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234 results on '"Huang, Peixin"'

2. An Industrial Robot Path Planning Method Based on Improved Whale Optimization Algorithm

Author

Huang, Peixin, Dong, Chen, Chen, Zhenyi, Zhen, Zihang, Jiang, Lei, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Jin, Hai, editor, Yu, Zhiwen, editor, Yu, Chen, editor, Zhou, Xiaokang, editor, Lu, Zeguang, editor, and Song, Xianhua, editor

Published
2024
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14. Learning Event Representations for Zero-Shot Detection via Dual-Contrastive Prompting.

Author

Li, Jiaxu, Ge, Bin, Xu, Hao, Huang, Peixin, and Huang, Hongbin

Subjects
LANGUAGE models, AUTOMATIC classification
Abstract

Zero-shot event detection aims to involve the automatic discovery and classification of new events within unstructured text. Current zero-shot event detection methods have not considered addressing the problem more effectively from the perspective of improving event representations. In this paper, we propose dual-contrastive prompting (COPE) model for learning event representations to address zero-shot event detection, which leverages prompts to assist in generating event embeddings using a pretrained language model, and employs a contrastive fusion approach to capture complex interaction information between trigger representations and sentence embeddings to obtain enhanced event representations. Firstly, we introduce a sample generator to create ordered contrastive sample sequences with varying degrees of similarity for each event instance, aiding the model in better distinguishing different types of events. Secondly, we design two distinct prompts to obtain trigger representations and event sentence embeddings separately. Thirdly, we employ a contrastive fusion module, where trigger representations and event sentence embeddings interactively fuse in vector space to generate the final event representations. Experiments show that our model is more effective than the most advanced methods. [ABSTRACT FROM AUTHOR]

Published
2024
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17. CRISPR-Cas9 Engineered Extracellular Vesicles for the Treatment of Dominant Progressive Hearing Loss

Author

Pan, Xiaoshu, primary, Huang, Peixin, additional, Ali, Samantha S., additional, Renslo, Bryan, additional, Hutchinson, Tarun E, additional, Erwin, Nina, additional, Greenberg, Zachary, additional, Ding, Zuo, additional, Li, Yanjun, additional, Warnecke, Athanasia, additional, Fernandez, Natalia E., additional, Staecker, Hinrich, additional, and He, Mei, additional

Published
2023
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23. An equilibrium honey badger algorithm with differential evolution strategy for cluster analysis.

Author

Huang, Peixin, Luo, Qifang, Wei, Yuanfei, and Zhou, Yongquan

Subjects
*CLUSTER analysis (Statistics), *BADGERS, *ALGORITHMS, *K-means clustering, *DATA mining, *DIFFERENTIAL evolution, *SWARM intelligence
Abstract

Data clustering is a machine learning method for unsupervised learning that is popular in the two areas of data analysis and data mining. The objective is to partition a given dataset into distinct clusters, aiming to maximize the similarity among data objects within the same cluster. In this paper, an improved honey badger algorithm called DELHBA is proposed to solve the clustering problem. In DELHBA, to boost the population's diversity and the performance of global search, the differential evolution method is incorporated into algorithm's initial step. Secondly, the equilibrium pooling technique is included to assist the standard honey badger algorithm (HBA) break free of the local optimum. Finally, the updated honey badger population individuals are updated with Levy flight strategy to produce more potential solutions. Ten famous benchmark test datasets are utilized to evaluate the efficiency of the DELHBA algorithm and to contrast it with twelve of the current most used swarm intelligence algorithms and k-means. Additionally, DELHBA algorithm's performance is assessed using the Wilcoxon rank sum test and Friedman's test. The experimental results show that DELHBA has better clustering accuracy, convergence speed and stability compared with other algorithms, demonstrating its superiority in solving clustering problems. [ABSTRACT FROM AUTHOR]

Published
2023
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44. Joint Event Extraction with Hierarchical Policy Network

Author

Huang Peixin, Weidong Xiao, Xiang Zhao, Zhen Tan, and Ryuichi Takanobu

Subjects
Structure (mathematical logic), Theoretical computer science, Event (computing), Process (engineering), Computer science, 010102 general mathematics, 02 engineering and technology, 01 natural sciences, Argument, 0202 electrical engineering, electronic engineering, information engineering, Natural (music), 020201 artificial intelligence & image processing, Joint (building), 0101 mathematics
Abstract

Most existing work on event extraction (EE) either follows a pipelined manner or uses a joint structure but is pipelined in essence. As a result, these efforts fail to utilize information interactions among event triggers, event arguments, and argument roles, which causes information redundancy. In view of this, we propose to exploit the role information of the arguments in an event and devise a Hierarchical Policy Network (HPNet) to perform joint EE. The whole EE process is fulfilled through a two-level hierarchical structure consisting of two policy networks for event detection and argument detection. The deep information interactions among the subtasks are realized, and it is more natural to deal with multiple events issue. Extensive experiments on ACE2005 and TAC2015 demonstrate the superiority of HPNet, leading to state-of-the-art performance and is more powerful for sentences with multiple events.

Published
2020

50. Enriched taxa were found among the gut microbiota of centenarians in East China

Author

Wang, Na, primary, Li, Rui, additional, Lin, Haijiang, additional, Fu, Chaowei, additional, Wang, Xuecai, additional, Zhang, Yiming, additional, Su, Meifang, additional, Huang, Peixin, additional, Qian, Junhua, additional, Jiang, Feng, additional, Wang, Hexing, additional, Jiang, Lufang, additional, Yu, Xin, additional, Liu, Jianxiang, additional, Chen, Yue, additional, and Jiang, Qingwu, additional

Published
2019
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