| 1. |
Shoichi Ishida, Tanuj Aasawat, Masato Sumita, Michio Katouda, Tatsuya Yoshizawa, Kazuki Yoshizoe, Koji Tsuda, Kei Terayama, ChemTSv2: Functional molecular design using de novo molecule generator, WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE, 10.1002/wcms.1680, 2023.07, Designing functional molecules is the prerogative of experts who have advanced knowledge and experience in their fields. To democratize automatic molecular design for both experts and nonexperts, we introduce a generic open-sourced framework, ChemTSv2, to design molecules based on a de novo molecule generator equipped with an easy-to-use interface. Besides, ChemTSv2 can easily be integrated with various simulation packages, such as Gaussian 16 package, and supports a massively parallel exploration that accelerates molecular designs. We exhibit the potential of molecular design with ChemTSv2, including previous work, such as chromophores, fluorophores, drugs, and so forth. ChemTSv2 contributes to democratizing inverse molecule design in various disciplines relevant to chemistry.This article is categorized under:Data Science > Databases and Expert SystemsData Science > Artificial Intelligence/Machine LearningData Science > Computer Algorithms and Programming. |
| 2. |
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. |
| 3. |
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.. |
| 4. |
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, 383-390, 2020.12, Modern shared-memory platforms embrace the Non-uniform Memory Access (NUMA) architecture - they have physically distributed, yet cache-coherent shared-memory. This paper explores the feasibility of a shared-memory graph processing engine for NUMA platforms inspired by designs that target zero-sharing platforms. This work exploits the characteristics of two processing modes, synchronous and asynchronous, in the context of the shared-memory NUMA platform. Depending on the algorithm, phase of execution, and graph topology, synchronous and asynchronous modes hold unique advantages over one another. We then explore a hybrid solution that combines synchronous and asynchronous processing within the same graph computation task and harness optimizations therein. An extensive evaluation using graphs with billions of edges and empirical comparisons with several state-of-the-art solutions demonstrate the performance advantages of our design.. |
| 5. |
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, Since the cost and time required to finetune parameters in traditional short-term traffic prediction models such as traffic simulators are very high, the prediction models have been developed mainly for managing recurrent congestion, rather than non-recurrent congestion caused, for example, by disaster. Machine learning models are promising candidates for traffic prediction during non-recurrent congestion due to their ability to tune parameters without a-priori knowledge, while their applicability to non-recurrent conditions has rarely been explored. To fill in this gap, this study conducts an exploratory analysis on the applicability of various machine learning models during a transportation network disruption with particular focuses on their ability to predict traffic states and the interpretability of the results. The analysis is conducted by using data obtained during the massive transport network disruption which occurred in Hiroshima in July 2018 due to heavy rain and subsequent landslides. The models tested include random forest, support vector machine, XGBoost, shallow feed-forward neural network, and deep feed-forward neural network. The results indicate that random forest and XGBoost methods produced the best results in terms of prediction accuracy. On the other hand, deep neural network models produce better results in terms of the interpretability of the results, i.e., the results can be logically explained from the perspective of existing traffic flow theory. These findings indicate that the model which produces the best prediction accuracy is not always the best for practical use since it does not mimic the mechanisms of congestion occurrence.. |
| 6. |
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, 12370 LNCS, 1-16, 2020.11, Data augmentation methods are indispensable heuristics to boost the performance of deep neural networks, especially in image recognition tasks. Recently, several studies have shown that augmentation strategies found by search algorithms outperform hand-made strategies. Such methods employ black-box search algorithms over image transformations with continuous or discrete parameters and require a long time to obtain better strategies. In this paper, we propose a differentiable policy search pipeline for data augmentation, which is much faster than previous methods. We introduce approximate gradients for several transformation operations with discrete parameters as well as a differentiable mechanism for selecting operations. As the objective of training, we minimize the distance between the distributions of augmented and original data, which can be differentiated. We show that our method, Faster AutoAugment, achieves significantly faster searching than prior methods without a performance drop.. |
| 7. |
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.. |
| 8. |
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. |
| 9. |
Faster AutoAugment: Learning Augmentation Strategies Using Backpropagation.. |
| 10. |
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. |
| 11. |
Using local minima to accelerate Krawczyk-Hansen global optimization. |
| 12. |
Shota Izumi, Daisuke Ishii, Kazuki Yoshizoe, An Extended GLB Library for Optimization Problems, 2018.01. |
| 13. |
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, Automatic design of organic materials requires black-box optimization in a vast chemical space. In conventional molecular design algorithms, a molecule is built as a combination of predetermined fragments. Recently, deep neural network models such as variational autoencoders and recurrent neural networks (RNNs) are shown to be effective in de novo design of molecules without any predetermined fragments. This paper presents a novel Python library ChemTS that explores the chemical space by combining Monte Carlo tree search and an RNN. In a benchmarking problem of optimizing the octanol-water partition coefficient and synthesizability, our algorithm showed superior efficiency in finding high-scoring molecules. ChemTS is available at https://github.com/tsudalab/ChemTS.[GRAPHICS].. |
| 14. |
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. |
| 15. |
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, Background: Artificially synthesized RNA molecules provide important ways for creating a variety of novel functional molecules. State-of-the-art RNA inverse folding algorithms can design simple and short RNA sequences of specific GC content, that fold into the target RNA structure. However, their performance is not satisfactory in complicated cases.Result: We present a new inverse folding algorithm called MCTS-RNA, which uses Monte Carlo tree search (MCTS), a technique that has shown exceptional performance in Computer Go recently, to represent and discover the essential part of the sequence space. To obtain high accuracy, initial sequences generated by MCTS are further improved by a series of local updates. Our algorithm has an ability to control the GC content precisely and can deal with pseudoknot structures. Using common benchmark datasets for evaluation, MCTS-RNA showed a lot of promise as a standard method of RNA inverse folding.Conclusion: MCTS-RNA is available at https://github.com/tsudalab/MCTS-RNA.. |
| 16. |
Optimization of Playout Policy Integrated with Monte-Carlo Tree Search. |
| 17. |
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, 33-38, 2015.06, We present a scalable parallel solver for numerical constraint satisfaction problems (NCSPs). Our parallelization scheme consists of homogeneous worker solvers, each of which runs on an available core and communicates with others via the global load balancing (GLB) method. The search tree of the branch and prune algorithm is split and distributed through the two phases of GLB: a random workload stealing phase and a workload distribution and termination phase based on a hyper-cube-shaped graph called lifeline. The parallel solver is simply implemented with X10 that provides an implementation of GLB as a library. In experiments, NCSPs from the literature were solved and attained up to 516-fold speedup using 600 cores of the TSUBAME2.5 supercomputer. Optimal GLB configurations are analyzed.. |
| 18. |
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. |
| 19. |
Humanlike AI for Mario Bros. Based on Evolutionary Computation and UCT. |
| 20. |
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. |
| 21. |
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. |
| 22. |
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, 161-166, 2009.12, After a computer named "Deep Blue" defeated the world chess champion Garry Kasparov in 1997, researchers studying computer board games focused their attention on the game "Go." Go is known to be more difficult for computers to play than chess or shogi because (1) the search space for Go is much larger, (2) it is difficult to define an appropriate evaluation function of position, and (3) a position sometimes changes globally in just one move. Recently, a new meth ad called Monte Carlo Go has been developed, which involves performing Monte Carlo simulations to evaluate a position. Monte Carlo Go increases the strength of the Computer-Go program. For Monte Carlo Go, the strength fully depends on the number of simulations. Several attempts were made to accelerate simulations, e.g., by the use of cluster systems and FPGAs. The cluster system yields good results, but it is a very expensive system. On the other hand, acceleration using an FPGA was not so easy because the usage of FPGA resources tends to be high. Previously, FPGA acceleration was feasible for smaller board such as a board with a 9 x 9 grid, while it was not feasible for the standard board with a 19 x 19 grid. In this paper, we propose triple line-based playout for Go (TLPG), a hardware algorithm for generating simulations using an FPGA. By reproducing global information redundantly, TLPG enables the generation of simulations only using local operations; this helps realize compact implementations of hardware logic, and thus, TLPG can handle both 9 x 9 and 19 x 19 grid Go boards. We implement TLPG on Xilinx Virtex-5 (XC5VFX70T-IFF1136) and evaluate it. TLPG can perform 40,649 playouts per second for a 9 x 9 grid Go board and 4,668 playouts per second for a 19 x 19 grid Go board.. |
| 23. |
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, We propose here a security evaluation methodology of image-based biometrics authentication systems against wolf attacks. A wolf attack is an attempt to impersonate a victim by feeding wolves into the system to be attacked. The wolf is input data that can be falsely accepted as a match with multiple templates. To create a secure system, we must evaluate the possibility of wolf attacks. Existing studies have relied on theoretical analysis of algorithms carried out by human beings, which is only effective if theoretical analysis is possible. Therefore, we propose a more generic approach based on a search to assist the developers of matching algorithms. We searched for wolves by using a recently developed algorithm called Monte-Carlo Tree Search (MCTS). We succeeded in detecting wolves in a matching algorithm, which appears promising considering that this is the first trial for this kind of approach. ©2009 IEEE.. |
| 24. |
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. |
| 25. |
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. |
| 26. |
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. |
| 27. |
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.. |
| 28. |
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.. |
| 29. |
Efficient Implementation of the Lambda Search based on Proof Numbers. |
| 30. |
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. |
| 31. |
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. |
| 32. |
YOSHIZOE K., Speculative Parallel Execution on JVM, 1st UK Workshop on Java for High Performance Network Computing, 1998.09. |
