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5,544 results on '"Chen, Na"'

7. Design of Automotive Interior and Human-Computer Interaction for Time-Sharing Rental: A Chinese Study

Author

Yang, Hao, Chen, Na, Jin, Quanxin, Zhao, Ying, 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, Harris, Don, editor, Li, Wen-Chin, editor, and Krömker, Heidi, editor

Published
2025
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18. Insights into the assembly of the neovaginal microbiota in Mayer-Rokitansky-Küster-Hauser (MRKH) syndrome patients

Author

Chen, Na, Hao, Lilan, Zhang, Zhe, Qin, Chenglu, Jie, Zhuye, Pan, Hongxin, Duan, Jiali, Huang, Xincheng, Zhang, Yunhong, Gao, Hongqin, Lu, Ruike, Sun, Tianshu, Yang, Hua, Shi, Jinqiu, Liang, Maolian, Guo, Jianbin, Gao, Qianqian, Zhao, Xiaoyue, Dou, Zhiyuan, Xiao, Liang, Zhang, Shaoqiao, Jin, Xin, Xu, Xun, Yang, Huanming, Wang, Jian, Jia, Huijue, Zhang, Tao, Kristiansen, Karsten, Chen, Chen, and Zhu, Lan

Published
2024
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31. Few-shot Image Classification based on Gradual Machine Learning

Author

Chen, Na, Kuang, Xianming, Liu, Feiyu, Wang, Kehao, and Chen, Qun

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Few-shot image classification aims to accurately classify unlabeled images using only a few labeled samples. The state-of-the-art solutions are built by deep learning, which focuses on designing increasingly complex deep backbones. Unfortunately, the task remains very challenging due to the difficulty of transferring the knowledge learned in training classes to new ones. In this paper, we propose a novel approach based on the non-i.i.d paradigm of gradual machine learning (GML). It begins with only a few labeled observations, and then gradually labels target images in the increasing order of hardness by iterative factor inference in a factor graph. Specifically, our proposed solution extracts indicative feature representations by deep backbones, and then constructs both unary and binary factors based on the extracted features to facilitate gradual learning. The unary factors are constructed based on class center distance in an embedding space, while the binary factors are constructed based on k-nearest neighborhood. We have empirically validated the performance of the proposed approach on benchmark datasets by a comparative study. Our extensive experiments demonstrate that the proposed approach can improve the SOTA performance by 1-5% in terms of accuracy. More notably, it is more robust than the existing deep models in that its performance can consistently improve as the size of query set increases while the performance of deep models remains essentially flat or even becomes worse., Comment: 17 pages,6 figures,5 tables, 55 conferences

Published
2023

37. Gate-tunable negative refraction of mid-infrared polaritons

Author

Hu, Hai, Chen, Na, Teng, Hanchao, Yu, Renwen, Xue, Mengfei, Chen, Ke, Xiao, Yuchuan, Qu, Yunpeng, Hu, Debo, Chen, Jianing, Sun, Zhipei, Li, Peining, de Abajo, F. Javier García, and Dai, Qing

Subjects
Physics - Optics
Abstract

Negative refraction provides an attractive platform to manipulate mid-infrared and terahertz radiation for molecular sensing and thermal radiation applications. However, its implementation based on available metamaterials and plasmonic media presents challenges associated with optical losses, limited spatial confinement, and lack of active tunability in this spectral range. Here, we demonstrate gate-tunable negative refraction at mid-infrared frequencies using hybrid topological polaritons in van der Waals heterostructures with high spatial confinement. We experimentally visualize wide-angle negatively-refracted surface polaritons on {\alpha}-MoO3 films partially decorated with graphene, undergoing planar nanoscale focusing down to 1.6% of the free-space wavelength. Our atomically thick heterostructures outperform conventional bulk materials by avoiding scattering losses at the refracting interface while enabling active tunability through electrical gating. We propose polaritonic negative refraction as a promising platform for infrared applications such as electrically tunable super-resolution imaging, nanoscale thermal manipulation, and molecular sensing.

Published
2022
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