, Advanced Materials Characterization Field, Center for Basic Research on Materials　Invited chief researcher

  • 2023/04 - Present
    Tohoku University　Applied Information Sciences, Graduate School of Information Sciences Department of Applied Information Sciences　Professor

  • 2022/10 - Present
    Osaka University　Research Center for Ultra-High Voltage Electron Microscopy　Guest Professor

  • 2022/05 - Present
    Tohoku University　Unprecedented-scale Data Analytics Center　Professor

  • 2018/06 - Present
    RIKEN　Center for Advanced Intelligence Project　Visiting researcher

  • 2022/04 - 2022/09
    Osaka University　Research Center for Ultra-High Voltage Electron Microscopy　Guest Associate Professor

  • 2017/04 - 2022/04
    Gifu University　Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering　Associate Professor

  • 2016/10 - 2020/03
    Japan Science and Technology Agency　Advanced Materials Informatics Group, Strategic Basic Research Programs　PRESTO(Precursory Research for Embryonic Science and Technology) Researcher

  • 2013/10 - 2017/03
    Gifu University　Department of Electrical, Electronic and Computer Engineering (Informatics Course), Faculty of Engineering, Gifu University　Assistant Professor (Tenure Track)

  • 2011/04 - 2013/09
    Graduate School of Computer Science and Engineering, Toyohashi University of Technology　Assistant Professor

  • 2008/04 - 2011/03
    Graduate School of Pharmaceutical Sciences, Kyoto University　Assistant Professor

  • 2008/04 - 2011/03
    Bioinformatics Center, Institute for Chemical Research, Kyoto University　Assistant Professor

  • 2006/04 - 2008/03
    Bioinformatics Center, Institute for Chemical Research, Kyoto University　Postdoctoral Researcher

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Committee Memberships 9

  • 日本顕微鏡学会 顕微鏡計測インフォマティックス研究部会　幹事

    2019/04 - Present

  • 日本応用数理学会・機械学習研究部会　主査

    2018/04 - 2020/03

  • 日本応用数理学会・機械学習研究部会　幹事

    2016/04 - 2018/03

  • 日本化学会情報化学部会・若手の会　コアメンバー

    2014/04 - 2017/03

  • 第19回情報論的学習理論ワークショップ　プログラム委員

    2016 - 2016

  • The 10th International Workshop on Machine Learning in Systems Biology　Program Committee

    2016 - 2016

  • 計測自動制御学会 ライフエンジニアリング部門　幹事

    2013/04 - 2014/03

  • 第40回構造活性相関シンポジウム　実行委員

    2012 - 2012

  • The 10th Annual International Workshop on Bioinformatics and Systems Biology　Local Organizing Committee

    2010 - 2010

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Research Interests 19

  • 計測インフォマティクス

  • Materials Informatics

  • Material Informatics

  • Statistical Machine Learning

  • Chemoinformatics

  • 遺伝子発現量

  • 統計的検定

  • 生物ネットワーク

  • Bioinformatics

  • 行列分解

  • 頻出パターンマイニング

  • 遺伝子機能解析

  • 遺伝子ネットワーク

  • データ統合

  • Data Mining

  • Machine Learning

  • 教師有り学習

  • クラスタリング

  • 半教師あり学習

Research Areas 5

  • Informatics / Mathematical informatics /

  • Informatics / Intelligent informatics /

  • Informatics / Biological, health, and medical informatics /

  • Informatics / Statistical science /

  • Informatics / Information theory /

Awards 3

  1. ward of the Outstanding Papers

    2020/06　The Ceramic Society of Japan　Award of the Outstanding Papers Published in the JCS-Japan in 2019

  2. a Top Performer of DREAM 9.5 Prostate Cancer DREAM Challenge, Sub-Challenge 1-b.

    2015/08/17　Dream Challenges

  3. 平成 16 年度連合大会奨励賞

    2005/01/31　電気関係学会東海支部

Papers 73

  1. Relationship between network topology and negative electrode properties in Wadsley–Roth phase TiNb2O7 Peer-reviewed

    Naoto Kitamura, Hikari Matsubara, Koji Kimura, Ippei Obayashi, Yohei Onodera, Ken Nakashima, Hidetoshi Morita, Motoki Shiga, Yasuhiro Harada, Chiaki Ishibashi, Yasushi Idemoto, Koichi Hayashi

    NPG Asia Materials　16　(1)　62　2024/12/10

    Publisher: Springer Science and Business Media LLC

    DOI： 10.1038/s41427-024-00581-5 　

    eISSN： 1884-4057

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    Abstract Wadsley–Roth phase TiNb2O7, with an octahedral network consisting of TiO6 and NbO6, has attracted significant attention as a negative electrode material for lithium-ion batteries in recent years owing to its excellent safety and high discharge capacity. In this work, we investigated the effect of the network structure (intermediate-range structure), which is considered to form Li+ conduction pathways, on the electrode properties of TiNb2O7. To this end, we prepared TiNb2O7 samples with different charge/discharge properties and generated atomic configurations that simultaneously reproduce both total scattering and Bragg profile data. Topological analyses based on persistent homology demonstrated that the network disorder hidden in the average structure (crystal structure) significantly degrades the negative electrode properties. In conclusion, controlling the network topology is considered the key to improving the negative electrode properties of TiNb2O7.

  2. Unravelling the density-driven modification of the topology generated by the interconnection of SiO4 tetrahedra in silica polymorphs Peer-reviewed

    Shinji Kohara, Shuya Sato, Motoki Shiga, Yohei Onodera, Hirokazu Masai, Toru Wakihara, Atsunobu Masuno, Akihiko Hirata, Naoto Kitamura, Yasushi Idemoto, Koji Kimura, Koichi Hayashi

    Journal of the Ceramic Society of Japan　132　(12)　653-662　2024/12/01

    Publisher: Ceramic Society of Japan

    DOI： 10.2109/jcersj2.24093 　

    ISSN： 1882-0743

    eISSN： 1348-6535

  3. 化学結合ネットワークに基づく構造秩序解析 Invited

    志賀元紀

    NEW GLASS　39　(3)　22-27　2024/11

  4. Nearly close-packed atomic arrangements in BaTi2O5 glass Peer-reviewed

    Hiroyuki Inoue, Atsunobu Masuno, Motoki Shiga, Yasuhiro Watanabe

    Scripta Materialia　252　2024/11/01

    DOI： 10.1016/j.scriptamat.2024.116271 　

    ISSN： 1359-6462

  5. 機械学習を用いた原子間ポテンシャル Invited

    志賀元紀

    NEW GLASS　39　(2)　36-42　2024/07

  6. Fast computational approach with prior dimension reduction for three-dimensional chemical component analysis using CT data of spectral imaging Peer-reviewed

    Motoki Shiga, Taisuke Ono, Kenichi Morishita, Keiji Kuno, Nanase Moriguchi

    Microscopy　2024/05/17

    Publisher: Oxford University Press (OUP)

    DOI： 10.1093/jmicro/dfae027 　

    ISSN： 2050-5698

    eISSN： 2050-5701

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    Abstract Spectral image (SI) measurement techniques, such as X-ray absorption fine structure (XAFS) imaging and scanning transmission electron microscopy (STEM) with energy-dispersive X-ray spectroscopy (EDS) or electron energy loss spectroscopy (EELS), are useful for identifying chemical structures in composite materials. Machine-learning techniques have been developed for automatic analysis of SI data and their usefulness has been proven. Recently, an extended measurement technique combining SI with a computed tomography (CT) technique (CT-SI), such as CT-XAFS and STEM-EDS/EELS tomography, was developed to identify the three-dimensional (3D) structures of chemical components. CT-SI analysis can be conducted by combining CT reconstruction algorithms and chemical component analysis based on machine-learning techniques. However, this analysis incurs high-computational costs owing to the size of the CT-SI datasets. To address this problem, this study proposed a fast computational approach for 3D chemical component analysis in an unsupervised learning setting. The primary idea for reducing the computational cost involved compressing the CT-SI data prior to CT computation and performing 3D reconstruction and chemical component analysis on the compressed data. The proposed approach significantly reduced the computational cost without losing information about the 3D structure and chemical components. We experimentally evaluated the proposed approach using synthetic and real CT-XAFS data, which demonstrated that our approach achieved a significantly faster computational speed than the conventional approach while maintaining analysis performance. As the proposed procedure can be implemented with any CT algorithm, it is expected to accelerate 3D analyses with sparse regularized CT algorithms in noisy and sparse CT-SI datasets.

  7. Direct observation of the atomic density fluctuation originating from the first sharp diffraction peak in SiO2 glass Peer-reviewed

    Akihiko Hirata, Shuya Sato, Motoki Shiga, Yohei Onodera, Koji Kimoto, Shinji Kohara

    NPG Asia Materials　16　(1)　25　2024/05/10

    Publisher: Springer Science and Business Media LLC

    DOI： 10.1038/s41427-024-00544-w 　

    eISSN： 1884-4057

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    Abstract The intermediate-range order of covalently bonded glasses has been extensively studied in terms of their diffraction peaks observed at low scattering angles; these peaks are called the first sharp diffraction peaks (FSDPs). Although the atomic density fluctuations originating from the quasilattice planes are a critical scientific target, direct experimental observations of these fluctuations are still lacking. Here, we report the direct observation of the atomic density fluctuations in silica glass by energy-filtered angstrom-beam electron diffraction. The correspondence between the local electron diffraction patterns of FSDPs and the atomic configurations constructed based on the X-ray and neutron diffraction results revealed that the local atomic density fluctuations originated from the quasi-periodic alternating arrangements of the columnar chain-like atomic configurations and interstitial tubular voids, as in crystals. We also discovered longer-range fluctuations associated with the shoulder of the FSDP on the low-Q side. The hierarchical fluctuations inherent in materials could aid in the elucidation of their properties and performance.

  8. Visual explanations of machine learning model estimating charge states in quantum dots Peer-reviewed

    Yui Muto, Takumi Nakaso, Motoya Shinozaki, Takumi Aizawa, Takahito Kitada, Takashi Nakajima, Matthieu R. Delbecq, Jun Yoneda, Kenta Takeda, Akito Noiri, Arne Ludwig, Andreas D. Wieck, Seigo Tarucha, Atsunori Kanemura, Motoki Shiga, Tomohiro Otsuka

    APL Machine Learning　2　(2)　026110　2024/04/15

    Publisher: AIP Publishing

    DOI： 10.1063/5.0193621 　

    eISSN： 2770-9019

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    Charge state recognition in quantum dot devices is important in the preparation of quantum bits for quantum information processing. Toward auto-tuning of larger-scale quantum devices, automatic charge state recognition by machine learning has been demonstrated. For further development of this technology, an understanding of the operation of the machine learning model, which is usually a black box, will be useful. In this study, we analyze the explainability of the machine learning model estimating charge states in quantum dots by gradient weighted class activation mapping. This technique highlights the important regions in the image for predicting the class. The model predicts the state based on the change transition lines, indicating that human-like recognition is realized. We also demonstrate improvements of the model by utilizing feedback from the mapping results. Due to the simplicity of our simulation and pre-processing methods, our approach offers scalability without significant additional simulation costs, demonstrating its suitability for future quantum dot system expansions.

  9. 物性予測のための機械学習法 Invited

    志賀元紀

    NEW GLASS　39　(1)　34-39　2024/03

  10. Atomic and Electronic Structure in MgO–SiO2 Peer-reviewed

    Yuta Shuseki, Shinji Kohara, Tomoaki Kaneko, Keitaro Sodeyama, Yohei Onodera, Chihiro Koyama, Atsunobu Masuno, Shunta Sasaki, Shohei Hatano, Motoki Shiga, Ippei Obayashi, Yasuaki Hiraoka, Junpei T. Okada, Akitoshi Mizuno, Yuki Watanabe, Yui Nakata, Koji Ohara, Motohiko Murakami, Matthew G. Tucker, Marshall T. McDonnell, Hirohisa Oda, Takehiko Ishikawa

    The Journal of Physical Chemistry A　128　(4)　716-726　2024/01/18

    Publisher: American Chemical Society (ACS)

    DOI： 10.1021/acs.jpca.3c05561 　

    ISSN： 1089-5639

    eISSN： 1520-5215

  11. Ring-originated anisotropy of local structural ordering in amorphous and crystalline silicon dioxide Peer-reviewed

    Motoki Shiga, Akihiko Hirata, Yohei Onodera, Hirokazu Masai

    Communications Materials　4　91　2023/11/03

    DOI： 10.1038/s43246-023-00416-w 　

  12. Structural-Order Analysis Based on Applied Mathematics

    Motoki Shiga, Ippei Obayashi

    The Materials Research Society Series　265-288　2023/10/26

    Publisher: Springer Nature Singapore

    DOI： 10.1007/978-981-99-5235-9_11 　

    ISSN： 2730-7360

    eISSN： 2730-7379

  13. 数理情報科学を用いた構造秩序解析 Invited

    志賀 元紀, 森田 秀利, 大林 一平

    セラミックス　58　(8)　527-530　2023/08

  14. Ring compaction as a mechanism of densification in amorphous silica

    Philip S. Salmon, Anita Zeidler, Motoki Shiga, Yohei Onodera, Shinji Kohara

    Physical Review B　107　(14)　2023/04/12

    Publisher: American Physical Society (APS)

    DOI： 10.1103/physrevb.107.144203 　

    ISSN： 2469-9950

    eISSN： 2469-9969

  15. Local structure analysis of disordered materials via contrast variation in scanning transmission electron microscopy

    Koji Kimoto, Motoki Shiga, Shinji Kohara, Jun Kikkawa, Ovidiu Cretu, Yohei Onodera, Kazuo Ishizuka

    AIP Advances　12　(9)　095219-095219　2022/09/01

    Publisher: AIP Publishing

    DOI： 10.1063/5.0104798 　

    eISSN： 2158-3226

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    The crystallographic structures of disordered materials are typically analyzed using diffractometry techniques, such as x-ray diffraction (XRD), neutron diffraction (ND), and electron diffraction (ED). Here, we demonstrate a novel technique to analyze the local structure of disordered materials via scanning transmission electron microscopy (STEM) under a contrast variation scheme. Contrast variation is a scheme used for the analysis of bulk materials, which combines two different diffractometry techniques with discrete scattering factors, such as ND and XRD. The STEM image contrasts of annular dark-field (ADF) and annular bright-field (ABF) imaging, which are characterized by different atomic number dependences, are simultaneously utilized. Simulated STEM images of amorphous SiO2 are examined using Fourier transform and autocorrelation operations, revealing that the Fourier transforms of ADF and ABF images are consistent with the results of conventional XRD/ED and ND techniques, respectively. The autocorrelation of the ABF image indicates the short-range ordering of light elements, which cannot be accomplished using conventional TEM, ED, and XRD techniques. As such, employing the contrast variation scheme in STEM imaging paves the way for analyzing the local crystallographic structure of non-monoatomic materials.

  16. A Generalized Framework of Multifidelity Max-Value Entropy Search Through Joint Entropy.

    Shion Takeno, Hitoshi Fukuoka, Yuhki Tsukada, Toshiyuki Koyama, Motoki Shiga, Ichiro Takeuchi, Masayuki Karasuyama

    Neural Computation　34　(10)　2145-2203　2022

    DOI： 10.1162/neco_a_01530 　

  17. Relationship between diffraction peak, network topology, and amorphous-forming ability in silicon and silica

    Shinji Kohara, Motoki Shiga, Yohei Onodera, Hirokazu Masai, Akihiko Hirata, Motohiko Murakami, Tetsuya Morishita, Koji Kimura, Kouichi Hayashi

    Scientific Reports　11　(1)　2021/12

    Publisher: Springer Science and Business Media LLC

    DOI： 10.1038/s41598-021-00965-5 　

    eISSN： 2045-2322

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    <title>Abstract</title>The network topology in disordered materials is an important structural descriptor for understanding the nature of disorder that is usually hidden in pairwise correlations. Here, we compare the covalent network topology of liquid and solidified silicon (Si) with that of silica (SiO2) on the basis of the analyses of the ring size and cavity distributions and tetrahedral order. We discover that the ring size distributions in amorphous (<italic>a</italic>)-Si are narrower and the cavity volume ratio is smaller than those in <italic>a</italic>-SiO2, which is a signature of poor amorphous-forming ability in <italic>a</italic>-Si. Moreover, a significant difference is found between the liquid topology of Si and that of SiO2. These topological features, which are reflected in diffraction patterns, explain why silica is an amorphous former, whereas it is impossible to prepare bulk <italic>a</italic>-Si. We conclude that the tetrahedral corner-sharing network of AX2, in which A is a fourfold cation and X is a twofold anion, as indicated by the first sharp diffraction peak, is an important motif for the amorphous-forming ability that can rule out <italic>a</italic>-Si as an amorphous former. This concept is consistent with the fact that an elemental material cannot form a bulk amorphous phase using melt quenching technique.

  18. Accelerated Discovery of Proton-Conducting Perovskite Oxide by Capturing Physicochemical Fundamentals of Hydration Peer-reviewed

    Junji Hyodo, Kota Tsujikawa, Motoki Shiga, Yuji Okuyama, Yoshihiro Yamazaki

    ACS ENERGY LETTERS　6　(8)　2985-2992　2021/08

    DOI： 10.1021/acsenergylett.1c01239 　

    ISSN： 2380-8195

  19. Dimensionality Reduction and Its Application to Spectral Analysis Invited Peer-reviewed

    26　(7)　434-442　2021

    Publisher:

    ISSN： 1341-688X

  20. Evaluation of prediction capability of proton concentration in perovskite oxides using machine learning

    20　(4)　80-91　2021

    Publisher:

    ISSN： 1346-6623

  21. Structure and properties of densified silica glass: characterizing the order within disorder

    Yohei Onodera, Shinji Kohara, Philip S. Salmon, Akihiko Hirata, Norimasa Nishiyama, Suguru Kitani, Anita Zeidler, Motoki Shiga, Atsunobu Masuno, Hiroyuki Inoue, Shuta Tahara, Annalisa Polidori, Henry E. Fischer, Tatsuya Mori, Seiji Kojima, Hitoshi Kawaji, Alexander I. Kolesnikov, Matthew B. Stone, Matthew G. Tucker, Marshall T. McDonnell, Alex C. Hannon, Yasuaki Hiraoka, Ippei Obayashi, Takenobu Nakamura, Jaakko Akola, Yasuhiro Fujii, Koji Ohara, Takashi Taniguchi, Osami Sakata

    NPG ASIA MATERIALS　12　(1)　2020/12

    DOI： 10.1038/s41427-020-00262-z 　

    ISSN： 1884-4049

    eISSN： 1884-4057

  22. Cost-effective search for lower-error region in material parameter space using multifidelity Gaussian process modeling Peer-reviewed

    Shion Takeno, Yuhki Tsukada, Hitoshi Fukuoka, Toshiyuki Koyama, Motoki Shiga, Masayuki Karasuyama

    PHYSICAL REVIEW MATERIALS　4　(8)　2020/08

    DOI： 10.1103/PhysRevMaterials.4.083802 　

    ISSN： 2475-9953

  23. Understanding Diffraction from Disordered Materials and the Extraction of Topology Hidden in the Pairwise Correlations by Persistent Homology

    Shinji KOHARA, Osami SAKATA, Yohei ONODERA, Ippei OBAYASHI, Motoki SHIGA, Akihiko HIRATA, Yasuaki HIRAOKA

    Nihon Kessho Gakkaishi　62　(1)　43-50　2020/02/29

    Publisher: The Crystallographic Society of Japan

    DOI： 10.5940/jcrsj.62.43 　

    ISSN： 0369-4585

    eISSN： 1884-5576

  24. Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its Parallelization.

    Shion Takeno, Hitoshi Fukuoka, Yuhki Tsukada, Toshiyuki Koyama, Motoki Shiga, Ichiro Takeuchi, Masayuki Karasuyama

    Proceedings of the 37th International Conference on Machine Learning(ICML)　9334-9345　2020

    Publisher: PMLR

  25. Understanding diffraction patterns of glassy, liquid and amorphous materials via persistent homology analyses Peer-reviewed

    Yohei Onodera, Shinji Kohara, Shuta Tahara, Atsunobu Masuno, Hiroyuki Inoue, Motoki Shiga, Akihiko Hirata, Koichi Tsuchiya, Yasuaki Hiraoka, Ippei Obayashi, Koji Ohara, Akitoshi Mizuno, Osami Sakata

    Journal of the Ceramic Society of Japan　127　(12)　853-863　2019/12

    DOI： 10.2109/jcersj2.19143 　

    ISSN： 1882-0743

    eISSN： 1348-6535

  26. Application of machine learning techniques to electron microscopic/spectroscopic image data analysis Peer-reviewed

    Shunsuke Muto, Motoki Shiga

    Microscopy　2019/11

  27. Estimation of material parameters based on precipitate shape: efficient identification of low-error region with Gaussian process modeling Peer-reviewed

    Yuhki Tsukada, Shion Takeno, Masayuki Karasuyama, Hitoshi Fukuoka, Motoki Shiga, Toshiyuki Koyama

    Scientific Reports　9　15794　2019/10

  28. Non-negative matrix factorization and its extensions for spectral image data analysis Peer-reviewed

    Motoki Shiga, Shunsuke Muto

    e-Journal of Surface Science and Nanotechnology　17　148-154　2019/09

  29. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen Peer-reviewed

    Michael Patrick Menden, Dennis Wang, Yuanfang Guan, Michael Mason, Bence Szalai, Krishna C Bulusu, Thomas Yu, Jaewoo Kang, Minji Jeon, Russ Wolfinger, Tin Nguyen, Mikhail Zaslavskiy, AstraZeneca-Sanger Drug Combination, DREAM Consortium, In Sock Jang, Zara Ghazoui, Mehmet Eren Ahsen, Robert Vogel, Elias Chaibub Neto, Thea Norman, Eric KY Tang, Mathew J Garnett, Giovanni Di Veroli, Stephen Fawell, Gustavo Stolovitzky, Justin Guinney, Jonathan R Dry, Julio Saez-Rodriguez

    Nature Communications　10　2674　2019/06

  30. TOF-SIMS Image Data Fusion by Multivariate Analysis and TOF-SIMS Spectrum Analysis by Sparse Modeling and Machine Learning Peer-reviewed

    Wataru Ishikura, Kazuma Takahashi, Takayuki Yamagishi, Dan Aoki, Kazuhiko Fukushima, Motoki Shiga, Satoka Aoyagi

    Journal of surface analysis　25　(2)　103-114　2018/12

    Publisher: Surface Analysis Society of Japan

    DOI： 10.1384/jsa.25.103 　

    ISSN： 1341-1756

    eISSN： 1347-8400

  31. A Crowdsourced Analysis to Identify ab Initio Molecular Signatures Predictive of Susceptibility to Viral Infection Peer-reviewed

    Slim Fourati, Aarthi Talla, Mehrad Mahmoudian, Joshua G Burkhart, Riku Klen, Ricardo Henao, Zafer Aydin, Ka Yee Yeung, Mehmet Eren Ahsen, Reem Almugbel, Samad Jahandideh, Xiao Liang, Torbjorn, E.M. Nordling, Motoki Shiga, Ana Stanescu, Robert Vogel, The Respiratory Viral, DREAM Challenge Consortium, Gaurav Pandey, Christopher Chiu, Micah T McClain, Chris W Woods, Geoffrey S Ginsburg, Laura L Elo, Ephraim L Tsalik, Lara M Mangravite, Solveig K Sieberts

    Nature Communications　9　4418　2018/10

    DOI： 10.1038/s41467-018-06735-8 　

  32. Informatics-Aided Raman Microscopy for Nanometric 3D Stress Characterization Peer-reviewed

    Hongxin Wang, Han Zhang, Bo Da, Motoki Shiga, Hideaki Kitazawa, Daisuke Fujita

    Journal of Physical Chemistry C　122　(13)　7187-7193　2018/04/05

    Publisher: American Chemical Society

    DOI： 10.1021/acs.jpcc.7b12415 　

    ISSN： 1932-7455 1932-7447

  33. Exploring a potential energy surface by machine learning for characterizing atomic transport Peer-reviewed

    Kanamori, Kenta, Toyoura, Kazuaki, Honda, Junya, Hattori, Kazuki, Seko, Atsuto, Karasuyama, Masayuki, Shitara, Kazuki, Shiga, Motoki, Kuwabara, Akihide, Takeuchi, Ichiro

    PHYSICAL REVIEW B　97　(12)　125124　2018/03

    DOI： 10.1103/PhysRevB.97.125124 　

    ISSN： 2469-9950

    eISSN： 2469-9969

  34. High spatial resolution hyperspectral imaging with machine-learning techniques Peer-reviewed

    Motoki Shiga, Shunsuke Muto

    Nanoinformatics　179-203　2018/01/15

    Publisher: Springer Singapore

    DOI： 10.1007/978-981-10-7617-6_9 　

  35. Time variations of the radial velocity of H2O masers in the semi-regular variable R Crt Peer-reviewed

    Hiroshi Sudou, Motoki Shiga, Toshihiro Omodaka, Chihiro Nakai, Kazuki Ueda, Hiroshi Takaba

    Journal of the Korean Astronomical Society　50　(6)　157-165　2017/12/01

    Publisher: Korean Astronomical Society

    DOI： 10.5303/JKAS.2017.50.6.157 　

    ISSN： 1225-4614

  36. Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data Peer-reviewed

    Justin Guinney, Tao Wang, Teemu D. Laajala, Kimberly Kanigel Winner, J. Christopher Bare, Elias Chaibub Neto, Suleiman A. Khan, Gopal Peddinti, Antti Airola, Tapio Pahikkala, Tuomas Mirtti, Thomas Yu, Brian M. Bot, Liji Shen, Kald Abdallah, Thea Norman, Stephen Friend, Gustavo Stolovitzky, Howard Soule, Christopher J. Sweeney, Charles J. Ryan, Howard I. Scher, Oliver Sartor, Yang Xie, Tero Aittokallio, Fang Liz Zhou, James C. Costello

    LANCET ONCOLOGY　18　(1)　132-142　2017/01

    DOI： 10.1016/S1470-2045(16)30560-5 　

    ISSN： 1470-2045

    eISSN： 1474-5488

  37. Matrix Factorization for Automatic Chemical Mapping from Electron Microscopic Spectral Imaging Datasets Peer-reviewed

    Motoki Shiga, Shunsuke Muto, Kazuyoshi Tatsumi, Koji Tsuda

    Transactions of the Materials Research Society of Japan　41　(4)　333-336　2016/12

    Publisher: The Materials Research Society of Japan

    DOI： 10.14723/tmrsj.41.333 　

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    <p>Advances in scanning transmission electron microscopy (STEM) techniques have enabled us to automatically obtain electron energy-loss (EELS)/energy-dispersive X-ray (EDX) spectral datasets from a specified region of interest (ROI) at an arbitrary step width, called spectral imaging (SI). Instead of manually identifying the potential constituent chemical components from the ROI, it is more effective and efficient to use a statistical approach for the automatic identification of the underlying chemical components and their spectra. This problem of automatic decomposition of chemical components can be formalized as a matrix factorization, which is a common problem setting in statistical machine learning. This paper first reviews several matrix factorization methods and then introduces our extension of a non-negative matrix factorization (NMF). The present NMF solves two problems: i) resolving overlapped spectral profiles, avoiding unnatural crosstalk, and ii) optimizing the number of chemical components. These effectiveness and comparisons with other matrix factorization methods are demonstrated using a real STEM-EELS dataset.</p>

  38. Sparse modeling of EELS and EDX spectral imaging data by nonnegative matrix factorization Peer-reviewed

    Motoki Shiga, Kazuyoshi Tatsumi, Shunsuke Muto, Koji Tsuda, Yuta Yamamoto, Toshiyuki Mori, Takayoshi Tanji

    ULTRAMICROSCOPY　170　43-59　2016/11

    DOI： 10.1016/j.ultramic.2016.08.006 　

    ISSN： 0304-3991

    eISSN： 1879-2723

  39. Machine-learning-based selective sampling procedure for identifying the low-energy region in a potential energy surface: A case study on proton conduction in oxides Peer-reviewed

    Kazuaki Toyoura, Daisuke Hirano, Atsuto Seko, Motoki Shiga, Akihide Kuwabara, Masayuki Karasuyama, Kazuki Shitara, Ichiro Takeuchi

    PHYSICAL REVIEW B　93　(5)　054112　2016/02

    DOI： 10.1103/PhysRevB.93.054112 　

    ISSN： 2469-9950

    eISSN： 2469-9969

  40. Application of Statistical/Information Processing to Spectral Image Data Analysis

    MUTO Shunsuke, SHIGA Motoki

    Hyomen Kagaku　37　(12)　610-615　2016

    Publisher: The Surface Science Society of Japan

    DOI： 10.1380/jsssj.37.610 　

    ISSN： 0388-5321

    More details Close

    <p>Recent development of digitized and automated measurement systems has inevitably accelerated applications of data mining techniques based on statistical/information processing. In the present article we outlined the recent progress in our chemical imaging techniques based on the statistical analysis of &lsquo;spectral image&rsquo; datasets, obtained by a suite of scanning transmission electron microscopy and associated spectroscopy. Finally, we discuss the future prospects of the field to extend the present bilinear model to multi-way analysis for more robust modeling of low signal-to-noise ratio data.</p>

  41. Estimating Proton Conductivity in Crystals by using Guassian Process and Dynamic Programming

    116　(300)　191-198　2016/01

    Publisher:

    ISSN： 0913-5685

  42. Two-step feature selection for predicting survival time of patients with metastatic castrate resistant prostate cancer Peer-reviewed

    Motoki Shiga

    F1000Research　5　2678　2016

    Publisher: Faculty of 1000 Ltd

    DOI： 10.12688/f1000research.8201.1 　

    ISSN： 1759-796X 2046-1402

  43. Direct conditional probability density estimation with sparse feature selection Peer-reviewed

    Motoki Shiga, Voot Tangkaratt, Masashi Sugiyama

    MACHINE LEARNING　100　(2-3)　161-182　2015/09

    DOI： 10.1007/s10994-014-5472-x 　

    ISSN： 0885-6125

    eISSN： 1573-0565

  44. ナノ電子顕微分光における情報処理技法の応用 Invited

    Shunsuke Muto, Motoki Shiga, Kazuyoshi Tatsumi, Koji Tsuda

    50　(7)　527-530　2015/07

    Publisher:

    ISSN： 0009-031X

  45. Non-Negative Matrix Factorization with Auxiliary Information on Overlapping Groups Peer-reviewed

    Motoki Shiga, Hiroshi Mamitsuka

    IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING　27　(6)　1615-1628　2015/06

    DOI： 10.1109/TKDE.2014.2373361 　

    ISSN： 1041-4347

    eISSN： 1558-2191

  46. Detecting Differentially Coexpressed Genes from Labeled Expression Data: A Brief Review Peer-reviewed

    Mitsunori Kayano, Motoki Shiga, Hiroshi Mamitsuka

    IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS　11　(1)　154-167　2014/01

    DOI： 10.1109/TCBB.2013.2297921 　

    ISSN： 1545-5963

    eISSN： 1557-9964

  47. Variational Bayes co-clustering with auxiliary information Peer-reviewed

    Motoki Shiga, Hiroshi Mamitsuka

    MultiClust 2013 - 4th Workshop on Multiple Clusterings, Multi-View Data, and Multi-Source Knowledge-Driven Clustering, in Conj. with the 19th ACM SIGKDD Int. Conf. on KDD 2013　5　1-4　2013

    DOI： 10.1145/2501006.2501012 　

  48. Compression of Topological Spectra (TFS) for Accelerating Chemical DataMining Peer-reviewed

    Motoki Shiga, Yoshimasa Takahashi

    Journal of Computer Chemistry, Japan　11　(2)　104-111　2012/08

    Publisher: Society of Computer Chemistry, Japan

    DOI： 10.2477/jccj.2012-0002 　

    ISSN： 1347-1767

    More details Close

    The chemical space of drug candidates is vast, and data volume in chemical databases is still getting larger. For mining such vast chemical data, accelerating data analysis is an essential issue. This paper validates an approach for improving computational cost by compressing topological fragment spectra (TFS) which is a descriptor of chemical graphs proposed by Takahashi et al.. First we show that TFS is a periodic signal whose cycle length is around 12 (mass number of carbon). And then we apply compression methods for periodic signals: Fourier transform and Wavelet transform. Experimental results on structural similarity searches and pharmaceutical activity predictions show that Wavelet transform gives more effective compression than Fourier transform.

  49. A Variational Bayesian Framework for Clustering with Multiple Graphs Peer-reviewed

    Motoki Shiga, Hiroshi Mamitsuka

    IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING　24　(4)　577-589　2012/04

    DOI： 10.1109/TKDE.2010.272 　

    ISSN： 1041-4347

  50. Efficient semi-supervised learning on locally informative multiple graphs Peer-reviewed

    Motoki Shiga, Hiroshi Mamitsuka

    PATTERN RECOGNITION　45　(3)　1035-1049　2012/03

    DOI： 10.1016/j.patcog.2011.08.020 　

    ISSN： 0031-3203

  51. Clustering genes with expression and beyond Invited Peer-reviewed

    Motoki Shiga, Hiroshi Mamitsuka

    WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY　1　(6)　496-511　2011/11

    DOI： 10.1002/widm.41 　

    ISSN： 1942-4787

    eISSN： 1942-4795

  52. Genome-Wide Integration on Transcription Factors, Histone Acetylation and Gene Expression Reveals Genes Co-Regulated by Histone Modification Patterns Peer-reviewed

    Yayoi Natsume-Kitatani, Motoki Shiga, Hiroshi Mamitsuka

    PLOS ONE　6　(7)　e22281　2011/07

    DOI： 10.1371/journal.pone.0022281 　

    ISSN： 1932-6203

  53. ROS-DET: robust detector of switching mechanisms in gene expression Peer-reviewed

    Mitsunori Kayano, Ichigaku Takigawa, Motoki Shiga, Koji Tsuda, Hiroshi Mamitsuka

    NUCLEIC ACIDS RESEARCH　39　(11)　e74　2011/06

    DOI： 10.1093/nar/gkr130 　

    ISSN： 0305-1048

    eISSN： 1362-4962

  54. A spectral approach to clustering numerical vectors as nodes in a network Peer-reviewed

    Motoki Shiga, Ichigaku Takigawa, Hiroshi Mamitsuka

    PATTERN RECOGNITION　44　(2)　236-251　2011/02

    DOI： 10.1016/j.patcog.2010.08.010 　

    ISSN： 0031-3203

    eISSN： 1873-5142

  55. Clustering for Comprehensive Genome Data Analysis

    Shiga Motoki

    Abstracts for Annual Meeting of Japanese Proteomics Society　2011　98-98　2011

    Publisher: Japanese Proteomics Society (Japan Human Proteome Organisation)

    DOI： 10.14889/jhupo.2011.0.98.0 　

  56. On the performance of methods for finding a switching mechanism in gene expression. Peer-reviewed

    Mitsunori Kayano, Ichigaku Takigawa, Motoki Shiga, Koji Tsuda, Hiroshi Mamitsuka

    Genome informatics. International Conference on Genome Informatics　24　69-83　2010/07

    Publisher: 1

    ISSN： 0919-9454

    More details Close

    We address an issue of detecting a switching mechanism in gene expression, where two genes are positively correlated for one experimental condition while they are negatively correlated for another. We compare the performance of existing methods for this issue, roughly divided into two types: interaction test (IT) and the difference of correlation coefficients. Interaction test, currently a standard approach for detecting epistasis in genetics, is the log-likelihood ratio test between two logistic regressions with/without an interaction term, resulting in checking the strength of interaction between two genes. On the other hand, two correlation coefficients can be computed for two experimental conditions and the difference of them shows the alteration of expression trends in a more straightforward manner. In our experiments, we tested three different types of correlation coefficients: Pearson, Spearman and a midcorrelation (biweight midcorrelation). The experiment was performed by using ~ 2.3 × 10(9) combinations selected out of the GEO (Gene Expression Omnibus) database. We sorted all combinations according to the p-values of IT or by the absolute values of the difference of correlation coefficients and then visually evaluated the top ranked combinations in terms of the switching mechanism. The result showed that 1) combinations detected by IT included non-switching combinations and 2) Pearson was affected by outliers easily while Spearman and the midcorrelation seemed likely to avoid them.

  57. Variational Bayes learning over multiple graphs Peer-reviewed

    Motoki Shiga, Hiroshi Mamitsuka

    Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010　166-171　2010

    DOI： 10.1109/MLSP.2010.5589257 　

  58. Annotating gene functions with integrative spectral clustering on microarray expressions and sequences. Peer-reviewed

    Limin Li, Motoki Shiga, Wai-ki Ching, Hiroshi Mamitsuka

    Genome informatics. International Conference on Genome Informatics　22　95-120　2010/01

    ISSN： 0919-9454

  59. Efficiently finding genome-wide three-way gene interactions from transcript- and genotype-data Peer-reviewed

    Mitsunori Kayano, Ichigaku Takigawa, Motoki Shiga, Koji Tsuda, Hiroshi Mamitsuka

    BIOINFORMATICS　25　(21)　2735-2743　2009/11

    DOI： 10.1093/bioinformatics/btp531 　

    ISSN： 1367-4803

    eISSN： 1460-2059

  60. Upper bound for variational free energy of Bayesian networks Peer-reviewed

    Kazuho Watanabe, Motoki Shiga, Sumio Watanabe

    Machine Learning　75　(2)　199-215　2009/05

    DOI： 10.1007/s10994-008-5099-x 　

    ISSN： 0885-6125 1573-0565

  61. Mining significant tree patterns in carbohydrate sugar chains Peer-reviewed

    Kosuke Hashimoto, Ichigaku Takigawa, Motoki Shiga, Minoru Kanehisa, Hiroshi Mamitsuka

    BIOINFORMATICS　24　(16)　I167-I173　2008/08

    DOI： 10.1093/bioinformatics/btn293 　

    ISSN： 1367-4803

  62. Clustering Analysis for Combining Multiple Genomic Data Peer-reviewed

    SHIGA Motoki, TAKIGAWA Ichigaku, MAMITSUKA Hiroshi

    Biophysics　48　(3)　190-194　2008/05

    Publisher: 日本生物物理学会

    DOI： 10.2142/biophys.48.190 　

    ISSN： 0582-4052 1347-4219

    eISSN： 1347-4219

  63. Annotating gene function by combining expression data with a modular gene network Peer-reviewed

    Motoki Shiga, Ichigaku Takigawa, Hiroshi Mamitsuka

    BIOINFORMATICS　23　(13)　I468-I478　2007/07

    DOI： 10.1093/bioinformatics/btm173 　

    ISSN： 1367-4803

    eISSN： 1460-2059

  64. A Spectral Clustering Approach to Optimally Combining Numerical Vectors with a Modular Network Peer-reviewed

    Motoki Shiga, Ichigaku Takigawa, Hiroshi Mamitsuka

    KDD-2007 PROCEEDINGS OF THE THIRTEENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING　647-656　2007

    DOI： 10.1145/1281192.1281262 　

  65. A Study on Entropy Estimation Accuracy for Discrete Information Sources

    Motoki Shiga

    Gifu University　2006/03

  66. Upper bounds for variational stochastic complexities of Bayesian networks Peer-reviewed

    Kazuho Watanabe, Motoki Shiga, Sumio Watanabe

    INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2006, PROCEEDINGS　4224　139-146　2006

    ISSN： 0302-9743

  67. An Optimal Entropy Estimator which is an Average over Plural Independent Estimates Peer-reviewed

    SHIGA Motoki, YOKOTA Yasunari

    IEEJ Transactions on Electronics, Information and Systems　125　(12)　1912-1913　2005/12

    Publisher: 社団法人 電気学会

    DOI： 10.1541/ieejeiss.125.1912 　

    ISSN： 0385-4221 1348-8155

    More details Close

    We cannot record biological signals, e.g., neural signals, over long time, because of biological tiredness and adaptation property. We often adopt the average over plural estimates obtained from samples recorded in different trials as a final estimate to improve the estimation accuracy. For minimizing mean squared error of the entropy estimation, we should optimally balance between bias error and mean squared error of individual estimations, however the optimal balance is unknown. This paper derivates the optimal balance between bias error and mean squared error of individual estimations.

  68. An Optimal Entropy Estimator for Discrete Random Variables Peer-reviewed

    Motoki Shiga, Yasunari Yokota

    Proc. of the 18th International Joint Conference on Neural Networks　1280-1285　2005/07

    DOI： 10.1109/IJCNN.2005.1556038 　

  69. Error Analysis of Entropy Estimator for A Memory-less Information Source Peer-reviewed

    Motoki Shiga, Yasunari Yokota

    Proc. of the International Workshop on Nonlinear Signal and Image Processing 2005　99-104　2005/05

  70. An Entropy Estimator That Minimizes Mean Squared Error under Condition of Restricted Averaged Squared Bias Error Peer-reviewed

    SHIGA Motoki, YOKOTA Yasunari

    The Transactions of the Institute of Electronics, Information and Communication Engineers. A　88　(4)　519-527　2005/04

    Publisher: 社団法人電子情報通信学会

    ISSN： 0913-5707

  71. Effect of time division on estimation accuracy in frequency domain ICA Peer-reviewed

    Y Yokota, H Iwata, M Shiga

    IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES　E87A　(12)　3424-3428　2004/12

    ISSN： 0916-8508

    eISSN： 1745-1337

  72. An entropy estimator improving mean squared error Peer-reviewed

    Y Yokota, M Shiga

    ELECTRONICS AND COMMUNICATIONS IN JAPAN PART III-FUNDAMENTAL ELECTRONIC SCIENCE　87　(9)　1-10　2004

    DOI： 10.1002/ecjc.10163 　

    ISSN： 1042-0967

  73. An Entropy Estimator Improving Mean Squared Error Peer-reviewed

    YOKOTA Yasunari, SHIGA Motoki

    The Transactions of the Institute of Electronics, Information and Communication Engineers. A　86　(9)　936-944　2003/09

    Publisher: 社団法人電子情報通信学会

    ISSN： 0913-5707

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Misc. 8

  1. Unravelling the density-driven modification of the topology generated (vol 132, pg 653, 2024)

    Shinji Kohara, Shuya Sato, Motoki Shiga, Yohei Onodera, Hirokazu Masai, Toru Wakihara, Atsunobu Masuno, Akihiko Hirata, Naoto Kitamura, Yasushi Idemoto, Koji Kimura, Koichi Hayashi

    JOURNAL OF THE CERAMIC SOCIETY OF JAPAN　133　(2)　65-65　2025/02

    DOI： 10.2109/jcersj2.2409302 　

    ISSN： 1882-0743

    eISSN： 1348-6535

  2. Conditional Density Estimation with Feature Selection

    SHIGA Motoki, SUGIYAMA Masashi

    IEICE technical report. Neurocomputing　113　(374)　17-22　2013/12/21

    Publisher: The Institute of Electronics, Information and Communication Engineers

    ISSN： 0913-5685

    More details Close

    On identification of the statistical dependency between inputs and outputs, an conditional density estimation is essential. The least-squares conditional density estimator (LS-CDE) proposed by Sugiyama et al. is more efficient and more applicable for more complex structures than regression models, which estimate the conditional mean of outputs. However, LS-CDE still suffers from large estimation error when many irrelevant features exist in inputs. In this paper, we propose extending LS-CDE to allow simultaneous feature selection during conditional density estimation. We evaluated our proposed method by numerical experiments.

  3. Direct Conditional Probability Density Estimation based on Sparse Additive Models

    SHIGA Motoki, SUGIYAMA Masashi

    113　(286)　53-60　2013/11/12

    Publisher: The Institute of Electronics, Information and Communication Engineers

    ISSN： 0913-5685

    More details Close

    On identification of the statistical dependency between inputs and outputs, an conditional density estimation is essential. The least-squares conditional density estimator (LS-CDE) proposed by Sugiyama et al. is more efficient and more applicable for more complex structures than regression models, which estimate the conditional mean of outputs. However, LS-CDE still suffers from large estimation error when many irrelevant features exist in inputs. In this paper, we propose extending LS-CDE to allow simultaneous feature selection during conditional density estimation and evaluate our proposed method by using numerical experiments.

  4. ロバスト相関係数差とP‐値による交互作用遺伝子対の効率的検出手法

    茅野光範, 瀧川一学, 志賀元紀, 津田宏治, 馬見塚拓

    統計関連学会連合大会講演報告集　2010　210　2010/09

  5. Finding three-way gene interactions from transcript and genotype data

    KAYANO Mitsunori, TAKIGAWA Ichigaku, SHIGA Motoki, TSUDA Koji, MAMITSUKA Hiroshi

    Proc Annu Conf Jpn Soc Bioinform　2010　P069.1-P069.2　2010

  6. Association of SNPs with Multiple Genes Using a Nonlinear Regression Model

    KAYANO Mitsunori, TAKIGAWA Ichigaku, SHIGA Motoki, TSUDA Koji, MAMITSUKA Hiroshi

    Proc Annu Conf Jpn Soc Bioinform　2008　P049.1-P049.2　2008

  7. Efficiently finding significant substructural patterns conserved in glycans

    TAKIGAWA Ichigaku, HASHIMOTO Kosuke, SHIGA Motoki, KANEHISA Minoru, MAMITSUKA Hiroshi

    Proc Annu Conf Jpn Soc Bioinform　2008　P066.1-P066.2　2008

  8. Rainfall Estimation Based on Maximizing Average Mutual Information between Rainfall and Radar Image

    SHIGA Motoki, YOKOTA Yasunari

    Proceedings of the IEICE General Conference　2001　(1)　266-267　2001/03/07

    Publisher: The Institute of Electronics, Information and Communication Engineers

Show all ︎Show first 5

Books and Other Publications 2

  1. Nanoinformatics

    Motoki Shiga, Shunsuke Muto

    Springer　2018/02

    ISBN: 9789811076176

  2. 脳神経システム解析のための数理アルゴリズム

    横田康成, 志賀元紀

    オーム社　2006

Presentations 43

  1. 物質・材料の構造解析のためのデータ科学技術 Invited

    志賀 元紀

    2024年度電気化学会九州支部シンポジウム（第63回工業物理化学講習会）「DX・先端技術を活用した研究開発」　2024/11/15

  2. ガラス材料に潜む構造秩序の定量解析法 Invited

    志賀 元紀

    第20回ガラス技術シンポジウム（GIC20）（第65回ガラスおよびフォトニクス材料討論会）　2024/11/07

  3. 物質・材料の計測データ解析のための機械学習 Invited

    志賀 元紀

    日本セラミックス協会 プレミアム講演会　2024/10/31

  4. 化学結合リングに基づくガラスと結晶の構造秩序解析 Invited

    志賀 元紀

    第3回Material Meets Data　2024/02/02

  5. Material structure analysis based on machine learning Invited

    Motoki Shiga

    The 9th CWRU x Tohoku Joint Workshop　2023/08/08

  6. 微細構造計測データ解析のための統計的機械学習 Invited

    志賀 元紀

    日本学術振興会ナノプローブテクノロジー第167委員会第106回研究会　2023/03/09

  7. 微細構造計測におけるデータ解析手法の開発および異分野間の連携 Invited

    志賀 元紀

    日本学術振興会R026先端計測技術の将来設計委員会 第11回研究会　2022/12/20

  8. 微細構造計測データ解析のための統計的機械学習 Invited

    志賀 元紀

    第37回分析電子顕微鏡討論会　2022/12/09

  9. 物質材料科学のための機械学習 Invited

    志賀 元紀

    2022年度ニューガラス大学院　2022/10/28

  10. 物質構造解析のためのインフォマティクス技術 Invited

    志賀 元紀

    第69回応用物理学会春季学術講演会, シンポジウム「応用物理におけるインフォマティクス応用の最前線」　2022/03/22

  11. 理論・実験・情報科学の融合によるガラスの構造秩序解析 Invited

    志賀 元紀

    第35回日本放射光学会年会・放射光科学合同シンポジウム, 企画講演「放射光を用いたガラス研究の最前線と未来」　2022/01/09

  12. スペクトルイメージング解析のための統計的機械学習 Invited

    志賀 元紀

    日本鉄鋼協会 材料の組織と特性部会 若手フォーラム 第4回研究会　2021/12/03

  13. 物質材料科学のための機械学習 Invited

    志賀 元紀

    2021年度 ニューガラス大学院　2021/10/29

  14. 微細構造計測データ解析のための統計的機械学習 Invited

    志賀 元紀

    第12回材料系ワークショップ〜マテリアルズインフォマティクスにおける「富岳」の活用に向けて〜　2021/10/06

  15. 化学結合リングに基づくガラス材料の多体相関解析 Invited

    志賀元紀

    TDA-MI workshop 2020　2020/11/14

  16. 化学結合トポロジーに基づく非晶質の多体相関解析 Invited

    志賀元紀, 平田秋彦, 小原真司, 小野寺陽平

    日本セラミックス協会第33回秋季シンポジウム　2020/09/03

  17. 微細構造計測データ解析のための統計的機械学習 Invited

    志賀元紀

    第３回計測インフォマティクス研究会　2019/09/18

  18. 微細構造計測データ解析のための統計的機械学習 Invited

    志賀元紀

    原子分解能ホログラフィー研究会・機能性材料ナノスケール原子相関合同研究会　2019/08/31

  19. オングストロームビーム電子回折イメージングを用いた非晶質構造の網羅的解析 Invited

    志賀元紀, 平田秋彦, 小原真司, 小野寺陽平

    NIMS先端計測シンポジウム2019　2019/03/07

  20. 物質構造計測へのデータ科学の導入 Invited

    志賀 元紀

    量子ビームを用いた回折実験・PDF解析・データ駆動型構造モデリング講習会（第3回RMCシミュレーションハンズオンチュートリアル）　2019/03/06

  21. Statistical machine learning for spectrum image data analysis International-presentation Invited

    Motoki Shiga

    JST PRESTO International Symposium on Materials Informatics　2019/02/11

  22. スペクトルイメージ解析のための統計的機械学習 Invited

    志賀 元紀

    プラスチック成形加工学会の第167回講演会　2019/01/26

  23. スペクトルイメージ解析のための統計的機械学習 Invited

    志賀 元紀

    日本顕微鏡学会「様々なイメージング技術研究部会」第6回研究会　2018/11/23

  24. Statistical Machine Learning for Spectrum Image Data Analysis International-presentation Invited

    Motoki Shiga

    The 19th KIM-JIM Symposium -Recent Advances in Artificial Intelligence and Simulations in Materials Science and Engineering-　2018/10/25

  25. Statistical Machine Learning for Microscopy Data Analysis International-presentation Invited

    Motoki Shiga

    14th International Conference on Atomically Controlled Surfaces, Interfaces and Nanostructures (ACSIN-14) and 26th International Colloquium on Scanning Probe Microscopy (ICSPM26)　2018/10/23

  26. 走査透過型電子顕微鏡データ解析のための機械学習法 Invited

    志賀 元紀

    応用物理学会秋季学術講演会特別シンポジウム（電子情報通信学会共催）「インフォマティクスへの招待」　2018/09/18

  27. 統計的機械学習を用いたスペクトルイメージ解析 Invited

    志賀 元紀

    顕微ナノ・表面科学・SPM合同シンポジウム　2018/03/26

  28. スペクトルデータ解析のための統計的機械学習 Invited

    志賀 元紀

    第二回 先端計測インフォマティクス・ワークショップ（NIMS先端計測シンポジウム 2018）　2018/03/08

  29. 統計的機械学習によるスペクトルイメージのモデリングと解析法 Invited

    志賀 元紀

    JSTさきがけ・マテリアルズインフォマティクス領域 第1回シンポジウム　2018/02/22

  30. スペクトラムイメージ解析のための統計的機械学習法 Invited

    志賀 元紀

    第31回日本放射光学会年会・放射光科学合同シンポジウム, 企画講演『情報科学を活用した放射光科学の新展開』　2018/01/08

  31. スペクトラムイメージ解析における機械学習 Invited

    志賀 元紀

    情報統合型物質・材料開発イニシアティブ チュートリアルセミナー 第6回「計測インフォマティクス」　2017/11/01

  32. スペクトルイメージデータ解析のための統計的機械学習法 Invited

    志賀元紀, 武藤俊介

    マイクロビームアナリシス第141委員会第169回研究会　2017/08/29

  33. 統計的機械学習に基づくスペクトラムイメージ解析 Invited

    志賀 元紀

    2017年真空・表面科学合同講演会・データ駆動表面科学研究部会セッション　2017/08/17

  34. 物質・材料の微細構造計測におけるインフォマティクス技術の開拓 Invited

    志賀 元紀

    第2回 さきがけ新分野開拓セミナー「ICTの展開」　2017/02/27

  35. 統計的機械学習に基づく走査型電子顕微鏡データ解析 Invited

    志賀 元紀

    先端計測インフォマティクス 大量データ時代の情報活用　2017/01/19

  36. 統計的機械学習による物質材料データ解析 Invited

    志賀 元紀

    日本金属学会・日本鉄鋼協会東海支部 若手材料研究会・技術交流フォーラム　2016/11/28

  37. スペクトルイメージング解析のための統計的機械学習 Invited

    志賀元紀

    第6回兵庫県マテリアルズ・インフォマティクス講演会　2021/08/20

  38. 物質・材料科学のための機械学習 Invited

    志賀元紀

    ガラスデータベースINTERGLAD第１回勉強会　2021/07/30

  39. Intermediate-Range Ordering in Glassy Materials Revealed by Statistical Analysis of Ring Characterizations Invited

    Motoki Shiga

    The 2nd International Workshop on Hyper-Ordered Structures　2021/06/26

  40. 分光スペクトル解析のための統計的機械学習 Invited

    志賀元紀, 武藤俊介

    日本顕微鏡学会 第77回学術講演会　2021/06/15

  41. 微細構造計測データ解析のための統計的機械学習 Invited

    志賀元紀

    近畿化学協会コンピュータ化学部会 公開講演会（第110回例会）　2021/06/01

  42. 化学結合トポロジーに基づくガラス構造の秩序解析 Invited

    志賀元紀

    多様な物質に潜む「超秩序構造」 〜構造物性研究の新展開〜, 日本物理学会第76回年次大会　2021/03/13

  43. スペクトラムイメージデータのノイズ処理と信号抽出の最近の進展 Invited

    志賀元紀

    顕微鏡計測インフォマティックス研究部会第2回研究会　2021/01/29

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Research Projects 30

  1. 材料デジタルツインで加速する磁性デバイス開発

    山崎 裕一, 志賀 元紀, 野村 光, 矢治 光一郎

    Offer Organization: 日本科学技術振興機構

    System: 戦略的創造研究推進事業 CREST

    2024/10 - 2030

  2. ナノ電子プローブ実・逆空間走査による統合データ駆動型材料物性解析

    武藤 俊介, 大塚 真弘, 齊藤 元貴, 志賀 元紀, 岡島 敏浩

    Offer Organization: 日本学術振興会

    System: 科学研究費助成事業 基盤研究(A)

    Category: 基盤研究(A)

    Institution: 名古屋大学

    2021/04/05 - 2025/03/31

  3. Supervision and research support based on a platform for hyper-ordered structures science

    Offer Organization: Japan Society for the Promotion of Science

    System: Grants-in-Aid for Scientific Research Grant-in-Aid for Transformative Research Areas (A)

    Category: Grant-in-Aid for Transformative Research Areas (A)

    Institution: Nagoya Institute of Technology

    2020/11/19 - 2025/03/31

  4. Comprehensive analysis of hyper-ordered structures based on mathematics and informatics

    Offer Organization: Japan Society for the Promotion of Science

    System: Grants-in-Aid for Scientific Research

    Category: Grant-in-Aid for Transformative Research Areas (A)

    Institution: Gifu University

    2020/11/19 - 2025/03/31

  5. High-order many body correlation analysis of network forming glass based on comprehensive integration of experimental, theoretical and data sciences

    Offer Organization: Japan Society for the Promotion of Science

    System: Grants-in-Aid for Scientific Research

    Category: Grant-in-Aid for Scientific Research (B)

    Institution: Gifu University

    2020/04/01 - 2023/03/31

  6. AIを導入したマテリアルズインフォマティクスによる巨大光吸収半導体の開発 Competitive

    藤原 裕之

    Offer Organization: 日本学術振興会

    System: 科学研究費補助金・基盤研究(B)

    2019/04 - 2023/03

  7. 物質・材料の微細構造計測におけるインフォマティクス技術の開拓 Competitive

    志賀 元紀

    Offer Organization: 国立研究開発法人 科学技術振興機構

    System: さきがけ・領域「理論・実験・計算科学とデータ科学が連携・融合した先進的マテリアルズインフォマティクスのための基盤技術の構築」

    2016/10 - 2020/03

  8. 複数データセットの効率的統合に基づく機械学習法 Competitive

    志賀元紀

    Offer Organization: 日本学術振興会

    System: 科学研究費助成事業

    Category: 基盤研究(B)

    2016/04 - 2020/03

  9. Statistical Machine Learning to combine multiple datasets Competitive

    Offer Organization: Japan Society for the Promotion of Science (JSPS)

    System: Grant-in-Aid for Scientific Research (B)

    2016/04 - 2020/03

  10. Machine Learning to combine multiple datasets Competitive

    SHIGA Motoki

    Offer Organization: Japan Society for the Promotion of Science

    System: Grant-in-Aid for Scientific Research (B)

    2016/04 - 2020/03

  11. Machine Learning for Materials Informatics Competitive

    Offer Organization: Japan Society for the Promotion of Science (JSPS)

    System: Grant-in-Aid for Scientific Research on Innovative Areas"Nano Informatics"

    2016/04 - 2018/03

  12. Machine Learning for Materials Informatics Competitive

    Koji Tsuda, Co-Investigator, Hisashi Kashima, Motoki Shiga

    Offer Organization: Ministry of Education, Culture, Sports, Science and Technology

    System: Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area)

    2016/04 - 2018/03

  13. 高次元データ解析における統計的機械学習法の開発 Competitive

    志賀元紀

    Offer Organization: 岐阜大学

    System: 活性化経費

    2016/06 - 2017/03

  14. Estimating data structure embedded in semi-structured data

    Mamitsuka Hiroshi, Takigawa Ichigaku, Hancock Timothy, Shiga Motoki, Tsuda Koji, Kayano Mitsunori, Nguyen Canh Hao

    Offer Organization: Japan Society for the Promotion of Science

    System: Grants-in-Aid for Scientific Research

    Category: Grant-in-Aid for Scientific Research (B)

    Institution: Kyoto University

    2012/04/01 - 2016/03/31

    More details Close

    The objective of this research is to build machine learning solutions for a variety of problems of semi-structured data, particularly graphs and networks. Particular problem focus was "label propagation" and "link prediction". We have not only built machine learning techniques but also applied our technique to real data, particularly those in life sciences.

  15. Biological Data Analysis Based on Statistical Machine Learning Approach Competitive

    Offer Organization: Institute for Chemical Research, Kyoto University

    System: Collaborative Research Grant

    2015/04 - 2016/03

  16. Genetalized Noise Model on Tensor Factorization with Auxiliary Information Competitive

    Offer Organization: Toyota Physical and Chemical Research Institute

    System: Toyota Physical and Chemical Research Institute Scholars

    2015/04 - 2016/03

  17. 補助情報を用いるテンソル因子化法における雑音モデルの一般化 Competitive

    志賀 元紀

    Offer Organization: 公益財団法人 豊田理化学研究所

    System: 豊田理研スカラー

    2015/04 - 2016/03

  18. Statistical Machine Learning for Efficient Screening on Material Design Competitive

    Offer Organization: Japan Society for the Promotion of Science (JSPS)

    System: Grant-in-Aid for Scientific Research on Innovative Areas"Nano Informatics"

    2014/04 - 2016/03

  19. Statistical Machine Learning with Heterogeneous Auxiliary Information Competitive

    Offer Organization: Ministry of Education, Culture, Sports, Science and Technology

    System: Grant-in-Aid for Young Scientists (B)

    2013/04 - 2016/03

  20. Data Mining for Gene Regulatory Mechanisms Competitive

    Offer Organization: Medical Institute of Bioregulation, Kyushu University

    System: Collaborative Research Grant

    2014/04 - 2015/03

  21. Bioinformatics with Auxiliary Biological Knowledge Competitive

    Offer Organization: Institute for Chemical Research, Kyoto University

    System: Collaborative Research Grant

    2014/04 - 2015/03

  22. Statistical Machine Learning with Heterogeneous Auxiliary Information Competitive

    志賀 元紀

    Offer Organization: Ministry of Education, Culture, Sports, Science and Technology

    System: Grants-in-Aid for Scientific Research(若手研究(B))

    Category: 若手研究(B)

    2013/04 - 2015/03

  23. 材料設計における効率的スクリーニングのための機械学習法 Competitive

    志賀 元紀

    Offer Organization: 文部科学省

    System: 科学研究費補助金(新学術領域研究(研究領域提案型))

    Category: 新学術領域研究(研究領域提案型)

    2014 - 2015

  24. Data Mining for Gene Regulatory Mechanisms Competitive

    Offer Organization: Medical Institute of Bioregulation, Kyushu University

    System: Collaborative Research Grant

    2013/04 - 2014/03

  25. Co-clustering of Biological Datasets for Personalized Medicine Competitive

    Offer Organization: Institute for Chemical Research, Kyoto University

    System: Collaborative Research Grant

    2013/04 - 2014/03

  26. Co-Clustering for Heterogeneous Simultaneous Measuring Datasets Competitive

    Offer Organization: The Okawa Foundation for Information and Telecommunications

    System: 2012 Research Grant, Artificial intelligence

    2013/03 - 2014/03

  27. Co-Clustering for Heterogeneous Simultaneous Measuring Datasets Competitive

    SHIGA Motoki

    Offer Organization: The Okawa Foundation for Information and Telecommunications

    System: 2012 Research Grant, Artificial intelligence

    2013/03 - 2014/03

  28. 生命科学上の非構造化データの統合マイニング Competitive

    分担(代表, 馬見塚拓

    Offer Organization: 科学技術振興機構 バイオインフォマティクス推進センター

    System: 創造的な生物・情報知識融合型の研究開発

    2007/10 - 2010/09

  29. Integrative Data Mining for Analyzing Biological Networks Competitive

    SHIGA Motoki

    Offer Organization: Ministry of Education, Culture, Sports, Science and Technology

    System: Grant-in-Aid for Young Scientists (B)

    Category: Grant-in-Aid for Young Scientists (B)

    Institution: Kyoto University

    2008/04 - 2010/03

    More details Close

    For analyzing heterogeneous biological networks and related information sources, I developed integrative data mining methods. My developed new methods are clustering nodes on multiple networks, an annotation method of gene functions, and fast mining methods by combining frequent pattern mining and a statistical hypothesis test.

  30. Integrative data mining for analyzing biological networks Competitive

    Motoki SHIGA

    Offer Organization: Ministry of Education, Culture, Sports, Science and Technology

    System: Grants-in-Aid for Scientific Research(若手研究(B))

    Category: 若手研究(B)

    Institution: Kyoto University

    2008 - 2009

    More details Close

    For analyzing heterogeneous biological networks and related information sources, I developed integrative data mining methods. My developed new methods are clustering nodes on multiple networks, an annotation method of gene functions, and fast mining methods by combining frequent pattern mining and a statistical hypothesis test.

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Teaching Experience 9

  1. 機械学習　岐阜大学工学部

  2. データ解析特論　岐阜大学大学院工学研究科

  3. データマイニング特論　岐阜大学大学院自然科学技術研究科

  4. 情報工学セミナー（情報コース・応用情報学科）　工学部（昼）

  5. 技術と技術者の倫理Ⅱ（電気電子・情報工学科）　工学部（昼）

  6. 確率統計応用（確率統計Ⅱ）　岐阜大学工学部

  7. 初年次セミナー　全学共通教育

  8. 応用情報セミナーⅡ　岐阜大学工学部

  9. 応用数理解析　岐阜大学大学院工学研究科

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Social Activities 4

  1. リサーチフェロー

    2018/04/01 - 2021/03/31

  2. ◎研究室から大学はいま：「機械学習」でAIを賢く

    マスコミ報道

    2019/04/02 - 2019/04/02

  3. 客員研究員

    2018/05/01 - 2019/03/31

  4. 非常勤講師

    非常勤講師

    2018/04/01 - 2019/03/31