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2,400 results on '"Tang, Sheng"'

1. Experimental Quantum Byzantine Agreement on a Three-User Quantum Network with Integrated Photonics

Author

Jing, Xu, Qian, Cheng, Weng, Chen-Xun, Li, Bing-Hong, Chen, Zhe, Wang, Chen-Quan, Tang, Jie, Gu, Xiao-Wen, Kong, Yue-Chan, Chen, Tang-Sheng, Yin, Hua-Lei, Jiang, Dong, Niu, Bin, and Lu, Liang-Liang

Subjects
Quantum Physics
Abstract

Quantum communication networks are crucial for both secure communication and cryptographic networked tasks. Building quantum communication networks in a scalable and cost-effective way is essential for their widespread adoption, among which a stable and miniaturized high-quality quantum light source is a key component. Here, we establish a complete polarization entanglement-based fully connected network, which features an ultrabright integrated Bragg reflection waveguide quantum source, managed by an untrusted service provider, and a streamlined polarization analysis module, which requires only one single-photon detector for each end user. We perform a continuously working quantum entanglement distribution and create correlated bit strings between users. Within the framework of one-time universal hashing, we provide the first experimental implementation of source-independent quantum digital signatures using imperfect keys circumventing the necessity for private amplification. More importantly, we further beat the 1/3 fault-tolerance bound in Byzantine agreement, achieving unconditional security without relying on sophisticated techniques. Our results offer an affordable and practical route for addressing consensus challenges within the emerging quantum network landscape.

Published
2024
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5. Topology-preserving Adversarial Training for Alleviating Natural Accuracy Degradation

Author

Mi, Xiaoyue, Tang, Fan, Weng, Yepeng, Wang, Danding, Cao, Juan, Tang, Sheng, Li, Peng, and Liu, Yang

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Despite the effectiveness in improving the robustness of neural networks, adversarial training has suffered from the natural accuracy degradation problem, i.e., accuracy on natural samples has reduced significantly. In this study, we reveal that natural accuracy degradation is highly related to the disruption of the natural sample topology in the representation space by quantitative and qualitative experiments. Based on this observation, we propose Topology-pReserving Adversarial traINing (TRAIN) to alleviate the problem by preserving the topology structure of natural samples from a standard model trained only on natural samples during adversarial training. As an additional regularization, our method can be combined with various popular adversarial training algorithms, taking advantage of both sides. Extensive experiments on CIFAR-10, CIFAR-100, and Tiny ImageNet show that our proposed method achieves consistent and significant improvements over various strong baselines in most cases. Specifically, without additional data, TRAIN achieves up to 8.86% improvement in natural accuracy and 6.33% improvement in robust accuracy., Comment: BMVC 2024; Code will be released on https://github.com/KululuMi/TRAIN

Published
2023

6. Dance Your Latents: Consistent Dance Generation through Spatial-temporal Subspace Attention Guided by Motion Flow

Author

Fang, Haipeng, Sun, Zhihao, Huang, Ziyao, Tang, Fan, Cao, Juan, and Tang, Sheng

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The advancement of generative AI has extended to the realm of Human Dance Generation, demonstrating superior generative capacities. However, current methods still exhibit deficiencies in achieving spatiotemporal consistency, resulting in artifacts like ghosting, flickering, and incoherent motions. In this paper, we present Dance-Your-Latents, a framework that makes latents dance coherently following motion flow to generate consistent dance videos. Firstly, considering that each constituent element moves within a confined space, we introduce spatial-temporal subspace-attention blocks that decompose the global space into a combination of regular subspaces and efficiently model the spatiotemporal consistency within these subspaces. This module enables each patch pay attention to adjacent areas, mitigating the excessive dispersion of long-range attention. Furthermore, observing that body part's movement is guided by pose control, we design motion flow guided subspace align & restore. This method enables the attention to be computed on the irregular subspace along the motion flow. Experimental results in TikTok dataset demonstrate that our approach significantly enhances spatiotemporal consistency of the generated videos., Comment: 10 pages, 5 figures

Published
2023

8. 10B-doped MCP detector developed for neutron resonance imaging at Back-n white neutron source

Author

Li, Qiang, Wang, Li-Jiao, Tang, Jing-Yu, Qiu, Xiang-Biao, Chen, Zhen, Zhao, Mao-Yuan, Ning, Chang-Jun, Pan, Kai, Xu, Wei, Li, Tao, Lu, Su-Peng, Yi, Han, Fan, Rui-Rui, Feng, Chang-Qing, Zhang, Rong, Sun, Xiao-Yang, An, Qi, Bai, Hao-Fan, Bai, Jiang-Bo, Bao, Jie, Cao, Ping, Chen, Qi-Ping, Chen, Yong-Hao, Cui, Zeng-Qi, Fan, An-Chuan, Feng, Fan-Zhen, Gu, Min-Hao, Han, Chang-Cai, Han, Zi-Jie, He, Guo-Zhu, He, Yong-Cheng, Hong, Yang, Hu, Yi-Wei, Huang, Han-Xiong, Jiang, Wei, Jiang, Zhi-Jie, Jin, Zheng-Yao, Kang, Ling, Li, Bo, Li, Gong, Li, Xiao, Li, Yang, Liu, Jie, Liu, Rong, Liu, Shu-Bin, Liu, Yi-Na, Luan, Guang-Yuan, Ren, Jie, Ren, Zhi-Zhou, Ruan, Xi-Chao, Song, Zhao-Hui, Sun, Kang, Tan, Zhi-Xin, Tang, Sheng-Da, Wang, Jin-Cheng, Wang, Peng-Cheng, Wang, Zhao-Hui, Wen, Zhong-Wei, Wu, Xiao-Guang, Wu, Xuan, Xia, Cong, Yu, Yong-Ji, Zhang, Guo-Hui, Zhang, Hang-Chang, Zhang, Lin-Hao, Zhang, Qi-Wei, Zhang, Xian-Peng, Zhang, Yu-Liang, Zhang, Yue, Zhang, Zhi-Yong, Zhou, Zhi-Hao, Zhu, Ke-Jun, and Zou, Chong

Published
2024
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9. Study of the response of 10B-doped MCP to wide-energy range neutrons from eV to MeV

Author

Li, Qiang, Wang, Li-Jiao, Qiu, Xiang-Biao, Li, Jing-Wen, Xu, Wei, Li, Tao, Lin, Ze-Bin, Ning, Chang-Jun, Chen, Yong-Hao, Fan, Rui-Rui, Sun, Kang, Tang, Jing-Yu, Zhang, Rong, Jing, Han-Tao, Mei, Bo, An, Qi, Bai, Hao-Fan, Bai, Jiang-Bo, Bao, Jie, Cao, Ping, Chen, Qi-Ping, Chen, Zhen, Cui, Zeng-Qi, Fan, An-Chuan, Feng, Chang-Qing, Feng, Fan-Zhen, Gao, Ke-Qing, Gu, Min-Hao, Han, Chang-Cai, Han, Zi-Jie, He, Guo-Zhu, He, Yong-Cheng, Hong, Yang, Hu, Yi-Wei, Huang, Han-Xiong, Jia, Wei-Hua, Jiang, Hao-Yu, Jiang, Wei, Jiang, Zhi-Jie, Jin, Zheng-Yao, Kang, Ling, Li, Bo, Li, Chao, Li, Gong, Li, Jia-Wen, Li, Xiao, Li, Yang, Liu, Jie, Liu, Rong, Liu, Shu-Bin, Luan, Guang-Yuan, Qi, Bin-Bin, Ren, Jie, Ren, Zhi-Zhou, Ruan, Xi-Chao, Song, Zhao-Hui, Tan, Zhi-Xin, Tang, Sheng-Da, Wang, Peng-Cheng, Wang, Zhao-Hui, Wen, Zhong-Wei, Wu, Xiao-Guang, Wu, Xuan, Xie, Li-Kun, Yang, Yi-Wei, Yi, Han, Yu, Yong-Ji, Zhang, Guo-Hui, Zhang, Lin-Hao, Zhang, Mo-Han, Zhang, Qi-Wei, Zhang, Xian-Peng, Zhang, Yu-Liang, Zhang, Yue, Zhang, Zhi-Yong, Zhao, Mao-Yuan, Zhou, Lu-Ping, Zhou, Zhi-Hao, and Zhu, Ke-Jun

Published
2024
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11. Progressive Open Space Expansion for Open-Set Model Attribution

Author

Yang, Tianyun, Wang, Danding, Tang, Fan, Zhao, Xinying, Cao, Juan, and Tang, Sheng

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Despite the remarkable progress in generative technology, the Janus-faced issues of intellectual property protection and malicious content supervision have arisen. Efforts have been paid to manage synthetic images by attributing them to a set of potential source models. However, the closed-set classification setting limits the application in real-world scenarios for handling contents generated by arbitrary models. In this study, we focus on a challenging task, namely Open-Set Model Attribution (OSMA), to simultaneously attribute images to known models and identify those from unknown ones. Compared to existing open-set recognition (OSR) tasks focusing on semantic novelty, OSMA is more challenging as the distinction between images from known and unknown models may only lie in visually imperceptible traces. To this end, we propose a Progressive Open Space Expansion (POSE) solution, which simulates open-set samples that maintain the same semantics as closed-set samples but embedded with different imperceptible traces. Guided by a diversity constraint, the open space is simulated progressively by a set of lightweight augmentation models. We consider three real-world scenarios and construct an OSMA benchmark dataset, including unknown models trained with different random seeds, architectures, and datasets from known ones. Extensive experiments on the dataset demonstrate POSE is superior to both existing model attribution methods and off-the-shelf OSR methods., Comment: accepted to CVPR2023

Published
2023

16. Research on the Digital Economy Correlation Pattern of the Pearl River Delta Urban Agglomeration Based on Enterprise Organization Relationship

Author

Gu Bin, Wu Kunyan, Tang Sheng, Zhou Tianyi, and Wang Yanli

Subjects
interlocking network model, social network analysis, digital economics, listed enterprise, pearl river delta urban agglomeration, Geography (General), G1-922
Abstract

Using the Pearl River Delta urban agglomeration as the research area, this study comprehensively uses an interlocking network model and social network analysis to construct a network based on the headquarters branch data of digital economy listed enterprises in 2005, 2010, 2015, and 2020 to analyze the digital economy correlation pattern of the Pearl River Delta urban agglomeration. The results show that from 2005 to 2020, the number of digital economy listed companies' headquarters and branches in the Pearl River Delta urban agglomeration has grown rapidly. The radiating influence of headquarters has gradually expanded, and cross-city connections among digital economy companies have strengthened. Shenzhen and Guangzhou have become the engines of digital economy development and the control centers of the correlation network. The overall correlation structure of the urban agglomeration transformed from a single triangle consisting of Guangzhou-Zhuhai-Shenzhen and the connecting cities in 2005 to a double triangle structure consisting of Guangzhou-Foshan-Shenzhen and Guangzhou-Zhuhai-Shenzhen and the connecting cities in 2020. The network density increased, the asymmetry weakened, and the agglomeration subgroups shifted from a single regional center to multiple regional centers. From an industrial perspective, there are significant disparities among the different sectors of the digital economy. Network connections were strongest in the information transmission, software, and information technology service industries, weakest in finance, and moderate in manufacturing. The dual-core status of Shenzhen and Guangzhou is prominent, whereas Zhaoqing and Jiangmen are on the network periphery. This study examines the formation and evolution of the digital economy network of the Pearl River Delta urban agglomeration from the perspective of corporate organizational connections, identifies the positions and roles of each city in the correlation network, analyzes the correlation pattern and evolution path of the digital economy, and expands the study of digital economy correlation from an industry perspective, revealing industry differences in the digital economy correlation of urban agglomerations. The characteristics of the regional digital economy correlation can be summarized based on the results of this study and the existing research. First, the degree of regional digital economy correlation continuously increases during the development of the digital economy, and the overall network connection is strengthened. Second, differentiation emerges in the digital economy correlation network, forming core and peripheral cities and displaying spatial distribution imbalances. This study also found that policy guidance may promote the formation of multiple centers in the region and contribute to the balanced development of the correlation network. Finally, the regional digital economy correlation exhibits industry differences due to industry foundations and development characteristics. To promote sustained and healthy development of the digital economy in the Pearl River Delta urban agglomeration, it is necessary to further enhance inter-city digital economy cooperation, better leverage the radiating driving effects of core cities, such as Shenzhen and Guangzhou, improve digital economy development policies and infrastructure in peripheral cities, and promote coordinated development of the overall digital economy in the urban agglomeration. Comprehensive planning of the industry layout is necessary in subdivided industries to promote balanced development across all sectors and form a concerted effort in digital economy development. These measures can also serve as a reference for other areas in developing digital economies.

Published
2024
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17. Nondestructive technique for identifying nuclides using neutron resonance transmission analysis at CSNS Back-n

Author

Tang, Sheng-Da, Chen, Yong-Hao, Tang, Jing-Yu, Fan, Rui-Rui, Li, Qiang, Li, Gong, Liu, Dong, Jin, Zheng-Yao, Cui, Xing-Zhu, Chen, Tian-Xiang, Yang, Yi-Wei, Liu, Rong, Yi, Han, Li, Yang, Yang, Zhen, An, Qi, Bai, Hao-Fan, Bai, Jiang-Bo, Bao, Jie, Cao, Ping, Chen, Qi-Ping, Chen, Zhen, Cui, Zeng-Qi, Fan, An-Chuan, Feng, Chang-Qing, Feng, Fan-Zhen, Gao, Ke-Qing, Gu, Min-Hao, Han, Chang-Cai, Han, Zi-Jie, He, Guo-Zhu, He, Yong-Cheng, Hong, Yang, Hu, Yi-Wei, Huang, Han-Xiong, Jia, Wei-Hua, Jiang, Hao-yu, Jiang, Wei, Jiang, Zhi-jie, Kang, Ling, Li, Bo, Li, Chao, Li, Jia-Wen, Li, Xiao, Liu, Jie, Liu, Shu-Bin, Luan, Guang-Yuan, Ning, Chang-Jun, Qi, Bin-Bin, Ren, Jie, Ren, Zhi-Zhou, Ruan, Xi-Chao, Song, Zhao-Hui, Sun, Kang, Tan, Zhi-Xin, Wang, Li-Jiao, Wang, Peng-Cheng, Wang, Zhao-Hui, Wen, Zhong-Wei, Wu, Xiao-Guang, Wu, Xuan, Xie, Li-Kun, Yu, Yong-Ji, Zhang, Guo-Hui, Zhang, Lin-Hao, Zhang, Mo-Han, Zhang, Qi-Wei, Zhang, Xian-Peng, Zhang, Yu-Liang, Zhang, Yue, Zhang, Zhi-Yong, Zhao, Mao-Yuan, Zhou, Lu-Ping, Zhou, Zhi-Hao, and Zhu, Ke-Jun

Published
2024
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45. Learning to Disentangle GAN Fingerprint for Fake Image Attribution

Author

Yang, Tianyun, Cao, Juan, Sheng, Qiang, Li, Lei, Ji, Jiaqi, Li, Xirong, and Tang, Sheng

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Rapid pace of generative models has brought about new threats to visual forensics such as malicious personation and digital copyright infringement, which promotes works on fake image attribution. Existing works on fake image attribution mainly rely on a direct classification framework. Without additional supervision, the extracted features could include many content-relevant components and generalize poorly. Meanwhile, how to obtain an interpretable GAN fingerprint to explain the decision remains an open question. Adopting a multi-task framework, we propose a GAN Fingerprint Disentangling Network (GFD-Net) to simultaneously disentangle the fingerprint from GAN-generated images and produce a content-irrelevant representation for fake image attribution. A series of constraints are provided to guarantee the stability and discriminability of the fingerprint, which in turn helps content-irrelevant feature extraction. Further, we perform comprehensive analysis on GAN fingerprint, providing some clues about the properties of GAN fingerprint and which factors dominate the fingerprint in GAN architecture. Experiments show that our GFD-Net achieves superior fake image attribution performance in both closed-world and open-world testing. We also apply our method in binary fake image detection and exhibit a significant generalization ability on unseen generators., Comment: 10 pages, 7 figures

Published
2021

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