Kyushu University Academic Staff Educational and Research Activities Database
List of Papers
Kazuki Yoshizoe Last modified date:2023.05.29

Professor / Section of Advanced Computational Science / Research Institute for Information Technology

1. Xiufeng Yang, Tanuj Kr Aasawat, Kazuki Yoshizoe, Practical Massively Parallel Monte-Carlo Tree Search Applied to Molecular Design., The Ninth International Conference on Learning Representations (ICLR2021), 2021.05.
2. Yuta Takahashi, Kazuki Yoshizoe, Masao Ueki, Gen Tamiya, Yu Zhiqian, Yusuke Utsumi, Atsushi Sakuma, Koji Tsuda, Atsushi Hozawa, Ichiro Tsuji, Hiroaki Tomita, Machine learning to reveal hidden risk combinations for the trajectory of posttraumatic stress disorder symptoms, Scientific Reports, 10.1038/s41598-020-78966-z, 10, 1, 2020.12, AbstractThe nature of the recovery process of posttraumatic stress disorder (PTSD) symptoms is multifactorial. The Massive Parallel Limitless-Arity Multiple-testing Procedure (MP-LAMP), which was developed to detect significant combinational risk factors comprehensively, was utilized to reveal hidden combinational risk factors to explain the long-term trajectory of the PTSD symptoms. In 624 population-based subjects severely affected by the Great East Japan Earthquake, 61 potential risk factors encompassing sociodemographics, lifestyle, and traumatic experiences were analyzed by MP-LAMP regarding combinational associations with the trajectory of PTSD symptoms, as evaluated by the Impact of Event Scale-Revised score after eight years adjusted by the baseline score. The comprehensive combinational analysis detected 56 significant combinational risk factors, including 15 independent variables, although the conventional bivariate analysis between single risk factors and the trajectory detected no significant risk factors. The strongest association was observed with the combination of short resting time, short walking time, unemployment, and evacuation without preparation (adjusted P value = 2.2 × 10−4, and raw P value = 3.1 × 10−9). Although short resting time had no association with the poor trajectory, it had a significant interaction with short walking time (P value = 1.2 × 10−3), which was further strengthened by the other two components (P value = 9.7 × 10−5). Likewise, components that were not associated with a poor trajectory in bivariate analysis were included in every observed significant risk combination due to their interactions with other components. Comprehensive combination detection by MP-LAMP is essential for explaining multifactorial psychiatric symptoms by revealing the hidden combinations of risk factors..
3. Tanuj Aasawat, Tahsin Reza, Kazuki Yoshizoe, Matei Ripeanu, HyGN: Hybrid Graph Engine for NUMA, 2020 IEEE International Conference on Big Data (Big Data), 10.1109/bigdata50022.2020.9378430, 2020.12.
4. Makoto Chikaraishi, Prateek Garg, Varun Varghese, Kazuki Yoshizoe, Junji Urata, Yasuhiro Shiomi, Ryuki Watanabe, On the possibility of short-term traffic prediction during disaster with machine learning approaches: An exploratory analysis, Transport Policy, 10.1016/j.tranpol.2020.05.023, 98, 91-104, 2020.11.
5. Ryuichiro Hataya, Jan Zdenek, Kazuki Yoshizoe, Hideki Nakayama, Faster AutoAugment: Learning Augmentation Strategies Using Backpropagation, Computer Vision – ECCV 2020, 10.1007/978-3-030-58595-2_1, 1-16, 2020.11.
6. Ryosuke Shibukawa, Shoichi Ishida, Kazuki Yoshizoe, Kunihiro Wasa, Kiyosei Takasu, Yasushi Okuno, Kei Terayama, Koji Tsuda, CompRet: a comprehensive recommendation framework for chemical synthesis planning with algorithmic enumeration, Journal of Cheminformatics, 10.1186/s13321-020-00452-5, 12, 1, 52, 2020.09, AbstractIn computer-assisted synthesis planning (CASP) programs, providing as many chemical synthetic routes as possible is essential for considering optimal and alternative routes in a chemical reaction network. As the majority of CASP programs have been designed to provide one or a few optimal routes, it is likely that the desired one will not be included. To avoid this, an exact algorithm that lists possible synthetic routes within the chemical reaction network is required, alongside a recommendation of synthetic routes that meet specified criteria based on the chemist’s objectives. Herein, we propose a chemical-reaction-network-based synthetic route recommendation framework called “CompRet” with a mathematically guaranteed enumeration algorithm. In a preliminary experiment, CompRet was shown to successfully provide alternative routes for a known antihistaminic drug, cetirizine. CompRet is expected to promote desirable enumeration-based chemical synthesis searches and aid the development of an interactive CASP framework for chemists..
7. Jinzhe Zhang, Kei Terayama, Masato Sumita, Kazuki Yoshizoe, Kengo Ito, Jun Kikuchi, Koji Tsuda, NMR-TS: de novo molecule identification from NMR spectra, Science and Technology of Advanced Materials, 10.1080/14686996.2020.1793382, 21, 1, 552-561, 2020.07.
8. Faster AutoAugment: Learning Augmentation Strategies Using Backpropagation..
9. Kazuki Yoshizoe, Aika Terada, Koji Tsuda, MP-LAMP: parallel detection of statistically significant multi-loci markers on cloud platforms, Bioinformatics, 10.1093/bioinformatics/bty219, 34, 17, 3047-3049, 2018.09.
10. Using local minima to accelerate Krawczyk-Hansen global optimization.
11. Shota Izumi, Daisuke Ishii, Kazuki Yoshizoe, An Extended GLB Library for Optimization Problems, 2018.01.
12. Xiufeng Yang, Jinzhe Zhang, Kazuki Yoshizoe, Kei Terayama, Koji Tsuda, ChemTS: an efficient python library for de novo molecular generation, Science and Technology of Advanced Materials, 10.1080/14686996.2017.1401424, 18, 1, 972-976, 2017.12.
13. Thaer M. Dieb, Shenghong Ju, Kazuki Yoshizoe, Zhufeng Hou, Junichiro Shiomi, Koji Tsuda, MDTS: automatic complex materials design using Monte Carlo tree search, Science and Technology of Advanced Materials, 10.1080/14686996.2017.1344083, 18, 1, 498-503, 2017.12.
14. Xiufeng Yang, Kazuki Yoshizoe, Akito Taneda, Koji Tsuda, RNA inverse folding using Monte Carlo tree search, BMC Bioinformatics, 10.1186/s12859-017-1882-7, 18, 1, 2017.12.
15. Optimization of Playout Policy Integrated with Monte-Carlo Tree Search.
16. Daisuke Ishii, Kazuki Yoshizoe, Toyotaro Suzumura, Scalable parallel numerical constraint solver using global load balancing, Proceedings of the ACM SIGPLAN Workshop on X10, 10.1145/2771774.2771776, 2015.06.
17. Daisuke Ishii, Kazuki Yoshizoe, Toyotaro Suzumura, Scalable Parallel Numerical CSP Solver, Lecture Notes in Computer Science, 10.1007/978-3-319-10428-7_30, 398-406, 2014.09.
18. Humanlike AI for Mario Bros. Based on Evolutionary Computation and UCT.
19. Junichi Hashimoto, Akihiro Kishimoto, Kazuki Yoshizoe, Kokolo Ikeda, Accelerated UCT and Its Application to Two-Player Games, Lecture Notes in Computer Science, 10.1007/978-3-642-31866-5_1, 1-12, 2012.01.
20. Kazuki Yoshizoe, Akihiro Kishimoto, Tomoyuki Kaneko, Haruhiro Yoshimoto, Yutaka Ishikawa, Scalable Distributed Monte Carlo Tree Search, Proceedings of The Fourth Annual Symposium on Combinatorial Search (SoCS2011), 4, 180-187, 2011.07.
21. Kenichi Koizumi, Mary Inaba, Kei Hiraki, Yasuo Ishii, Takefumi Miyoshi, Kazuki Yoshizoe, Triple Line-Based Playout for Go - An Accelerator for Monte Carlo Go, 2009 International Conference on Reconfigurable Computing and FPGAs, 10.1109/reconfig.2009.75, 2009.12.
22. Yasuhiro Tanabe, Kazuki Yoshizoe, Hideki Imai, A study on security evaluation methodology for image-based biometrics authentication systems, 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, 10.1109/btas.2009.5339016, 2009.09.
23. 美添 一樹, 分岐因子が一様な探索空間のためのAND-OR木探索アルゴリズム, 博士論文. 東京大学大学院情報理工学系研究科, 2009.02.
24. Kazuki Yoshizoe, A New Proof-Number Calculation Technique for Proof-Number Search, Computers and Games, 10.1007/978-3-540-87608-3_13, 135-145, 2008.09.
25. Rei Yoshida, Rie Shigetomi, Kazuki Yoshizoe, Akira Otsuka, Hideki Imai, A Privacy Protection Scheme for a Scalable Control Method in Context-Dependent Services, WEWoRC 2007: Research in Cryptology, 10.1007/978-3-540-88353-1_1, 1-12, 2008.07.
26. λ Search Based on Proof and Disproof Numbers
We present the df-pn λ search algorithm that combines threats with proof and disproof numbers. λ search is a promising method based on threats. Df-pn is an efficient algorithm that employs the notion of proof and disproof numbers. However, λ search uses neither proof nor disproof numbers, whereas df-pn incorporates no information on threat levels. Integrating threats with proof and disproof numbers is a natural extension to further enhance the search performance. We introduce pseudo-nodes for various threat levels at each node, to represent a node searched with a specific threat level. Then the proof and disproof numbers of the original node are defined using pseudo-nodes, which provides a model that can be searched with df-pn. We compared df-pn λ with df-pn on games with different properties. The results showed that df-pn λ is better than df-pn in Shogi and Go..
27. Kazuki Yoshizoe, Akihiro Kishimoto, Martin Mueller, Lambda Depth-first Proof Number Search and its Application to Go, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-2007), 2404-2409, 2007.01, Thomsen's lambda search and Nagai's depth-first proof-number (DFPN) search are two powerful but very different AND/OR tree search algorithms. Lambda Depth-First Proof Number search (LDFPN) is a novel algorithm that combines ideas from both algorithms. lambda search can dramatically reduce a search space by finding different levels of threat sequences. DFPN employs the notion of proof and disproof numbers to expand nodes expected to be easiest to prove or disprove. The method was shown to be effective for many games. Integrating lambda order with proof and disproof numbers enables LDFPN to select moves more effectively, while preserving the efficiency of DFPN. LDFPN has been implemented for capturing problems in Go and is shown to be more efficient than DFPN and more robust than an algorithm based on classical lambda search..
28. Efficient Implementation of the Lambda Search based on Proof Numbers.
29. Haruhiro Yoshimoto, Kazuki Yoshizoe, Tomoyuki Kaneko, Akihiro Kishimoto, Kenjiro Taura, Monte carlo go has a way to go, Twenty-First National Conference on Artificial Intelligence (AAAI-06), 1070-1075, 2006.07.
30. Kazuki Yoshizoe, A search algorithm for finding multi purpose moves in sub problems of Go, 10th Game Programming Workshop (GPW05), 10, 76-83, 2005.11.
31. Kazuki Yoshizoe, Takashi Matsumoto, Kei Hiraki, Speculative Parallel Execution on JVM, Proceedings of the 1st UK Workshop on Java for High Performance Network Computing, 1998.09.