日本語 / English

Kei Terayama 

• Associate Professor, Bioinformatics Laboratory, Yokohama-City University

• Contact: terayama/at/yokohama-cu.ac.jp

Research topics

• Machine learning and its applications

Journal Papers

2023

1. Y. Shimizu, M. Ohta, S. Ishida, K. Terayama, M. Osawa, T. Honma,  K. Ikeda, "AI-driven molecular generation of not-patented pharmaceutical compounds using world open patent data" Journal of Cheminformatics, 2023, Vol. 15, No.120, pp.1-11. [DOI: 10.1186/s13321-023-00791-z]

2. S. Wang, K. Mizuno, S. Tabeta, K. Terayama, S. Sakamoto, Y. Sugimoto, K. Sugimoto, H. Fukami, L. A. Jimenez, "An efficient segmentation method based on semi-supervised learning for seafloor monitoring in Pujada Bay, Philippines", Ecological Informatics, 78, 102371, 2023. [DOI: 10.1016/j.ecoinf.2023.102371]

3. K. Terayama, Y. Osaki, T. Fujita, R. Tamura, M. Naito, K. Tsuda,* T. Matsui,* M. Sumita,* "Koopmans’ Theorem-Compliant Long-Range Corrected (KTLC) Density Functional Mediated by Black-Box Optimization and Data-Driven Prediction for Organic Molecules", Journal of Chemical Theory and Computation, 2023, Vol.19, No.19, 6770–6781. [DOI: 10.1021/acs.jctc.3c00764]

4. A. Takahashi,* K. Terayama,* Y. Kumagai, R. Tamura, F. Oba, "Fully autonomous materials screening methodology combining first-principles calculations, machine learning and high-performance computing system", Science and Technology of Advanced Materials: Methods, 2023, Vol. 3, No. 1, 2261834. [DOI: 10.1080/27660400.2023.2261834]

5. R. Tamura,* K. Terayama, M. Sumita, K. Tsuda,* "Ranking Pareto optimal solutions based on projection free energy", Physical Review Materials, 2023, 7, 093804. [DOI: 10.1103/PhysRevMaterials.7.093804]

6. Y. Murakami, S. Ishida, Y. Demizu, K. Terayama*, "Design of antimicrobial peptides containing non-proteinogenic amino acids using multi-objective Bayesian optimisation", Digital Discovery, 2023, 2, 1347–1353. [DOI: 10.1039/D3DD00090G]

7. S. Nojima#*, T. Kadoi#, A. Suzuki, C. Kato, S. Ishida, K. Kido, K. Fujita, Y. Okuno, M. Hirokawa, K. Terayama*, Eiichi Morii, "Deep-learning-based differential diagnosis of follicular thyroid tumors using histopathological images", Modern Pathology, 2023, 100296. [DOI: 10.1016/j.modpat.2023.100296]

8. S. Ishida, T. Aasawat, M. Sumita, M, Katouda, T. Yoshizawa, K. Yoshizoe, K. Tsuda, K. Terayama*,  "ChemTSv2: Functional molecular design using de novo molecule generator" WIREs Computional Molecular Science. 2023. e1680. [DOI: 10.1002/wcms.1680]

9. K. Nakamura, E. Uchino, N. Sato, A. Araki, K. Terayama, R. Kojima, K. Murashita, K. Itoh, T. Mikami, Y. Tamada, Y. Okuno, "Individual health-disease phase diagrams for disease prevention based on machine learning", Journal of Biomedical Informatics, 2023, 104448. [DOI: 10.1016/j.jbi.2023.104448]

10. G. Deffrennes, K. Terayama, T. Abe, E. Ogamino, R. Tamura, "A framework to predict binary liquidus by combining machine learning and CALPHAD assessments", Materials & Design, 2023, 112111.  [DOI: 10.1016/j.matdes.2023.112111]

11. S. Matsumoto*, S. Ishida, K. Terayama and Yasuhshi Okuno*, "Quantitative analysis of protein dynamics using a deep learning technique combined with experimental cryo-EM density data and MD simulations", Biophysics and Physicobiology, Vol.20, 2023, e200022. [DOI: 10.2142/biophysico.bppb-v20.0022]

2022

12. N. Ienaga, S. Takahata, K. Terayama, D. Enomoto, H. Ishihara, H. Noda, H. Hagihara*, "Development and Verification of Postural Control Assessment Using Deep-Learning-Based Pose Estimators: Towards Clinical Applications", Occupational Therapy International, Vol.2022, 6952999, 2022. [DOI: 10.1155/2022/6952999]

13. R. Tamura*, M. Sumita*, K. Terayama, K. Tsuda*, F. Izumi, Y. Matsushita*, "Automatic Rietveld refinement by robotic process automation with RIETAN-FP," Science and Technology of Advanced Materials: Methods, 2022. [DOI: 10.1080/27660400.2022.2146470]

14. T. Yoshizawa, S. Ishida*, T. Sato, M. Ohta, T. Honma, K. Terayama*, "Selective Inhibitor Design for Kinase Homologs Using Multiobjective Monte Carlo Tree Search," Journal Chemical Information and Modeling, 2022. [DOI: 10.1021/acs.jcim.2c00787]

15. H. Iwata, Y. Hayashi*, A. Hasegawa, K. Terayama, Y. Okuno*, "Classification of scanning electron microscope images of pharmaceutical excipients using deep convolutional neural networks with transfer learning," International Journal of Pharmaceutics: X, Vol. 4, 100135, 2022. [DOI: 10.1016/j.ijpx.2022.100135]

16. M. Sumita*, K. Terayama, R. Tamura, K. Tsuda, "QCforever: A Quantum Chemistry Wrapper for Everyone to Use in Black-Box Optimization," Journal Chemical Information and Modeling, Vol. 62, No. 18, pp.4427–4434, 2022. [DOI: 10.1021/acs.jcim.2c00812]

17. S. Nojima*, S. Ishida, K. Terayama, K. Matsumoto, T. Matsui, S. Tahara, K. Ohshima, H. Kiyokawa, K. Kido, K. Ukon, S. Y. Yoshida, T. T. Mitani, Y. Doki, T. Mizushima, Y. Okuno, E. A. Susaki, H. R. Ueda, E. Morii*, "A novel three-dimensional imaging system based on polysaccharide staining for accurate histopathological diagnosis of inflammatory bowel diseases," Cellular and Molecular Gastroenterology and Hepatology, Vol. 14, No. 4, pp.905-924, 2022. [DOI: 10.1016/j.jcmgh.2022.07.001]

18. A. Tokuhisa*, Y. Akinaga, K. Terayama, Y. Okamoto, and Y. Okuno, "Single-Image Super-Resolution Improvement of X-ray Single-Particle Diffraction Images Using a Convolutional Neural Network," Journal of Chemical Information and Modeling, Vol.62, No.14, pp.3352-3364, 2022. [DOI: 10.1021/acs.jcim.2c00660]

19. N. Ienaga*, K. Higuchi, T. Takashi, K. Gen, K. Terayama*, "Normal hatching rate estimation for bulk samples of Pacific bluefin tuna (Thunnus orientalis) eggs using deep learning," Aquacultural Engineering, Vol.98, 102274, 2022. [DOI: 10.1016/j.aquaeng.2022.102274]

20. T. Yoshida*, J. Zhou, K. Terayama, D. Kitazawa, "Monitoring of cage-cultured sea cucumbers using an underwater time-lapse camera and deep learning-based image analysis," Smart Agricultural Technology, Vol. 3, 100087, 2022. [DOI: 10.1016/j.atech.2022.100087]

21. Y. Motoyama*, R. Tamura*, K. Yoshimi*, K. Terayama, T. Ueno, K. Tsuda, "Bayesian optimization package: PHYSBO," Computer Physics Communications, Vol.278, 108405, 2022. [DOI: 10.1016/j.cpc.2022.108405]

22. T. Fujita#, K. Terayama#, M. Sumita*, R. Tamura, Y. Nakamura, M. Naito, Koji Tsuda*, "Understanding the evolution of a de novo molecule generator via characteristic functional group monitoring," Science and Technology of Advanced Materials, Vol.23, No.1, pp.352-360, 2022. [DOI: 10.1080/14686996.2022.2075240]

23. R. Tamura*, G. Deffrennes, K. Han, T. Abe, H. Morito, Y. Nakamura, M. Naito, R. Katsube, Y. Nose, K. Terayama*, "Machine-learning-based phase diagram construction for high-throughput batch experiments," Science and Technology of Advanced Materials: Methods, 2022. [DOI: 10.1080/27660400.2022.2076548]

24. R. Kanada*, K. Terayama*, A. Tokuhisa, S. Matsumoto, Y. Okuno, "Enhanced Conformational Sampling with an Adaptive Coarse-Grained Elastic Network Model Using Short-Time All-Atom Molecular Dynamics," Journal of Chemical Theory and Computation, Vol.18, No.4, pp.2062–2074, 2022. [DOI: 10.1021/acs.jctc.1c01074]

25. M. Sumita, K. Terayama, N. Suzuki, S. Ishihara, R. Tamura, M. K. Chahal, D. T. Payne, K. Yoshizoe, K. Tsuda,  "De novo creation of a naked eye–detectable fluorescent molecule based on quantum chemical computation and machine learning," Science Advances, Vol.8, No.10, eabj3906, 2022. [DOI: 10.1126/sciadv.abj3906]

26. S. Ishida, K. Terayama, R. Kojima, K. Takasu, Y. Okuno, "AI-Driven Synthetic Route Design Incorporated with Retrosynthesis Knowledge," Journal of Chemical Information and Modeling, Vol.62, No.6, pp.1357–13672022. [DOI: 10.1021/acs.jcim.1c01074]

27. G. Deffrennes, K. Terayama, T. Abe, R. Tamura, "A machine learning–based classification approach for phase diagram prediction," Materials & Design, Vol.215, 110497, 2022. [DOI: 10.1016/j.matdes.2022.110497]

28. N. Ienaga, A. Cravotta, A., K. Terayama,  B. W. Scotney, H. Saito, M. G. Busà, "Semi-automation of gesture annotation by machine learning and human collaboration." Language  Resources and Evaluation, 2022. [DOI: 10.1007/s10579-022-09586-4] 

29. Wei-Hsun Hu, Ta-Te Chen, Ryo Tamura, Kei Terayama, Siqian Wang, Ikumu Watanabe, Masanobu Naito*, "Topological alternation from structurally adaptable to mechanically stable crosslinked polymer," Science and Technology of Advanced Materials, Vol.23, No.1, pp.66-75, 2022. [DOI: 10.1080/14686996.2021.2025426]

30. K. Terayama, K. Mizuno*, S. Tabeta, S. Sakamoto, Y. Sugimoto, K. Sugimoto, H. Fukami, M. Sakagami, L. A. Jimenez, "Cost-effective seafloor habitat mapping using a portable speedy sea scanner and deep-learning-based segmentation: A sea trial at Pujada Bay, Philippines," Methods in Ecology and Evolution, Vol.13, No.2, pp.339-345, 2022. [DOI: 10.1111/2041-210X.13744]

    • METHODS BLOG

31. K. Terayama*, K. Han, R. Katsube, I. Ohnuma, T. Abe, Y. Nose, R. Tamura*, "Acceleration of phase diagram construction by machine learning incorporating Gibbs' phase rule," Scripta Materialia, Vol. 28, 114335, 2022. [DOI: 10.1016/j.scriptamat.2021.114335]

2021

32. Xiaolin Sun, Ryo Tamura, Masato Sumita, Kenichi Mori, Kei Terayama, and Koji Tsuda*, "Integrating Incompatible Assay Data Sets with Deep Preference Learning," ACS Medicinal Chemistry Letters, Vol.13, No.1, pp.70-75, 2021. [DOI: 10.1021/acsmedchemlett.1c00439]

33. Y. Amamoto*, H. Kikutake, K. Kojio, A. Takahara, and K. Terayama, "Visualization of judgment regions in convolutional neural networks for X-ray diffraction and scattering images of aliphatic polyesters," Polymer Journal, Vol.53, pp.1269-1279, 2021. [DOI: 10.1038/s41428-021-00531-w]

34. K. Terayama*, M. Sumita, M. Katouda, K. Tsuda, and Y. Okuno*, "Efficient Search for Energetically Favorable Molecular Conformations against Metastable States via Gray-Box Optimization," Journal of Chemical Theory and Computation, Vol.17, No.8, pp.5419-5427, 2021. [DOI: 10.1021/acs.jctc.1c00301]

35. T. Yamashita, S. Kanehira, N. Sato, H. Kino, K. Terayama, H. Sawahata, T. Sato, F. Utsuno, K. Tsuda, T. Miyake, and T. Oguchi, "CrySPY: a crystal structure prediction tool accelerated by machine learning," Science and Technology of Advanced Materials: Methods, Vol.1, No.1, pp.87-97, 2021. [DOI: 10.1080/27660400.2021.1943171]

36. B. Ma, K. Terayama*, S. Matsumoto, Y. Isaka, Y. Sasakura, H. Iwata, M. Araki, and Y. Okuno*, "Structure-Based de Novo Molecular Generator Combined with Artificial Intelligence and Docking Simulations," Journal of Chemical Information and Modeling, Vol. 61, No.7, pp.3304-3313, 2021. [DOI: 10.1021/acs.jcim.1c00679]

37. Y. Zhang, J. Zhang, K. Suzuki, M. Sumita, K. Terayama, J. Li, Z. Mao, K. Tsuda, and Y. Suzuki*, "Discovery of polymer electret material via de novo molecule generation and functional group enrichment analysis", Applied Physics Letters, Vol.118, 223904, 2021. [DOI: 10.1063/5.0051902]

    • Selected as a Featured Article

38. S. Nojima#, K. Terayama#, S. Shimoura, S. Hijiki, N. Nonomura, E Morii, Y. Okuno,  K. Fujita*, "A deep learning system to diagnose the malignant potential of urothelial carcinoma cells in cytology specimens." Cancer Cytopathology, Vol.129, No.12, pp.984-995, 2021. [DOI: 10.1002/cncy.22443]

39. H. Habe, Y. Takeuchi, K. Terayama, and M. Sakagami, "Pose estimation of swimming fish using an NACA airfoil model for collective behavior analysis," Journal of Robotics and Mechatronics, Vol.33, No.3, pp.547-555, 2021. [DOI: 10.20965/jrm.2021.p0547]

40. K. Terayama, M. Sumita, R. Tamura, and K. Tsuda*, "Black-Box Optimization for Automated Discovery," Accounts of Chemical Research, Vol.54, No.6, pp.1334-1346, 2021. [DOI: 10.1021/acs.accounts.0c00713]

41. S. Matsumoto#, S. Ishida#, M. Araki, T. Kato, K. Terayama*, and Y. Okuno* "Extraction of protein dynamics information from cryo-EM maps using deep learning," Nature Machine Intelligence, Vol.3, pp.153-160, 2021. [DOI: 10.1038/s42256-020-00290-y]

    • "New AI Tool May Speed Up Drug Discovery Using Images" (Psychology Today 2/7/2021)

42. N. Ienaga#, K. Higuchi#, T. Takashi, K. Gen, K. Tsuda, and K. Terayama*, "Vision-based egg quality prediction in Pacific bluefin tuna (Thunnus orientalis) by deep neural network," Scientific Reports, Vol.11, No.6, 2021. [DOI: 10.1038/s41598-020-80001-0]

2020

43. H. Hagihara*, N. Ienaga, K. Terayama, Y. Moriguchi, and M. Sakagami, "Looking represents choosing in toddlers: Exploring the equivalence between multimodal measures in forced-choice tasks," Infancy, Vol.26, pp.148–167, 2020. [DOI:10.1111/infa.12377]

44. R. Shibukawa#, S. Ishida#, K. Yoshizoe, K. Wasa, K. Takasu, Y. Okuno, K. Terayama*, and K. Tsuda*, "CompRet: a comprehensive recommendation framework for chemical synthesis planning with algorithmic enumeration," Journal of Cheminformatics,  Vol.12, No.52, 2020. [DOI: 10.1186/s13321-020-00452-5]

45. K. Mizuno*, K. Terayama, S. Hagino, S. Tabeta, S. Sakamoto, T. Ogawa, K. Sugimoto, and H. Fukami, "An efficient coral survey method based on a large‐scale 3‐D structure model obtained by Speedy Sea Scanner and U‐Net segmentation," Scientific Reports, Vol.10, No.1, 12416, 2020. [DOI: 10.1038/s41598-020-69400-5]

46. J. Zhang, K. Terayama, M. Sumita, K. Yoshizoe, K. Ito, J. Kikuchi, and K. Tsuda, "NMR-TS: de novo molecule identification from NMR spectra," Science and Technology of Advanced Materials, Vol.21, No.1, pp.552-561, 2020. [DOI: 10.1080/14686996.2020.1793382]

47. A. Tokuhisa, R. Kanada, S. Chiba, K. Terayama, Y. Isaka, B. Ma, N. Kamiya, and Y. Okuno, "Coarse-grained diffraction template matching model to retrieve multi-conformational models for biomolecule structures from noisy diffraction patterns," Journal of Chemical Information and Modeling, Vol.60, No.6, pp.2803-2818, 2020. [DOI: 10.1021/acs.jcim.0c00131]

48. K. Terayama*, M. Sumita, R. Tamura, D. T. Payne, M. K. Chahal, S. Ishihara, and K. Tsuda*, "Pushing property limits in materials discovery via boundless objective-free exploration," Chemical Science, Vol.11, pp.5959-5968, 2020. [DOI: 10.1039/D0SC00982B]

    • "Algorithm tracks down buried treasure among existing compounds"

    • "Machine learning helps find materials that buck the trend" (Research Highlight 8/7/2020)

49. R. Katsube*, K. Terayama, R. Tamura, and Y. Nose*, "Experimental establishment of phase diagram guided by uncertainty sampling:  an application to the deposition of Zn-Sn-P films by molecular beam epitaxy," ACS Materials Letters, Vol. 2, No.6, pp.571-575, 2020. [DOI: 10.1021/acsmaterialslett.0c00104]

50. H. Hagihara#*, N. Ienaga#, D. Enomoto, S. Takahata, H. Ishihara, H. Noda, K. Tsuda, and K. Terayama*, "Computer Vision–Based Approach for Quantifying Occupational Therapists' Qualitative Evaluations of Postural Control," Occupational Therapy International, Vol.2020, 8542191, 2020. [DOI: 10.1155/2020/8542191]

51. R. Kanada*, A. Tokuhisa, K. Tsuda, Y. Okuno, K. Terayama*, "Exploring Successful Parameter Region for Coarse-Grained Simulation of Biomolecules by Bayesian Optimization and Active Learning," Biomolecules, Vol.10, Issue. 3, No. 482,  2020. [DOI: 10.3390/biom10030482]

2019

52. Y. Saito*, K. Shin, K. Terayama, S. Desai, M. Onga, Y. Nakagawa, Y. M. Itahashi, Y. Iwasa, M. Yamada, K. Tsuda*, “Deep Learning-based quality filtering of mechanically exfoliated 2D crystals,” npj Computational Materials, Vol.5, No. 124, 2019. [DOI: 10.1038/s41524-019-0262-4]

53. S. Ishida, K. Terayama, R. Kojima, K. Takasu, Y. Okuno, "Prediction and Interpretable Visualization of Retrosynthetic Reactions Using Graph Convolutional Networks," Journal of Chemical Information and Modeling, Vol. 59, No. 12, pp.5026-5033, 2019. [DOI: 10.1021/acs.jcim.9b00538]

54. K. Terayama#, A. Shinobu#, K. Tsuda, K. Takemura, A. Kitao*, "evERdock BAI: machine-learning-guided selection of protein-protein complex structure", Journal of Chemical Physics, Vol. 151, No. 21, 215104, 2019. [DOI: 10.1063/1.5129551]

55.  K. Mizuno, K. Terayama, S. Tabeta, S. Sakamoto, Y. Matsumoro, Y. Sugimoto, T. Ogawa, K. Sugimoto, H. Fukami, M. Sakagami, M. Deki, and A. Kawakubo, Development of an efficient coral-coverage estimation method using a towed optical camera array system (SSS: Speedy Sea Scanner) and deep-learning-based segmentation: A sea trial at the Kujuku-shima islands, IEEE Journal of Oceanic Engineering, 2019. [DOI: 10.1109/JOE.2019.2938717]

56. K. Shin, D. P. Tran, K. Takemura, A. Kitao, K. Terayama*, and K. Tsuda*, "Enhancing biomolecular sampling with reinforcement learning: tree search molecular dynamics simulation method," ACS Omega, Vol.4, No.9, pp.13853-13862, 2019. [DOI: 10.1021/acsomega.9b01480]

57. K. Terayama*, K. Tsuda*, R. Tamura*, "Efficient recommendation tool of materials by executable file based on machine learning," Japanese Journal of Applied Physics, Vol. 58, No. 9, 098001, 2019. [DOI: 10.7567/1347-4065/ab349b]

58. K. Terayama*#, K. Shin#, K. Mizuno, K. Tsuda*, "Integration of sonar and optical camera images using deep neural network for fish monitoring," Aquacultural Engineering, Vol. 86, 102000, 2019. [DOI: 10.1016/j.aquaeng.2019.102000]

59. K. Terayama*#, R. Tamura#, Y. Nose, H. Hiramatsu, H. Hosono, Y. Okuno, K. Tsuda*, "Efficient Construction Method for Phase Diagrams Using Uncertainty Sampling," Physical Review Materials, Vol. 3, No. 3, 033802, 2019. [DOI: 10.1103/PhysRevMaterials.3.033802]

2018

60. M. Araki, H. Iwata, B. Ma, A. Fujita, K. Terayama, Y. Sagae, F. Ono, K. Tsuda, N. Kamiya, Y. Okuno*, “Improving the accuracy of protein-ligand binding mode prediction using a molecular dynamics-based pocket generation approach,” Journal of Computational Chemistry, Vol. 39, No. 32, pp.2679-2689, 2018. [DOI: 10.1002/jcc.25715]

61. N. Yoshikawa, K. Terayama, M. Sumita, T. Homma, K. Oono, K. Tsuda*, "Population-based de novo Molecule Generation, Using Grammatical Evolution," Chemistry Letters, Vol.47, No.11, pp.1431-1434, 2018. [DOI: 10.1246/cl.180665] (Selected for Editor's Choice, Cover Picture)

62. K. Terayama*, T. Yamashita, T. Oguchi, and K. Tsuda*, "Fine-grained optimization method for crystal structure prediction," npj Computational Materials, Vol.4, No. 32, 2018. [DOI: 10.1038/s41524-018-0090-y]

63. K. Terayama*, H. Iwata, M. Araki, Y. Okuno, and K. Tsuda*, "Machine Learning Accelerates MD-based Binding Pose Prediction between Ligands and Proteins," Bioinformatics, Vol.34, Issue 5, pp.770-778, 2018. [DOI: 10.1093/bioinformatics/btx638]

-2017

64. X. Yang, J. Zhang, K. Yoshizoe, K. Terayama, and K. Tsuda*, "ChemTS: An Efficient Python Library for de novo Molecular Generation", Science and Technology of Advanced Materials, Vol.18, No.1, pp.972-976, 2017. [DOI: 10.1080/14686996.2017.1401424]

65. K. Terayama*, H. Habe and M. Sakagami, “Multiple Fish Tracking with an NACA Airfoil Model for Collective Behavior Analysis,” IPSJ Transactions on Computer Vision and Applications, Vol.8, No.4, pp.1-7 (2016). [DOI: 10.1186/s41074-016-0004-1]

66. K. Terayama*, H. Hioki* and M. Sakagami*, “A measurement method for speed distribution of collective motion with optical flow and its applications to school of fish,” International Journal of Semantic Computing, Vol.9, No.2, pp.143-168 (2015). [DOI:10.1142/S1793351X15400012]

67. K. Terayama*, H. Tsuiki*, “A Stream Calculus of Bottomed Sequences for Real Number Computation,” Electronic Notes in Theoretical Computer Science, Vol.298, pp.383-402(2013). [DOI:10.1016/j.entcs.2013.09.023]

(*: corresponding author, #: equally contributed)

Refereed International Conference Papers

• Ichiro K. Shimatani, K. Terayama, M. Sakagami, “Circular regression models for identifying abnormal parts in swarm behaviors and their quantitative characterization: data science approach,” The 2nd International Symposium on Swarm Behavior and Bio-Inspired Robotics (SWARM 2017), Kyoto, 2017/11/1. (Refereed, Oral)

• K. Terayama, H. Hioki, and M. Sakagami, “Measuring Tail Beat Frequency and Coast Phase in School of Fish for Collective Motion Analysis,” The 8th International Conference on Graphic and Image Processing (ICGIP 2016), Tokyo, 2016/10. [DOI:10.1117/12.2266447](Refereed, Oral).

• K. Terayama, K. Hongo, H. Habe and M. Sakagami, “Appearance-based Multiple Fish Tracking for Collective Motion Analysis,” The Third IAPR Asian Conference on Pattern Recognition (ACPR 2015), Kuala Lumpur, 2015/11/5. [10.1109/ACPR.2015.7486526](Refereed, Poster, acceptance rate 170/422 = 40.3%).

• K. Terayama and M. Sakagami, “Measurement of Velocity Fields of Schools of Sardines and Existence of Averaged Tori,” The First International Symposium on Swarm Behavior and Bio-Inspired Robotics (SWARM 2015), Kyoto, 2015/10(Refereed, Accepted).

• K. Terayama and H. Hioki, “A Practical Classifier for Photographs and Non-Photographic Images Based on Local Visual Features,” The 14th IAPR Conference on Machine Vision Applications (MVA 2015), pp.307-311, Tokyo, 2015/5/20(Refereed, Poster).

• K. Terayama, H. Hioki and M. Sakagami, “A Measurement Method for Speed Distribution of Collective Motion with Optical Flow and its Application to Estimation of Rotation Curve,” IEEE International Symposium on Multimedia (ISM 2014), Taichung, 2014/12(Oral(Regular Paper), acceptance rate 21.3%).