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687 results on '"Xu, Chunlin"'

1. Multi-heterojunctioned plastics with high thermoelectric figure of merit

Author

Wang, Dongyang, Ding, Jiamin, Ma, Yingqiao, Xu, Chunlin, Li, Zhiyi, Zhang, Xiao, Zhao, Yao, Zhao, Yue, Di, Yuqiu, Liu, Liyao, Dai, Xiaojuan, Zou, Ye, Kim, BongSoo, Zhang, Fengjiao, Liu, Zitong, McCulloch, Iain, Lee, Myeongjae, Chang, Cheng, Yang, Xiao, Wang, Dong, Zhang, Deqing, Zhao, Li-Dong, Di, Chong-an, and Zhu, Daoben

Published
2024
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4. Genome-wide variation study and inter-tissue communication analysis unveil regulatory mechanisms of egg-laying performance in chickens

Author

Wang, Dandan, Tan, Lizhi, Zhi, Yihao, Bu, Lina, Wang, Yangyang, Wang, Zhang, Guo, Yulong, Tian, Weihua, Xu, Chunlin, Li, Donghua, Li, Zhuanjian, Jiang, Ruirui, Han, Ruili, Li, Guoxi, Wang, Yongqiang, Xia, Dong, Tian, Yadong, Dunn, Ian C., Hu, Xiaoxiang, Li, Hong, Zhao, Yiqiang, Kang, Xiangtao, and Liu, Xiaojun

Published
2024
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10. OH-Former: Omni-Relational High-Order Transformer for Person Re-Identification

Author

Chen, Xianing, Xu, Chunlin, Cao, Qiong, Xu, Jialang, Zhong, Yujie, Xu, Jiale, Li, Zhengxin, Wang, Jingya, and Gao, Shenghua

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Transformers have shown preferable performance on many vision tasks. However, for the task of person re-identification (ReID), vanilla transformers leave the rich contexts on high-order feature relations under-exploited and deteriorate local feature details, which are insufficient due to the dramatic variations of pedestrians. In this work, we propose an Omni-Relational High-Order Transformer (OH-Former) to model omni-relational features for ReID. First, to strengthen the capacity of visual representation, instead of obtaining the attention matrix based on pairs of queries and isolated keys at each spatial location, we take a step further to model high-order statistics information for the non-local mechanism. We share the attention weights in the corresponding layer of each order with a prior mixing mechanism to reduce the computation cost. Then, a convolution-based local relation perception module is proposed to extract the local relations and 2D position information. The experimental results of our model are superior promising, which show state-of-the-art performance on Market-1501, DukeMTMC, MSMT17 and Occluded-Duke datasets.

Published
2021

19. The European Polysaccharide Network of Excellence (EPNOE) research roadmap 2040: Advanced strategies for exploiting the vast potential of polysaccharides as renewable bioresources

Author

Gericke, Martin, Amaral, Adérito J.R., Budtova, Tatiana, De Wever, Pieter, Groth, Thomas, Heinze, Thomas, Höfte, Herman, Huber, Anton, Ikkala, Olli, Kapuśniak, Janusz, Kargl, Rupert, Mano, João F., Másson, Már, Matricardi, Pietro, Medronho, Bruno, Norgren, Magnus, Nypelö, Tiina, Nyström, Laura, Roig, Anna, Sauer, Michael, Schols, Henk A., van der Linden, John, Wrodnigg, Tanja M., Xu, Chunlin, Yakubov, Gleb E., Stana Kleinschek, Karin, and Fardim, Pedro

Published
2024
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20. Evaluating the Effectiveness of Web Search Engines on Results Diversification

Author

Wu, Shengli, Zhang, Zhongmin, and Xu, Chunlin

Abstract

Introduction: Recently, the problem of diversification of search results has attracted a lot of attention in the information retrieval and Web search research community. For multi-faceted or ambiguous queries, a search engine is generally favoured if it is able to identify relevant documents on a wider range of different aspects. Method: We evaluate the performance of three major Web search engines: Google, Bing and Ask manually using 200 multi-faceted or ambiguous queries from TREC. Analysis: Both classical metrics and intent-aware metrics are used to evaluate search results. Results: Experimental results show that on average Bing and Google are comparable and Ask is slightly worse than the former two. However, Ask does very well in one subtype of queries -- ambiguous queries. The average performance of the three search engines is better than the average of the top two runs submitted to the TREC web diversity task in 2009-2012. Conclusions: Generally, all three Web search engines do well, this indicates that all of them must use state-of-the-art technology to support the diversification of search results.

Published
2019

25. Neural networks for text matching

Author

Xu, Chunlin and Wu, Shengli

Subjects
Text matching, Text classification, Natural language processing
Abstract

Text matching is a task of comparing two texts to identify their relationship. It is an important component of a variety of natural language processing (NLP) tasks. Recently, neural networks provide a new paradigm for text matching. In this thesis, we seek to improve text matching in the following two perspectives: (1) text representation and (2) text interaction. Tree-Structured Long Short-Term Memory (TreeLSTM) has shown its effectiveness in text representation. To further improve the expressive power of TreeLSTM, we propose two new text representation models: TreeLSTM with Tag-aware Hypernetwork (TagHyperTreeLSTM) and Tag-Enhanced Dynamic Compositional Neural Network (TE_DCNN), respectively. These two models are both devised to alleviate the inability of distinguishing different syntactic compositions of standard TreeLSTM with the aid of Part-of-Speech (POS) tags. TagHyperTreeLSTM contains two separate TreeLSTMs, a tag-aware hypernetwork TreeLSTM to generate parameters of the sentence encoder TreeLSTM dynamically, and a sentence encoder TreeLSTM to generate the final sentence representation. TE_DCNN shares similar framework with TagHyperTreeLSTM, but it extends the standard TreeLSTM with binarized constituency tree to a novel structure, named ARTreeLSTM with general constituency tree in which non-leaf nodes can have any number of child nodes. In addition, the ability of capturing matching features between two texts is of great significance for improving text matching performance. Two new interaction-based matching models are proposed in this thesis: Multi-Level Compare-Aggregate model (MLCA) and Multi-Level Matching Network (MMN). MLCA matches each word in one text against the other text at three different levels of granularity - word level (word-by-word matching), phrase level (word-by-phrase matching) and sentence level (word-by-sentence matching).MMN utilises multiple levels of word representations such as word embedding, contextualized word representation, to obtain multiple word level matching results for final text level matching decision. In summary, this thesis proposes four novel neural network based models for text matching. As text matching is a fundamental operation in various NLP tasks and applications including paraphrase identification, natural language inference, machine comprehension, information retrieval and so on, we believe that models presented in this thesis would promote the performance of the above NLP tasks and applications containing text matching. Moreover, text representation models described in this thesis would also be helpful for some other NLP applications such as text classification.

Published
2021

29. Data Fusion Methods with Graded Relevance Judgment

Author

Huang, Yidong, Xu, Qiuyu, Liu, Yao, Xu, Chunlin, Wu, Shengli, 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, Zhao, Xiang, editor, Yang, Shiyu, editor, Wang, Xin, editor, and Li, Jianxin, editor

Published
2022
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30. Laccase-Catalyzed Copolymerization of Tannin and Technical Lignin: Polymerization Kinetics and Enhanced Properties of Polyphenols

Author

Lu, Zonghong, primary, Tienaho, Jenni, additional, Kilpeläinen, Petri, additional, Wang, Luyao, additional, Zhang, Hao, additional, Hu, Liqiu, additional, Liu, Rui, additional, Brännström, Hanna, additional, Halmemies, Eelis, additional, Wang, Xiaoju, additional, Toivakka, Martti, additional, and Xu, Chunlin, additional

Published
2024
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31. Suberin-Based Aqueous Dispersions for Barrier Packaging Applications

Author

Hu, Liqiu, primary, Koppolu, Rajesh, additional, Hämäläinen, Roosa, additional, Kanerva, Heimo, additional, Nick, Tapani, additional, Toivakka, Martti, additional, Korpinen, Risto, additional, Saranpää, Pekka, additional, Qasim, Umair, additional, Liimatainen, Henrikki, additional, Xu, Chunlin, additional, and Anghelescu-Hakala, Adina, additional

Published
2024
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36. Composite Hydrogels with Rapid Self-Healing, Stretchable, Moldable and Antibacterial Properties Based on PVA/ε-Poly-l-lysine/Hyaluronic Acid.

Author

Sun, Na, Liu, Xiangnan, Lv, Wenqi, Xu, Chunlin, Zhang, Ailing, and Sun, Panpan

Subjects
FOURIER transform infrared spectroscopy, ESCHERICHIA coli, BIOMEDICAL materials, ELECTROSTATIC interaction, HYDROGEN bonding, HYDROGELS
Abstract

Self-healing, stretchable, and moldable hydrogels have a great potential application in tissue engineering and soft robotics. Despite great success in reported hydrogels, it is still a great challenge to construct the moldable hydrogels with an ultrafast self-healing performance. Herein, the composite hydrogels (PBLH) with ultrafast self-healing, stretchable, and moldable properties were successfully constructed by poly (vinyl alcohol) (PVA), borate (B), ε-poly-l-lysine (EPL), and hyaluronic acid (HA) based on an efficient one-pot method. Fourier transform infrared spectroscopy, X-ray diffraction, and rheological measurements confirmed the formation of a dynamic network among PVA, B, EPL, and HA through the cross-linking of dynamic borate bonds, electrostatic interaction, and hydrogen bonding. Having fabricated the dynamic network structure, the damage gap of the composite hydrogels can heal within 1 min, presenting an excellent self-healing ability. Simultaneously, the composite hydrogels can be molded into various shapes, and the length of the composite hydrogels can be stretched to 15 times their original length. In addition, the composite hydrogels exhibited an excellent antibacterial property against Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli). Our results illustrated that the composite hydrogels not only retain the advantages of traditional hydrogels but also possess ultrafast self-healing, outstanding stretchable and antibacterial properties, presenting a prospective candidate for constructing biomedical materials. [ABSTRACT FROM AUTHOR]

Published
2024
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45. Tunable a temperature-dependent GST-based metamaterial absorber for switching and sensing applications

Author

Guo Linyang, Ma Xiaohui, Chang Zhaoqing, Xu Chunlin, Liao Jun, and Zhang Ran

Subjects
Metamaterials, Metasurface, Absorption peak, Mining engineering. Metallurgy, TN1-997
Abstract

The development of temperature-sensitive tunable metamaterials based on GST particles is one of the hot topics. These metamaterials have rich resonance properties and can be used in communication, optical control, coding, and other fields. However, it is still elusive how to achieve the tunability of metamaterials based on GST particles while maintaining the original resonance performance. In this paper, a tunable Terahertz (THz) metamaterial based on GST particles is verified and measured, which can maintain the original resonances with temperature increasing. Two absorption peaks (P1: 86% at 1.98 THz, P2: 51% at 5.88 THz) and a valley (V1) are achieved by this metamaterial at room temperature. The P1, P2, and V1 of the samples are temperature-sensitive, which are shifted to lower frequencies with temperature increasing. The maximum FOM value is 7651, and the maximum S value is 2190. Moreover, the resonance position of the P2 is always covered by the V1 with the temperature increasing. Therefore, this proposed temperature-sensitive tunable metamaterial realizes the absorption window conversion between P2 peak (or “ON”) and V1 valley (or “OFF”). This proposed metamaterial can be applied to crystallinity sensing.

Published
2021
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