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4. Representation Learning of Tangled Key-Value Sequence Data for Early Classification

Author

Duan, Tao, Zhao, Junzhou, Zhang, Shuo, Tao, Jing, and Wang, Pinghui

Subjects
Computer Science - Machine Learning, Computer Science - Networking and Internet Architecture
Abstract

Key-value sequence data has become ubiquitous and naturally appears in a variety of real-world applications, ranging from the user-product purchasing sequences in e-commerce, to network packet sequences forwarded by routers in networking. Classifying these key-value sequences is important in many scenarios such as user profiling and malicious applications identification. In many time-sensitive scenarios, besides the requirement of classifying a key-value sequence accurately, it is also desired to classify a key-value sequence early, in order to respond fast. However, these two goals are conflicting in nature, and it is challenging to achieve them simultaneously. In this work, we formulate a novel tangled key-value sequence early classification problem, where a tangled key-value sequence is a mixture of several concurrent key-value sequences with different keys. The goal is to classify each individual key-value sequence sharing a same key both accurately and early. To address this problem, we propose a novel method, i.e., Key-Value sequence Early Co-classification (KVEC), which leverages both inner- and inter-correlations of items in a tangled key-value sequence through key correlation and value correlation to learn a better sequence representation. Meanwhile, a time-aware halting policy decides when to stop the ongoing key-value sequence and classify it based on current sequence representation. Experiments on both real-world and synthetic datasets demonstrate that our method outperforms the state-of-the-art baselines significantly. KVEC improves the prediction accuracy by up to $4.7 - 17.5\%$ under the same prediction earliness condition, and improves the harmonic mean of accuracy and earliness by up to $3.7 - 14.0\%$., Comment: 12 pages, 31 figures, Accepted by ICDE2024

Published
2024

7. Fetal overgrowth and weight trajectories during infancy and adiposity in early childhood

Author

Tao, Min-Yi, Liu, Xin, Chen, Zi-Lin, Yang, Meng-Nan, Xu, Ya-Jie, He, Hua, Fang, Fang, Chen, Qian, Mao, Xuan-Xia, Zhang, Jun, Ouyang, Fengxiu, Shen, Xiu-Hua, Li, Fei, Luo, Zhong-Cheng, Shen, Xiaoming, Huang, Hong, Sun, Kun, Zhang, Jun, Wang, Weiye, Xu, Weiping, Ouyang, Fengxiu, Li, Fei, Huang, Yin, Zhang, Jinsong, Yan, Chonghuai, Shen, Lisong, Bao, Yixiao, Tian, Ying, Chen, Weiwei, Zhang, Huijuan, Tong, Chuanliang, Xu, Jian, Zhang, Lin, Zhang, Yiwen, Jiang, Fang, Yu, Xiaodan, Yu, Guangjun, Chen, Jinjin, Zhang, Yu, Li, Xiaotian, Cheng, Haidong, Zhang, Qinying, Duan, Tao, Hua, Jing, and Peng, Hua

Published
2024
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29. Machine learning-enabled maternal risk assessment for women with pre-eclampsia (the PIERS-ML model): a modelling study

Author

Brown, Mark A., Davis, Gregory K., Parker, Claire, Walters, Barry N., Sass, Nelson, Ansermino, J. Mark, Cao, Vivien, Cundiff, Geoffrey W., von Dadelszen, Emma C.M., Douglas, M. Joanne, Dumont, Guy A., Dunsmuir, Dustin T., Hutcheon, Jennifer A., Joseph, K.S., Lalji, Sayrin, Lee, Tang, Li, Jing, Lim, Kenneth I., Lisonkova, Sarka, Lott, Paula, Menzies, Jennifer M., Millman, Alexandra L., Palmer, Lynne, Payne, Beth A., Qu, Ziguang, Russell, James A., Sawchuck, Diane, Shaw, Dorothy, Still, D. Keith, Ukah, U. Vivian, Wagner, Brenda, Walley, Keith R., Hugo, Dany, Gruslin, The late Andrée, Tawagi, George, Smith, Graeme N., Côté, Anne-Marie, Moutquin, Jean-Marie, Ouellet, Annie B., Lee, Shoo K., Duan, Tao, Zhou, Jian, Haniff, The late Farizah, Mahajan, Swati, Noovao, Amanda, Karjalainend, Hanna, Kortelainen, Alja, Laivuori, Hannele, Ganzevoort, J. Wessel, Groen, Henk, Kyle, Phillipa M., Moore, M. Peter, Pullar, Barbra, Bhutta, Zulfiqar A., Qureshi, Rahat N., Sikandar, Rozina, Bhutta, The late Shereen Z., Cloete, Garth, Hall, David R., van Papendorp, The late Erika, Steyn, D. Wilhelm, Biryabarema, Christine, Mirembe, Florence, Nakimuli, Annettee, Allotey, John, Thangaratinam, Shakila, Nicolaides, Kypros H., Ionescu, Olivia, Syngelaki, Argyro, de Swiet, Michael, Magee, Laura A., von Dadelszen, Peter, Akolekar, Ranjit, Walker, James J., Robson, Stephen C., Broughton-Pipkin, Fiona, Loughna, Pamela, Vatish, Manu, Redman, Christopher W.G., Barry, Sarah J.E., Kavanagh, Kimberley, Montgomery-Csobán, Tunde, Murray, Paul, Robertson, Chris, Tsigas, Eleni Z., Woelkers, Douglas A., Lindheimer, Marshall D., Grobman, William A., Sibai, Baha M., Merialdi, Mario, Widmer, Mariana, Montgomery-Csobán, Tünde, Barry, Sarah J E, Vivian Ukah, U, Payne, Beth A, Nicolaides, Kypros H, Hutcheon, Jennifer A, and Magee, Laura A

Published
2024
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36. Research Progress on the Application of Biotechnology in Value-added Conversion of Grain, Oil and Feed Resources

Author

WANG Yong-wei, SHI Jing-jing, DUAN Tao, HE Bei-bei, LIU Kuan-bo, WANG Li, and LI Ai-ke

Subjects
grain and oil feed resources, biotechnology, value-added utilization, fermented feed, soybean meal replacement, Food processing and manufacture, TP368-456, Nutrition. Foods and food supply, TX341-641
Abstract

The shortage of high-quality protein feed resources has become an important variable affecting food security in China. Grain and oil processing by-products are abundant in China, but the feeding value is low. If these resources are processed through modern biotechnology, such as biofermentation, enzymolysis technology or synthetic biology, their quality and efficiency will be improved and can be used to replace the soybean meal. This paper summarized the production process, feed value and its application in soybean meal replacement of fermented feed, enzymolysis feed and microbial protein feed, and also introduced the research progress of microbial adsorption, degradation, or enzymes on removing mycotoxins, finally put forward the research prospect of development, industrialization application, standard establishment, and industry supervision for biological fermentation feed. This paper can provide guidance for the application of biotechnology in grain and oil feed resources.

Published
2023
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37. Progress and Challenges of Materials and Methods for Uranium Extraction From Seawater

Author

TANG Xing-rui, HUANG Peng-ling, RUAN Hao-ming, TANG Li, GONG Xiang, DUAN Tao, CHEN Shu-sen, HE Rong, and ZHU Wen-kun

Subjects
uranium extraction from seawater, adsorption method, reduction method, materials, Nuclear engineering. Atomic power, TK9001-9401, Chemical technology, TP1-1185
Abstract

The ocean covers 70% of the earth’s area, and the seawater uranium is more than 1000 times that of terrestrial uranium ore. In order to increase the power generation and ensure the sustainable operation of nuclear power plants, the development of unconventional uranium resources is of great significance. As an effective means to solve shortage of terrestrial uranium resources, uranium extraction from seawater has received extensive attention in recent years. At present, many methods including membrane separation, chemical deposition, solvent extraction, ion exchange, reduction and adsorption have been applied to uranium extraction from seawater. This paper focuses on the recent research progress of adsorption method used inorganic materials, organic materials, carbon materials as adsorbent, and emerging electroreduction technologies, and further discusses the challenges and opportunities of these two methods.

Published
2023
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