Search

Your search keyword '"Xu, Muxin"' showing total 11 results
Start Over Author "Xu, Muxin"

Refine your results

more »

more »

11 results on '"Xu, Muxin"'

1. Oriented Classroom Instructional Behavior Recognition Benchmark

Author

Xu, Muxin, Shen, Yingshan, Wang, Sizhu, Yuan, Xiaofeng, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, van Leeuwen, Jan, Series Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Kobsa, Alfred, Series Editor, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Nierstrasz, Oscar, Series Editor, Pandu Rangan, C., Editorial Board Member, Sudan, Madhu, Series Editor, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Vardi, Moshe Y, Series Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Kubincová, Zuzana, editor, Hao, Tianyong, editor, Capuano, Nicola, editor, Temperini, Marco, editor, Ge, Shili, editor, Mu, Yuanyuan, editor, Fantozzi, Paolo, editor, and Yang, Jing, editor

Published
2024
Full Text
View/download PDF

2. Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition

Author

Chen, Tianshui, Xu, Muxin, Hui, Xiaolu, Wu, Hefeng, and Lin, Liang

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Recognizing multiple labels of images is a practical and challenging task, and significant progress has been made by searching semantic-aware regions and modeling label dependency. However, current methods cannot locate the semantic regions accurately due to the lack of part-level supervision or semantic guidance. Moreover, they cannot fully explore the mutual interactions among the semantic regions and do not explicitly model the label co-occurrence. To address these issues, we propose a Semantic-Specific Graph Representation Learning (SSGRL) framework that consists of two crucial modules: 1) a semantic decoupling module that incorporates category semantics to guide learning semantic-specific representations and 2) a semantic interaction module that correlates these representations with a graph built on the statistical label co-occurrence and explores their interactions via a graph propagation mechanism. Extensive experiments on public benchmarks show that our SSGRL framework outperforms current state-of-the-art methods by a sizable margin, e.g. with an mAP improvement of 2.5%, 2.6%, 6.7%, and 3.1% on the PASCAL VOC 2007 & 2012, Microsoft-COCO and Visual Genome benchmarks, respectively. Our codes and models are available at https://github.com/HCPLab-SYSU/SSGRL., Comment: accepted by ICCV 2019

Published
2019

4. A Novel EEG-Based Depression Detection Framework

Author

Shen, Yingshan, Xu, Muxin, Fan, Xiaomao, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sun, Xingming, editor, Zhang, Xiaorui, editor, Xia, Zhihua, editor, and Bertino, Elisa, editor

Published
2022
Full Text
View/download PDF

11. Fluoride-resistant Streptococcus mutans within cross-kingdom biofilms support Candida albicans growth under fluoride and attenuate the in vitro anti-caries effect of fluorine.

Author

Sun Y, Chen Y, Du Q, Zhang J, Xu M, Zheng G, Zhou W, Zhou X, Qiu L, Pan Y, and Zhang K

Abstract

Fluoride-resistant Streptococcus mutans ( S. mutans ) might affect the ecological balance of biofilms in the presence of fluoride. We used a S. mutans and Candida albicans ( C. albicans ) cross-kingdom biofilm model to investigate whether fluoride-resistant S. mutans in biofilms would support C. albicans growth under fluoride stress and attenuate the in vitro anti-caries effect of fluorine. The impact of fluoride-resistant S. mutans on formation of cross-kingdom biofilms by S. mutans and C. albicans in the presence of fluoride was investigated in vitro using the crystal violet staining assay. Biofilm constitution was determined using colony-forming unit (CFU) counts and fluorescent in situ hybridization (FISH). Extracellular polysaccharide (EPS) generation in biofilms was determined by EPS/bacterial dying and water-insoluble polysaccharide detection. Acid production and demineralization were monitored using pH, lactic acid content, and transversal microradiography (TMR). The gene expression of microorganisms in the cross-kingdom biofilm was measured using qRT-PCR. Our results showed that both C. albicans and fluoride-resistant S. mutans grew vigorously, forming robust cross-kingdom biofilms, even in the presence of sodium fluoride (NaF). Moreover, fluoride-resistant S. mutans -containing cross-kingdom biofilms had considerable cariogenic potential for EPS synthesis, acid production, and demineralization ability in the presence of NaF than fluoride-sensitive S. mutans -containing biofilms. Furthermore, the gene expression of microorganisms in the two cross-kingdom biofilms changed dissimilarly in the presence of NaF. In summary, fluoride-resistant S. mutans in cross-kingdom biofilms supported C. albicans growth under fluoride and might attenuate the anti-caries potential of fluorine by maintaining robust cross-kingdom biofilm formation and cariogenic virulence expression in vitro in the presence of NaF., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Sun, Chen, Du, Zhang, Xu, Zheng, Zhou, Zhou, Qiu, Pan and Zhang.)

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results