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- MOTOTAKE Yoh-ichi
- Graduate School of Social Data Science
- MOTOTAKE Yoh-ichi
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Papers
| 1. | Procedure to reveal the mechanism of pattern formation process by topological data analysis (Peer-reviewed) Yoh-ichi Mototake, Masaichiro Mizumaki, Kazue Kudo, Kenji Fukumizu Physica D: Nonlinear Phenomena Vol.470,pp.134359 2024.12 
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| 2. | Autoregressive with Slack Time Series Model for Forecasting a Partially-Observed Dynamical Time Series (Peer-reviewed) A. Okuno, Y. Morishita, Y. Mototake IEEE Access 2024.2
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| 3. | Search for high-creep-strength welding conditions considering HAZ shape factors for 2 1/4Cr-1Mo steel (Peer-reviewed) Hitoshi IZUNO, Masahiko Demura, Masayoshi Yamazaki, Satoshi Minamoto, Junya Sakurai, Kenji Nagata, Yoh-ichi Mototake, Daisuke Abe, Keisuke Torigata Welding in the World 2024.2 |
| 4. | Revealing the Mechanism of Large-scale Gradient Systems Using a Neural Reduced Potential (Peer-reviewed) Shunya Tsuji, Ryo Murakami, Hayaru Shouno, Yoh-ichi Mototake NeurIPS 2023 Workshop: Machine Learning and the Physical Sciences 2023.12 |
| 5. | Extracting Nonlinear Symmetries From Trained Neural Networks on Dynamics Data (Peer-reviewed) Yoh-ichi Mototake NeurIPS 2023 Workshop: AI for Science from Theory to Practice 2023.12 |
| 6. | Quantification of Galaxy Distribution with Topological Data Analysis and Detection of the Baryon Acoustic Oscillation (Peer-reviewed) Tsutomu T. Takeuchi, Kai T. Kono, Suchetha Cooray, Atsushi J. Nishizawa, Koya Murakami, Hai-Xia Ma, Yoh-Ichi Mototake Proceedings of the Institute of Statistical Mathematics Vol.71,No.2,pp.159-187 2023.12 |
| 7. | Quantifying physical insights cooperatively with exhaustive search for Bayesian spectroscopy of X-ray photoelectron spectra (Peer-reviewed) Hiroyuki Kumazoe, Kazunori Iwamitsu, Masaki Imamura, Kazutoshi Takahashi, Yoh-ichi Mototake, Masato Okada, Ichiro Akai Scientific Reports Vol.13,No.1 2023.8
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| 8. | Quantitative prediction of fracture toughness (<i>K</i><sub>I<i>c</i></sub>) of polymer by fractography using deep neural networks (Peer-reviewed) Y. Mototake, K. Ito, M. Demura Science and Technology of Advanced Materials: Methods Vol.2,No.1,pp.310-321 2022.9
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| 9. | Interpretable conservation law estimation by deriving the symmetries of dynamics from trained deep neural networks (Peer-reviewed) Yoh-ichi Mototake Physical Review E Vol.103,pp.033303- 2021.3 |
| 10. | Comparison of neuronal responses in primate inferior-temporal cortex and feed-forward deep neural network model with regard to information processing of faces (Peer-reviewed) Narihisa Matsumoto, Yoh-ichi Mototake, Kenji Kawano, Masato Okada, Yasuko Sugase-Miyamoto Journal of Computational Neuroscience 2021.2
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| 11. | Free Energy Estimation of Metastable Structures of Block Copolymers using Topological Data Analysis (Peer-reviewed) Yoh-ichi Mototake, Sadato Yamanaka, Takeshi Aoyagi, Takaaki Ohnishi, Kenji Fukumizu Journal of Computer Chemistry Japan Vol.19,No.4,pp.169-171 2021 |
| 12. | Topological Data Analysis of Domain Pattern Formation in Materials (Peer-reviewed) 本武陽一, 水牧仁一朗, 工藤和恵, 福水健次 スマートプロセス学会誌 Vol.10,No.3,pp.108-119 2021
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| 13. | A universal Bayesian inference framework for complicated creep constitutive equations (Peer-reviewed) Yoh-ichi Mototake, Hitoshi Izuno, Kenji Nagata, Masahiko Demura, Masato Okada Scientific Reports 2020.6 |
| 14. | Development of spectral decomposition based on Bayesian information criterion with estimation of confidence interval (Peer-reviewed) Hiroshi Shinotsuka, Hideki Yoshikawa, Yoh-ichi Mototake, Hayaru Shouno, Masato Okada, Kenji Nagata Science and Technology of Advanced Materials 2020.6 |
| 15. | Topological Data Analysis for microdomain patternsof Block Copolymer (Peer-reviewed) Yoh-ichi Mototake, Sadato Yamanaka, Takeshi Aoyagi, Takaaki Ohnishi, Kenji Fukumizu Proceedings of the 2020 International Symposium on Nonlinear Theory and its Applications 2020 |
| 16. | Revealing the existence of the ontological commitment in fish schools (Peer-reviewed) Takayuki Niizato, Kotaro Sakamoto, Yoh-ichi Mototake, Hisashi Murakami, Takenori Tomaru, Tomotaro Hoshika, Toshiki Fukushima Artif Life Robotics Vol.25,pp.633-633–642 2020
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| 17. | Towards a Geometrical Understanding of Physical Phenomena via Extraction of Data Manifolds using Generative Models (Peer-reviewed) Kotaro Sakamoto, Yuichiro Mori, Yoh-ichi Mototake Proceedings of the 2020 International Symposium on Nonlinear Theory and its Applications 2020 |
| 18. | Finding continuity and discontinuity in fish schools via integrated information theory. (Peer-reviewed) Takayuki Niizato, Kotaro Sakamoto, Yoh-Ichi Mototake, Hisashi Murakami, Takenori Tomaru, Tomotaro Hoshika, Toshiki Fukushima PloS one Vol.15,No.2,pp.e0229573- 2020
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| 19. | Four-types of IIT-induced group integrity of Plecoglossus altivelis (Peer-reviewed) Takayuki Niizato, Kotaro Sakamoto, Yoh-ichi Mototake, Hisashi Murakami, Yuta Nishiyama, Toshiki Fukushima Entropy Vol.22,No.7,pp.726 2020
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| 20. | Data-based selection of creep constitutive models for high-Cr heat-resistant steel (Peer-reviewed) IZUNO, Hitoshi, Demura, Masahiko, TABUCHI, Masaaki, Mototake, Yoh-ichi, Okada, Masato Science and Technology of Advanced Materials Vol.21,No.1,pp.219-228 2020 |
| 21. | Universal Framework of Bayesian Creep Model Selection for Steel, (Peer-reviewed) Yoh-ichi Mototake, Hitoshi Izuno, Kenji Nagata, Masahiko Demura, Masato Okada Materials Research Meeting 2019 2019.12 |
| 22. | Universal Framework of Bayesian Creep Model Selection for Steel, (Peer-reviewed) Yoh-ichi Mototake, Hitoshi Izuno, Kenji Nagata, Masahiko Demura, Masato Okada International Conference on Computational & Experimental Engineering and Sciences 2019.3 |
| 23. | Descriptor Extraction on Inherent Creep Strength of Carbon Steels by Exhaustive Search (Peer-reviewed) Junya Sakurai, Junya Inoue, Masahiko Demura, Yoichi Mototake, Masato Okada, Masayoshi Yamazaki International Conference on Computational & Experimental Engineering and Sciences 2019 |
| 24. | Conservation Law Estimation by Extracting the Symmetry of a Dynamical System Using a DNN (Peer-reviewed) Yoh-ichi Mototake NeurIPS2019 Workshop on Machine Learning and the Physical Sciences(ML4PS) 2019 |
| 25. | Semi-flat minima and saddle points by embedding neural networks to overparameterization (Peer-reviewed) Kenji Fukumizu, Shoichiro Yamaguch, Yoh-ichi Mototake, Mirai Tanaka Advances in Neural Information Processing Systems (NeurIPS) 2019 |
| 26. | Bayesian Hamiltonian Selection in X-ray Photoelectron Spectroscopy (Peer-reviewed) Yoh-ichi Mototake, Masaichiro Mizumaki, Ichiro Akai, Masato Okada Journal of the Physical Society of Japan Vol.88,No.3 2019
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| 27. | Bayesian Spectral Deconvolution Based on Poisson Distribution: Bayesian Measurement and Virtual Measurement Analytics (VMA) (Peer-reviewed) Kenji Nagata, Yoh-ichi Mototake, Rei Muraoka, Takehiko Sasaki, Masato Okada Journal of the Physical Society of Japan Vol.88,No.4,pp.044003 2019
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| 28. | Creep Model Selection for Grade 91 Steel Using Data Scientific Method (Peer-reviewed) Hitoshi Izuno, Masahiko Demura, Masaaki Tabuchi, Yohichi Mototake, Masato Okada International Conference on Computational & Experimental Engineering and Sciences 2019 |
| 29. | レプリカ交換モンテカルロ法を用いたMixture of Experts モデルにおけるベイズ推論 (Peer-reviewed) 松平京介, 永田賢二, 本武陽一, 岡田真人 情報処理学会論文誌数理モデル化と応用 (TOM) 2019 |
| 30. | Heap Paradox in Fish Schools (Peer-reviewed) Takayuki Niizato, Kotaro Sakamoto, Yoh-Ichi Mototake, Hisashi Murakami, Yuta Nishiyama, Toshiki Fukushima SWARM2019 2019 |
| 31. | Spectral deconvolution through bayesian LARS-OLS (Peer-reviewed) Yoh-ichi Mototake, Yasuhiko Igarashi, Hikaru Takenaka, Kenji Nagata, Masato Okada Journal of the Physical Society of Japan Vol.87,No.11 2018 |
| 32. | Life as an emergent phenomenon: studies from a large-scale boid simulation and web data (Peer-reviewed) Takashi Ikegami, Yoh-ichi Mototake, Shintaro Kobori, Mizuki Oka, Yasuhiro Hashimoto PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES Vol.375,No.2109 2017.12
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| 33. | Revisiting Classification of Large Scale Flocking (Peer-reviewed) Norihiro Maruyama, Yasuhiro Hashimoto, Yhoichi Mototake, Daichi Saito, Takashi Ikegami The 2nd International Symposium on Swarm Behavior and Bio-Inspired Robotics 2017 |
| 34. | Creating an in-group relation between humans and agents (Peer-reviewed) Yoh Ichi Mototake, Haruaki Fukuda, Kazuhiro Ueda Transactions of the Japanese Society for Artificial Intelligence Vol.31,No.6,pp.AI30-J_1-10- 2016
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| 35. | The dynamics of deep neural networks (Peer-reviewed) Yhoichi Mototake, Takashi Ikegami the Twentieth International Symposium on Artificial Life and Robotics 2015 |
| 36. | A Simulation Study of Large Scale Swarms (Peer-reviewed) Yhoichi Mototake, Takashi Ikegami The 1st International Symposium on Swarm Behavior and Bio-Inspired Robotics 2015 2015 |
Misc.
| 1. | 位相的データ分析による強磁性体磁区パターン形成過程の分析
本武陽一, 水牧仁一朗, 工藤和恵, 福水健次 日本放射光学会年会・放射光科学合同シンポジウム(Web) Vol.34th 2021 |
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| 2. | 位相的データ分析法による材料構造形成過程の分析 (Peer-reviewed) 本武陽一, 水牧仁一朗, 工藤和恵, 福水健次 スマートプロセス学会誌 Vol.10,No.3 2021 |
| 3. | X線光電子分光におけるベイズ推論によるハミルトニアン選択
本武陽一, 水牧仁一朗, 赤井一郎, 岡田真人 日本物理学会講演概要集(CD-ROM) Vol.74,No.1 2019 |
| 4. | Detecting Large Scale Flocking and Development of Spatio-Temporal Patterns
丸山 典宏, 橋本 康弘, 本武 陽一, 斉藤 大地, 池上 高志 システム制御情報学会研究発表講演会講演論文集 Vol.62,pp.3p- 2018.5
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| 5. | 脳情報科学が拓くAIとICT：2．脳情報科学と人工知能 -ネオコグニトロンからDeep Learningへ-
本武 陽一, 庄野逸, 田村弘, 岡田真人 情報処理 Vol.59,No.1,pp.42-47 2017 |
| 6. | Analysis of Multimodal Deep Neural Networks : Towards the Elucidation of The Modality Integration Mechanism
本武 陽一, 池上 高志 電子情報通信学会技術研究報告 = IEICE technical report : 信学技報 Vol.116,No.300,pp.369-373 2016.11
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Presentations
| No. | Name of subject/Conference Name | Year | Site |
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| 1. | 物理学者と学習機械の効果的な協業に向けて：学習済み深層ニューラルネットワークからの解釈可能な物理法則抽出(ディープラーニングと物理学2020 オンライン) |
Holding date :
Presentation date : 2020.7.30 |
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| 2. | 第2回日米独先端科学(JAGFoS)シンポジウム(第2回日米独先端科学(JAGFoS)シンポジウム) |
Holding date :
Presentation date : 2019.10 |
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| 3. | The 4th Workshop on Self-Organization and Robustness of Evolving Many-Body Systems(The 4th Workshop on Self-Organization and Robustness of Evolving Many-Body Systems) |
Holding date :
Presentation date : 2019.3 |
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| 4. | 第2回教育・コミュニケーションロボット研究開発シンポジウム(第2回教育・コミュニケーションロボット研究開発シンポジウム) |
Holding date :
Presentation date : 2018 |
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| 5. | 神経回路学会時限研究会「ニューラルネットの温故知新」(神経回路学会時限研究会「ニューラルネットの温故知新」) |
Holding date :
Presentation date : 2016 |
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| 6. | 第11回全脳アーキテクチャ勉強会(第11回全脳アーキテクチャ勉強会) |
Holding date :
Presentation date : 2015 |
Awards
| No. | Award name | Year |
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| 1. | The 3rd International Symposium on Swarm Behavior and Bio-Inspired Robotics, Best Paper Award Finalists | 2019 |
| 2. | 脳学際研究部門第1回公開シンポジウム 最優秀発表賞 | 2017 |
| 3. | 2016 Annual Conference Award | 2016 |
| 4. | The First International Symposium on Swarm Behavior and Bio-Inspired Robotics, Best Student Paper Award Finalists | 2015 |
Research Projects
| No. | Research subject | Research item(Awarding organization, System name) | Year |
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| 1. | Machine-Learning-Reinforced Cosmic Structure Formation: From Large-Scale Structure Formation to Galaxy Evolution
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Grant-in-Aid for Scientific Research (A)
( Awarding organization: Japan Society for the Promotion of Science System name: Grants-in-Aid for Scientific Research ) |
2024.4 - 2029.3 |
| 2. | 革新的セラミック材料設計のための材料パターン情報学の創成
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( Awarding organization: 国立研究開発法人 新エネルギー・産業技術総合開発機構（NEDO） System name: 未踏チャレンジ２０５０ ) |
2022.8 - 2025.7 |
| 3. | Development of machine learning methods for discovering symmetries in pattern dynamics
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Grant-in-Aid for Early-Career Scientists
( Awarding organization: Japan Society for the Promotion of Science System name: Grants-in-Aid for Scientific Research Grant-in-Aid for Early-Career Scientists ) |
2022.4 - 2027.3 |
| 4. | 解釈可能AIによるパターンダイナミクスの数理構造抽出と材料情報学への応用
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さきがけ
( Awarding organization: 国立研究開発法人科学技術振興機構 System name: 戦略的創造研究推進事業（さきがけ） ) |
2021.10 - 2025.3 |
| 5. | Constructing a reduced model of a pattern formation process on the basis of topological data analysis
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Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area)
( Awarding organization: Japan Society for the Promotion of Science System name: Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area) ) |
2020.4 - 2022.3 |
| 6. | 代数幾何的学習理論の物理データ分析への応用手法の検討
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一般研究２
( Awarding organization: 統計数理研究所 System name: 統計数理研究所共同利用 ) |
2020.4 - 2021.3 |
| 7. | TDAによる強磁性体磁区パターン形成過程の分析
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一般研究２
( Awarding organization: 統計数理研究所 System name: 統計数理研究所共同利用 ) |
2020.4 - 2021.3 |
