198 citations
- Beijing University of Posts and TelecommunicationsCN15 papers
- Southeast UniversityCN13 papers
- Tsinghua UniversityCN12 papers
- ZTE (China)CN9 papers
- State Key Laboratory of Mobile Networks and Mobile Multimedia Technology8 papers
- Peking UniversityCN7 papers
- Nanjing UniversityCN6 papers
- Purple Mountain LaboratoriesCN6 papers
- Shanghai Jiao Tong UniversityCN6 papers
- University of Science and Technology of ChinaCN6 papers
- Beijing Institute of TechnologyCN4 papers
- Beijing Jiaotong UniversityCN4 papers
107 papers
Experimental certification of the nonlocal advantages of quantum imaginarity
Jian-Hao Wu, Kai-Yu Yuan, Yun-Xiang Tian +5
Quantum imaginarity is a distinct resource in quantum information theory, yet its nonlocal properties have not been fully explored. Here, we report an experimental study of the non…
Link-adaptive digital twin for robust physical-layer modeling in hybrid-amplified ultra-wideband optical networks
Xiaoxuan Gao, Rentao Gu, Yingchun Wang +3
Accurate physical-layer modeling is increasingly essential for reliable ultra-wideband operation and capacity optimization, especially under the intensified inter-channel stimulate…
Robust device-independent characterization of sharpness and incompatibility of unsharp instruments
Qian Zhang, Kai-Yu Yuan, Yan-Xin Rong +3
Unsharp measurements are key resources for tasks that balance information gain and disturbance, but certifying them without device assumptions remains a challenge. We propose a ful…
Auditing Data Provenance in LLM Fine-tuning via Intrinsic Distributional Fingerprints
Zirui Huang, Yunlong Mao, Wei Tong +3
The proliferation of customized Large Language Models (LLMs) poses critical risks of Data Intellectual Property (Data IP) infringement via unauthorized fine-tuning on proprietary d…
Visual Information Extraction from Documents via Classification-Guided Large Vision-Language Models
Huafu Li, Guo Chen, Jia Xia +5
Visual information extraction (VIE) from visually rich documents remains challenging due to high layout variability and real-world impairments. Existing methods typically rely on s…
An automated method of identifying incorrectly labelled images based on the sequences of loss functions of deep learning networks
Zhipeng Zhang, Wenhui Shou, Wengting Ma +5
Deep learning is widely applied in medical image analysis, but up to 10% of manually labelled images may be incorrect, degrading model performance. This paper proposes an automated…