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Template of a research paper (including abstract)

IMPORTANT Usually a student of FWI needs to publish 3 (or more) peer-reviewed papers in five (at most six) years. If your are not confident, please join an existing project (see this page. Notice that higher priority is better).

1. High-priority projects

1.1 Nonlinear representation theory/非線形代表理論/非线性代表理论

This is a novel theory arguing that the number of representatives for a group of people follows - in general - a nonlinear (non-proportional) law with respect to the size of the group, where "representative" is used in a wide meaning including Member of Parliament and executive manager of a company etc. The theory with this name was first proposed by Zhao, Tanimoto, and Lyu in late 2023, but its contents have been studied for a long time. Currently we are studying the parliament. In the literature, there are two issues on the size of a parliament and the apportionment of the seats respectively. For the first time in the world, we found that these two are actually connected: the rule to decide the size also decides the way of apportionment.

See here for detail.

1.2 Network optimization algorithms and their applications

This topic includes finding a minimum distance dominating set in a given graph or network and its applications including the size of a parliament.

Keywords: dominating set, approximation algorithm, heuristic algorithm, distance dominating, representative nodes, size of parliament

Publications

  1. Lyu, W., Zhao, L. (2024). Pyramid as a Core Structure in Social Networks. In: Cherifi, H., Rocha, L.M., Cherifi, C., Donduran, M. (eds) Complex Networks & Their Applications XII. COMPLEX NETWORKS 2023. Studies in Computational Intelligence, vol 1142. Springer, Cham. https://doi.org/10.1007/978-3-031-53499-7_7
  2. Wenruo Lyu and Liang Zhao. 2024. A Spatial Connection Aware Complex Network Model for Real-World Social Networks. In Proceedings of the 2023 11th International Conference on Information Technology: IoT and Smart City (ICIT '23). Association for Computing Machinery, New York, NY, USA, 155–160. https://doi.org/10.1145/3638985.3639011
  3. Zhao, L., Peng, T. (2020). An Allometric Scaling for the Number of Representative Nodes in Social Networks. In: Masuda, N., Goh, KI., Jia, T., Yamanoi, J., Sayama, H. (eds) Proceedings of NetSci-X 2020: Sixth International Winter School and Conference on Network Science. NetSci-X 2020. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-38965-9_4
  4. Zhao, L.: Finding small dominating sets in large-scale networks. In: Dehmer, M., et al. Big Data of Complex Networks. Chapman and Hall/CRC, Boca Raton (2016)

2. Medium-priority projects

2.1 Graph learning

We study methods and applications of graph Learning, i.e., learning features (finding orders) from graph instances automatically with modern machine/deep learning techniques. One application is algorithms for chemical compound inferring and searching (QSAR and inverse QSAR) by collaboration with Nagamochi lab; Another application is algorithms for drug discovery from Traditional Chinese Medicine, for which we collaborate with Du lab of Kyoto Institute of Technology. We are also studying the explainability of graph learning. This research has connections to the Information Wisdom theory in the sense it studies what learning is. Therefore, its priority is also high.

Keywords: deep learning, graph learning, explainability of machine learning, graph algorithm, drug discovery

Publications (selected)

  1. Li, Y. (2024). Connected-C: Learning the Explainability of Graph Neural Network on Counterfactual and Factual Reasoning via Connected Component. In: Zhao, F., Miao, D. (eds) AI-generated Content. AIGC 2023. Communications in Computer and Information Science, vol 1946. Springer, Singapore. https://doi.org/10.1007/978-981-99-7587-7_7
  2. Gajjar, P., Zuo, Z., Li, Y., Zhao, L. (2023). Enhancing Graph Convolutional Networks with Variational Quantum Circuits for Drug Activity Prediction. In: Kumar, S., Sharma, H., Balachandran, K., Kim, J.H., Bansal, J.C. (eds) Third Congress on Intelligent Systems. CIS 2022. Lecture Notes in Networks and Systems, vol 613. Springer, Singapore. https://doi.org/10.1007/978-981-19-9379-4_57
  3. Jianshen Zhu, Naveed Ahmed Azam, Kazuya Haraguchi, Liang Zhao, Hiroshi Nagamochi, Tatsuya Akutsu. An Inverse QSAR Method Based on Linear Regression and Integer Programming. Front. Biosci. (Landmark Ed) 2022, 27(6), 188. https://doi.org/10.31083/j.fbl2706188

2.2 Image processing algorithms and their applications

We are developing image processing algorithms and their applications, e.g., for screening mental disorder (see the detail), glaucoma, and remote sensing.

Keywords: Image processing, Machine learning, Deep learning, Mental disorder screening, Glaucoma screening, Remote sensing.

Publications

  1. R. Goperma, R. Basnet, P. G. Adhikari, S. N. Joshi, and L. Zhao, “NETRA: Enhancing Glaucoma Diagnosis Through Deep Learning - A Comparative Clinical Validation Study,” 2023 IEEE 11th Region 10 Humanitarian Technology Conference (R10-HTC), pp. 691–698, Oct. 2023, doi: 10.1109/R10-HTC57504.2023.10461926.
  2. R. Basnet, R. Goperma, and L. Zhao, “Attentive Cross-Domain Few-shot Learning and Domain Adaptation in HSI Classification,” TENCON 2023 - 2023 IEEE Region 10 Conference (TENCON), pp. 220–225, Oct. 2023, doi: 10.1109/TENCON58879.2023.10322397.

2.3 Information wisdom theory/情報智慧論/信息智慧论

What is wisdom? What is its difference from knowledge? Zhao proposes a theory that wisdom shall be defined as the action for living, i.e., wisdom for life. More precisely, he proposes an equation that Wisdom = Learning + Random selecting. We are interested in what life is, how life, boids, human, autonomous devices, AI, creativity and future lives can learn orders given the freedom of choice. For detail, check the following publication and/or contact Prof. Zhao.

Keywords: Life, Wisdom，Information, AI, Algorithm, Philosophy, Cognition, Creativity, Random selecting

Publications

  1. Invited talk (in Chinese), Liang Zhao, Information Wisdom, Jan 25, 2024.
  2. 授業，趙亮，「知恵することー情報の視点から生命と社会の本質を考える」，[2023年シラバス]
  3. 講演，趙亮，ふれデミックカフェ@KRP AIは創造もできるか．[講演資料]
  4. Cong Xu & Liang Zhao (2023) Collective achievement, cohesive support, complementary expertise: 3Cs emergent model for shared leadership, Human Resource Development International, 26:2, 175-200, DOI: 10.1080/13678868.2022.2065442
  5. 書籍（分担），趙亮，「情報乱雑さで生きることを考えてみる——機械は賢くなれるか」，池田編著，[実践する総合生存学]，京都大学学術出版会，2021年．
  6. Zhao L., Li W. (2020) “Choose for No Choose”—Random-Selecting Option for the Trolley Problem in Autonomous Driving. Proc. LISS2019. [Link to the paper] or [a draft at researchgate].

3. Low-priority projects

3.1 Optimization in evacuation shelter assignment/避難所割当最適化

日本は，災害後の避難が基本的に自主避難になっている．つまり，どこの避難所に行くかは，各地域（京都では「自主防災会」と呼ばれている）の決めることになっている．その結果，当然ながら最寄りの避難所が選ばれるが、問題は、ほかの地域に済む人の避難が原則不可になっていることで、余裕のある地域とまったく不十分な地域がある（例えば、京大近くの修学院小学校地区では収容率が2%になっている）。本研究は，そういう事情を考慮し全体の最適化の改善を目指す。

Keywords/キーワード: 避難所割当，最適化，自主防災

Level: undergraduate or master project (3 months or more)

Publications

  1. L. Zhao, "A practical system for optimized assignment of shelters to evacuees," 2017 IEEE Canada International Humanitarian Technology Conference (IHTC), Toronto, ON, Canada, 2017, pp. 42-45, doi: 10.1109/IHTC.2017.8058196.
  2. L. Zhao et al., Optimal Assignment of Wide-Area Evacuation Centers for Kyoto City, 5th Annual International Conference on Operations Research and Statistics (ORS 2017), Singapore, March 6, 2017, doi: 10.5176/2251-1938_ORS17.21.
  3. 趙 亮. 新入生授業に課題解決型研究を取り入れて : 京都市広域避難場所割当マップの制作を通じた試み. オペレーションズ・リサーチ = Communications of the Operations Research Society of Japan : 経営の科学. 61(11)=671:2016.11,p.768-771. https://ndlsearch.ndl.go.jp/books/R000000004-I027743785

3.2 Approximation algorithm and its implementation for the Minimum Equivalent Graph problem

Minimum Equivalent Graph problem asks to find a minimum-size, spanning subgraph of a directed graph that keeps the reachability among pairs of nodes. This problem has applications in network design, network visualization, etc. Prof. Zhao developed a very fast algorithm for this NP-hard problem long time ago and would like to provide an open-source implementation to, e.g., the NetworkX package.

Keywords: minimum equivalent graph, strongly-connected spanning subgraph, union-find structure

Level: internship project for an undergraduate or master student (1 month)

Publications

  1. Liang Zhao, Hiroshi Nagamochi, Toshihide Ibaraki, A linear time 53-approximation for the minimum strongly-connected spanning subgraph problem, Information Processing Letters, Volume 86, Issue 2, 2003, Pages 63-70, https://doi.org/10.1016/S0020-0190(02)00476-3.

3.3 Optimization algorithms and their applications.

Here are some topics that I am interested in knowing more.

  1. (not our work) G. Zhao, T. Zhou and L. Gao, "CM-GCN: A Distributed Framework for Graph Convolutional Networks using Cohesive Mini-batches," 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA, 2021, pp. 153-163, doi: 10.1109/BigData52589.2021.9671931.

Past projects

  • Early screening for secondary hypertension (collaboration with 新疆人民医院. 日本笹川医学奨学金第40期共同研究コース)
  • Early screening method for dementia