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Five-user quantum virtual local area network with an AlGaAs entangled photon source

Science China(Physics,Mechanics & Astronomy) 2025年第3期68卷 61-73页

作者： Cheng Qian Hongkai Tian Xu Jing Yichen Liu Zhe Chen Huiran Luo Yuan Du Xiaodong Zheng Tangsheng Chen Yuechan Kong Hua-Lei Yin Dong Jiang Bin Niu Liangliang Lu Key Laboratory of State Manipulation and Advanced Materials in Provincial Universities School of Physical Science and TechnologyNanjing Normal UniversityNanjing210023China School of Internet Anhui UniversityHefei230039China National Key Laboratory of Solid-State Microwave Devices and Circuits Nanjing Chip Valley Industrial Technology InstituteNanjing Electronic Devices InstituteNanjing210016China Research Center for Quantum Optics and Quantum Communication School of ScienceQingdao University of TechnologyQingdao266520China Department of Physics and Beijing Key Laboratory of Opto-electronic Functional Materials and Micro-nano Devices Key Laboratory of Quantum State Construction and Manipulation(Ministry of Education)Renmin University of ChinaBeijing100872China Beijing Academy of Quantum Information Sciences Beijing100193China National Engineering Research Center of Agro-Ecological Big Data Analysis and Application Anhui UniversityHefei230601China National Laboratory of Solid-State Microstructures and School of Physics Nanjing UniversityNanjing210093China Hefei National Laboratory Hefei230088China

The promotion of quantum network applications demands the scalable connection of quantum *** is preferable to set up multiple logical networks coexisting on a single physical network infrastructure to accommodate a larger number of *** we present a quantum virtual network architecture that offers this level of scalability,without being constrained to a fixed physical-layer network relying solely on passive multiplexing *** architecture can be understood as arising from the superposition of a fully connected entanglement distribution network and port-based virtual local area network,which group multiusers by access *** terms of hardware,we leverage a semiconductor chip with a high figure-of-merit modal overlap to directly generate high-quality polarization entanglement,and a streamlined polarization analysis module,which requires only one single-photon detector for each end *** experimentally perform the BBM92 QKD protocol on the five-user quantum virtual network and demonstrate voice and image encryption on a campus area *** results may provide insights into the realization of large-scale quantum networks with integrated and cost-efficient photonic architecture.

关键词： quantum network quantum entanglement distribution integrated photonics image and voice encryption

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Out-of-Distribution Fault Diagnosis of Industrial Cyber-Physical Systems Based on Orthogonal Anchor Clustering With Adaptive Balance

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 48-60页

作者： Liu, Ruonan Hu, Puyuan Zhao, Siheng Sun, Zhijian Han, Te Pang, Zhibo Zhang, Weidong Shanghai Jiao Tong University Department of Automation Shanghai200240 China National Key Laboratory of Marine Engine Science and Technology Shanghai201108 China Crossocean of Suzhou Technology Suzhou215121 China Beijing Institute of Technology Center for Energy and Environmental Policy Research School of Management Beijing100081 China ABB Corporate Research Sweden Department of Automation Technology Vasteras722 26 Sweden KTH Royal Institute of Technology Department of Intelligent Systems Stockholm114 28 Sweden Henan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng475001 China Shanghai Jiaotong University Department of Automation Shanghai200240 China

Given the critical role of rotating machinery in industrial cyber-physical systems (ICPS), ensuring their reliable operation is essential for the stability and safety of ICPS. Deep neural networks have demonstrated competitive results for intelligent fault diagnosis, which are usually trained via the historical data of all fault modes. However, in real engineering, it is usually difficult to collect samples and exhaust all failures during the training stage. As a result, out-of-distribution fault diagnosis (OOD-FD) becomes a more realistic problem that requires the methods to not only accurately diagnose the known faults, but also effectively recognize unknown ones. Therefore, a novel orthogonal anchor clustering with focal attention (FA-OAC) is proposed in this paper for OOD-FD. Firstly, an orthogonal anchor clustering (OAC) algorithm is proposed to fix the class center of each fault mode orthogonally and distinguish the known and unknown faults at the class level. Then, because the identifiability of different fault modes changes a lot in OOD-FD, the focal attention mechanism is applied to dynamically adjust the attention to different fault modes according to the distance loss of OAC, thus addressing the identifiability imbalance problem. To verify the effectiveness of the proposed method, different OOD-FD tasks are designed based on two rotating machinery datasets. The experimental results and comparison with state-of-the-art fault diagnosis methods have demonstrated that the proposed method has improved the OOD-FD performance greatly and therefore provides an effective tool in real engineering. © 2023 IEEE.

关键词： Rotating machinery

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On time slices, time complexity, and energy increase in implementing quantum adiabatic evolution by circuit model

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The European Physical Journal D 2025年第5期79卷 1-6页

作者： Sun, Jie Qian, Ling Cai, Dunbo Huang, Zhiguo Lu, Songfeng School of Internet Anhui University Hefei China National Engineering Research Center of Agro-Ecological Big Data Analysis and Application Anhui University Hefei China Fin China-Anhui University Joint Laboratory for Financial Big Data Research Hefei Financial China Information & Technology Co. Ltd. Hefei China China Mobile (Suzhou) Software Technology Company Limited Suzhou China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China

In this paper, motivated by previous related works on a kind of quantum local adiabatic evolution, we mainly study the circuit model of its corresponding quantum global adiabatic search algorithm, in which the evolving paths functions in the system Hamiltonian have a general form instead of the usual linear interpolating ones which are widely investigated in the literature. The main conclusion found here can be summarized as follows. Though this quantum adiabatic computation can demonstrate various algorithmic performances, when considering implementing it by a quantum circuit, the time slices thus produced always keep as an invariant which is proportional to inverse square of the overlap between the initial and final states in the problem of interest and also equal to that of multiplying the time complexity and the energy together, by noting that compared with the usual adiabatic evolutions, there is being a great increase of energy of the system. As far as we know, the result exposed here is new and is hoped to be useful for further understanding the kind of quantum adiabatic evolution itself and its connection with the quantum circuit model. Also, we hope the main conclusion here will be ubiquitous when considering applying this kind of quantum adiabatic evolution to solve the problems beyond quantum search.

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CTGDiff: A Conditional Diffusion Model for Cardiotocography Signal Synthesis

CTGDiff: A Conditional Diffusion Model for Cardiotocography ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiaoqing Li Pufan Cai Yu Lu Shijie Shi Liangkun Ma Xianghua Fu College of Big Data and Internet Shenzhen Technology University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Surgery Yong Loo Lin School of Medicine National University of Singapore Singapore School of Applied Technology Shenzhen University Shenzhen China Department of Obstetrics and Gynecology Peking Union Medical College Hospital Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The analysis of Cardiotocography (CTG) signals is often hindered by challenges such as limited data availability and label imbalance, which can undermine the performance of deep learning models. To address these issues, we present CTGDiff, a novel conditional diffusion model designed for generating synthetic Fetal Heart Rate (FHR) and Uterine Contraction (UC) signals. CTGDiff leverages both Phase-Rectified Signal Averaging (PRSA) spectrograms and UC as conditioning inputs for FHR, and integrates time encoding, condition generation from PRSA features, and residual blocks with dilated convolutions to capture both temporal dynamics and long-range dependencies. Extensive experiments, both qualitative and quantitative, demonstrate the model’s ability to synthesize high-quality CTG signals. In comparison with GANs and image-based diffusion models, CTGDiff achieves superior signal fidelity and distribution similarity for FHR, as indicated by metrics such as a 0.004 maximum mean deviation (MMD), 0.646 percent root mean square difference (PRD), 3.951 relative entropy (RE), and 0.291 Frechet distance (FD). Expert evaluations confirm that the model can generate both normal and abnormal CTG signals with high accuracy, conditioned on specific input data. These results underscore the potential of diffusion models for a wide range of applications in biomedical time series analysis, including signal synthesis, imputation, and noise reduction.

关键词： Measurement Fetal heart rate Biological system modeling Time series analysis Diffusion models data models Signal synthesis Cardiography Speech processing Spectrogram

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ZuantuSet: A Collection of Historical Chinese Visualizations and Illustrations

arXiv

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arXiv 2025年

作者： Mei, Xiyao Zhang, Yu Yang, Chaofan Shi, Rui Yuan, Xiaoru National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University Beijing China Department of Computer Science University of Oxford Oxford United Kingdom Center for Research on Ancient Chinese History Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Application Peking University Beijing China PKU-WUHAN Institute for Artificial Intelligence Wuhan China

Historical visualizations are a valuable resource for studying the history of visualization and inspecting the cultural context where they were created. When investigating historical visualizations, it is essential to consider contributions from different cultural frameworks to gain a comprehensive understanding. While there is extensive research on historical visualizations within the European cultural framework, this work shifts the focus to ancient China, a cultural context that remains underexplored by visualization researchers. To this aim, we propose a semi-automatic pipeline to collect, extract, and label historical Chinese visualizations. Through the pipeline, we curate ZuantuSet, a dataset with over 71K visualizations and 108K illustrations. We analyze distinctive design patterns of historical Chinese visualizations and their potential causes within the context of Chinese history and culture. We illustrate potential usage scenarios for this dataset, summarize the unique challenges and solutions associated with collecting historical Chinese visualizations, and outline future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： data collection

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Continual Test-Time Adaptation for Single Image Defocus Deblurring via Causal Siamese Networks

arXiv

引用

arXiv 2025年

作者： Cui, Shuang Li, Yi Li, Jiangmeng Tang, Xiongxin Su, Bing Xu, Fanjiang Xiong, Hui National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences China Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China China Department of Computer Science & Engineering the Hong Kong University of Science and Technology Hong Kong

Single image defocus deblurring (SIDD) aims to restore an all-in-focus image from a defocused one. Distribution shifts in defocused images generally lead to performance degradation of existing methods during out-of-distribution inferences. In this work, we gauge the intrinsic reason behind the performance degradation, which is identified as the heterogeneity of lens-specific point spread functions. Empirical evidence supports this finding, motivating us to employ a continual test-time adaptation (CTTA) paradigm for SIDD. However, traditional CTTA methods, which primarily rely on entropy minimization, cannot sufficiently explore task-dependent information for pixel-level regression tasks like SIDD. To address this issue, we propose a novel Siamese networks-based continual test-time adaptation framework, which adapts source models to continuously changing target domains only requiring unlabeled target data in an online manner. To further mitigate semantically erroneous textures introduced by source SIDD models under severe degradation, we revisit the learning paradigm through a structural causal model and propose Causal Siamese networks (CauSiam). Our method leverages large-scale pre-trained vision-language models to derive discriminative universal semantic priors and integrates these priors into Siamese networks, ensuring causal identifiability between blurry inputs and restored images. Extensive experiments demonstrate that CauSiam effectively improves the generalization performance of existing SIDD methods in continuously changing domains. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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DMSACNN: Deep Multiscale Attentional Convolutional Neural Network for EEG-Based Motor Decoding

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Liu, Ke Xing, Xin Yang, Tao Yu, Zhuliang Xiao, Bin Wang, Guoyin Wu, Wei Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing China Chongqing University of Posts and Telecommunications Key Laboratory of Cyberspace Big Data Intelligent Security of Ministry of Education China South China University of Technology College of Automation Science and Engineering Guangzhou510641 China Chongqing Normal University National Center for Applied Mathematics in Chongqing Chongqing401331 China Shanghai Jiao Tong University School of Medicine Songjiang Hospital Songjiang Research Institute Shanghai Key Laboratory of Emotions and Affective Disorders Shanghai201600 China

Objective: Accurate decoding of electroencephalogram (EEG) signals has become more significant for the brain-computer interface (BCI). Specifically, motor imagery and motor execution (MI/ME) tasks enable the control of external devices by decoding EEG signals during imagined or real movements. However, accurately decoding MI/ME signals remains a challenge due to the limited utilization of temporal information and ineffective feature selection methods. Methods: This paper introduces DMSACNN, an end-to-end deep multiscale attention convolutional neural network for MI/ME-EEG decoding. DMSACNN incorporates a deep multiscale temporal feature extraction module to capture temporal features at various levels. These features are then processed by a spatial convolutional module to extract spatial features. Finally, a local and global feature fusion attention module is utilized to combine local and global information and extract the most discriminative spatiotemporal features. Main results: DMSACNN achieves impressive accuracies of 78.20%, 96.34% and 70.90% for hold-out analysis on the BCI-IV-2a, High Gamma and OpenBMI datasets, respectively, outperforming most of the state-of-the-art methods. Conclusion and significance: These results highlight the potential of DMSACNN in robust BCI applications. Our proposed method provides a valuable solution to improve the accuracy of the MI/ME-EEG decoding, which can pave the way for more efficient and reliable BCI systems. The source code for DMSACNN is available at https://***/xingxin-99/***. © 2025 IEEE.

关键词： Convolutional neural networks

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Multimodal Dialogue Emotion Recognition Based on Label Optimization and Coarse-Grained Assisted Fine-Grained

Multimodal Dialogue Emotion Recognition Based on Label Optim...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhibo Zhang Zhenyu Yang Yan Huang Baojie Xu Wenyue Hu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal dialogue emotion recognition integrates data from multiple modalities to accurately identify emotional states in conversations. However, differences in expression and information density across modalities complicate the fusion of features. Traditional methods may introduce redundant information from other utterances, reducing the accuracy of emotion recognition. Existing one-hot labels often fail to capture the full range of emotional expressions, leading to biased results. To address these issues, we propose a model that fuses different modalities within the same utterance to avoid redundancy. It employs a progressive classification process, refining emotion recognition from coarse to fine granularity. Additionally, we use emotion polarity probabilities as weights for fine-grained classification and introduce a multimodal information-rich label that considers both the data and their interactions. Experiments on IEMOCAP and MELD datasets demonstrate the model’s effectiveness, significantly improving dialog emotion recognition accuracy. Our code is available at https://***/r/LOCG-188E.

关键词： Emotion recognition Accuracy Fuses Refining Termination of employment Speech recognition Signal processing Speech processing Optimization Faces

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Robust Indoor Person Re-Identification with Multimodal Training

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IEEE Internet of Things Journal 2025年

作者： Su, Can Xue, Xinlei Ma, Lei Zhang, Xiaolong Yan, Wei Bian, Kaigui Peking University School of Computer Science AI Innovation Center National Engineering Laboratory for Big Data Analysis and Applications Beijing100871 China Peking University School of Computer Science Beijing100871 China Peking University Beijing Academy of Artificial Intelligence National Biomedical Imaging Center College of Future Technology National Key Laboratory for Multimedia Information Processing Beijing100871 China Beihang University Beijing100191 China

Existing person re-identification (ReID) methods mainly rely on images and videos to match persons across cameras, yet visual data captured by cameras are vulnerable to environmental interferences (e.g. illumination and occlusion) or personal appearance changes, leading to performance degradation under such scenes. Meanwhile, the popularization of Wi-Fi networks has allowed probe requests to be captured for mobile sensing applications such as crowd counting and trajectory estimation. However, the MAC address randomization technique adopted by modern devices breaks the association of probe requests and adversely affects the functionality of these applications. In this paper, we propose MaRPA, the first multimodal training approach that incorporates both videos and Wi-Fi probe requests to simultaneously promote tasks of probe requests association and person ReID. MaRPA first distinguishes among pairwise probe request frames through a contrastive learning model. It then matches video and probe request sequences by exploring their similarities from the position and the vision aspects. Matched videos and probe requests provide complementary information and generate more robust features for both tasks. To evaluate MaRPA, we contribute a new dataset containing synchronous videos and probe requests data for probe requests association and person ReID. Experimental results demonstrate the effectiveness of our approach. For probe requests association, it achieves > 85% discrimination accuracy and > 0.90 V-measure score;for person ReID, it achieves 75.8% mAP and 90.6% Rank-1, improving state-of-the-art video-based ReID methods by over 40% © 2014 IEEE.

关键词： Wi-Fi

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A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qing Liu Qian Gao Jun Fan Zhiqiang Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China China Telecom Digital Intelligence Techonology Co Ltd Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN— a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model’s effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets.

关键词： Measurement Bridges Contrastive learning Signal processing data processing Vectors Space exploration Resource management Speech processing Mutual information

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