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Near 100%spectral-purity photons from reconfigurable micro-rings

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Chinese Physics B 2020年第11期29卷 135-139页

作者： Pingyu Zhu Yingwen Liu Chao Wu Shichuan Xue Xinyao Yu Qilin Zheng Yang Wang Xiaogang Qiang Junjie Wu Ping Xu Institute for Quantum Information&State Key Laboratory of High Performance Computing College of ComputerNational University of Defense TechnologyChangsha 410073China National Innovation Institute of Defense Technology AMSBeijing 100071China National Laboratory of Solid State Microstructures and School of Physics Nanjing UniversityNanjing 210093China

We propose an on-chip reconfigurable micro-ring to engineer the spectral-purity of *** micro-ring resonator is designed to be coupled by one or two asymmetric Mach-Zehnder interferometers and the coupling coefficients hence the quality-factors of the pump and the converted photons can be dynamically changed by the interferometer’s internal *** calculate the joint-spectrum function and obtain the spectral-purity of photons and Schmidt number under different *** show that it is a dynamical method to adjust the spectral-purity and can optimize the spectralpurity of photons up to near 100%.The condition for high-spectral-purity photons is ensured by the micro-ring itself,so it overcomes the trade-off between spectral purity and brightness in the traditional post-filtering *** scheme is robust to fabrication variations and can be successfully applied in different fabrication labs and different *** high-spectral-purity photons will be beneficial for quantum information processing like Boson sampling and other quantum algorithms.

关键词： spectral-purity micro-ring Mach–Zehnder interferometer

Uncertainty-informed Mutual Learning for Joint Medical Image Classification and Segmentation

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arXiv 2023年

作者： Ren, Kai Zou, Ke Liu, Xianjie Chen, Yidi Yuan, Xuedong Shen, Xiaojing Wang, Meng Fu, Huazhu National Key Laboratory of Fundamental Science on Synthetic Vision Sichuan University Sichuan China College of Computer Science Sichuan University Sichuan China Department of Radiology West China Hospital Sichuan University Sichuan China College of Mathematics Sichuan University Sichuan China Institute of High Performance Computing Agency for Science Technology and Research Singapore

Classification and segmentation are crucial in medical image analysis as they enable accurate diagnosis and disease monitoring. However, current methods often prioritize the mutual learning features and shared model parameters, while neglecting the reliability of features and performances. In this paper, we propose a novel Uncertainty-informed Mutual Learning (UML) framework for reliable and interpretable medical image analysis. Our UML introduces reliability to joint classification and segmentation tasks, leveraging mutual learning with uncertainty to improve performance. To achieve this, we first use evidential deep learning to provide image-level and pixel-wise confidences. Then, an uncertainty navigator is constructed for better using mutual features and generating segmentation results. Besides, an uncertainty instructor is proposed to screen reliable masks for classification. Overall, UML could produce confidence estimation in features and performance for each link (classification and segmentation). The experiments on the public datasets demonstrate that our UML outperforms existing methods in terms of both accuracy and robustness. Our UML has the potential to explore the development of more reliable and explainable medical image analysis models. © 2023, CC BY.

关键词： Uncertainty analysis

Linear uncertainty quantification of graphical model inference 24

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Linear uncertainty quantification of graphical model inferen...

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Chenghua Guo Han Yu Jiaxin Liu Chao Chen Qi Li Sihong Xie Xi Zhang Key Laboratory of Trustworthy Distributed Computing and Service (MoE) Beijing University of Posts and Telecommunications China College of Artificial Intelligence Beijing University of Posts and Telecommunications China Department of Computer Science and Engineering Lehigh University School of Computer Science and Technology Harbin Institute of Technology (Shenzhen) China Department of Computer Science Iowa State University AI Thrust The Hong Kong University of Science and Technology (Guangzhou) China

ISBN: (纸本)9798331314385

Uncertainty Quantification (UQ) is vital for decision makers as it offers insights into the potential reliability of data and model, enabling more informed and risk-aware decision-making. Graphical models, capable of representing data with complex dependencies, are widely used across domains. Existing sampling-based UQ methods are unbiased but cannot guarantee convergence and are time-consuming on large-scale graphs. There are fast UQ methods for graphical models with closed-form solutions and convergence guarantee but with uncertainty underestimation. We propose LinUProp, a UQ method that utilizes a novel linear propagation of uncertainty to model uncertainty among related nodes additively instead of multiplicatively, to offer linear scalability, guaranteed convergence, and closed-form solutions without underestimating uncertainty. Theoretically, we decompose the expected prediction error of the graphical model and prove that the uncertainty computed by LinUProp is the generalized variance component of the decomposition. Experimentally, we demonstrate that LinUProp is consistent with the sampling-based method but with linear scalability and fast convergence. Moreover, LinUProp outperforms competitors in uncertainty-based active learning on four real-world graph datasets, achieving higher accuracy with a lower labeling budget.

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Enhancing SNN-based Spatio-Temporal Learning: A Benchmark Dataset and Cross-Modality Attention Model

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arXiv 2024年

作者： Zhou, Shibo Yang, Bo Yuan, Mengwen Jiang, Runhao Yan, Rui Pan, Gang Tang, Huajin Research Center for Data Hub and Security Zhejiang Lab Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Research Center for High Efficiency Computing System Zhejiang Lab Hangzhou China College of Computer Science and Technology Zhejiang University of Technology Hangzhou China The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China

Spiking Neural Networks (SNNs), renowned for their low power consumption, brain-inspired architecture, and spatio-temporal representation capabilities, have garnered considerable attention in recent years. Similar to Artificial Neural Networks (ANNs), high-quality benchmark datasets are of great importance to the advances of SNNs. However, our analysis indicates that many prevalent neuromorphic datasets lack strong temporal correlation, preventing SNNs from fully exploiting their spatio-temporal representation capabilities. Meanwhile, the integration of event and frame modalities offers more comprehensive visual spatio-temporal information. Yet, the SNN-based cross-modality fusion remains underexplored. In this work, we present a neuromorphic dataset called DVS-SLR that can better exploit the inherent spatio-temporal properties of SNNs. Compared to existing datasets, it offers advantages in terms of higher temporal correlation, larger scale, and more varied scenarios. In addition, our neuromorphic dataset contains corresponding frame data, which can be used for developing SNN-based fusion methods. By virtue of the dual-modal feature of the dataset, we propose a Cross-Modality Attention (CMA) based fusion method. The CMA model efficiently utilizes the unique advantages of each modality, allowing for SNNs to learn both temporal and spatial attention scores from the spatio-temporal features of event and frame modalities, subsequently allocating these scores across modalities to enhance their synergy. Experimental results demonstrate that our method not only improves recognition accuracy but also ensures robustness across diverse scenarios. © 2024, CC0.

关键词： Spatio-temporal data

An Operator Learning Approach via Function-valued Reproducing Kernel Hilbert Space for Differential Equations

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arXiv 2022年

作者： Bao, Kaijun Qian, Xu Liu, Ziyuan Song, Songhe Department of Mathematics College of Liberal Arts and Science National University of Defense Technology Changsha410073 China State Key Laboratory of High Performance Computing National University of Defense Technology Changsha410073 China

Much recent work has addressed the solution of a family of partial differential equations by computing the inverse operator map between the input and solution space. Toward this end, we incorporate function-valued reproducing kernel Hilbert spaces in our operator learning model. We use neural networks to parameterize Hilbert-Schmidt integral operator and propose an architecture. Experiments including several typical datasets show that the proposed architecture has desirable accuracy on linear and nonlinear partial differential equations even with a small amount of data. By learning the mappings between function spaces, the proposed method can find the solution of a high-resolution input after learning from lower-resolution data. Copyright © 2022, The Authors. All rights reserved.

关键词： Inverse problems

Hfn:Heterogeneous Feature Network for Multivariate Time Series Anomaly Detection

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SSRN

SSRN 2023年

作者： Zhan, Jun Wu, Chengkun Yang, Canqun Miao, Qiucheng Ma, Xiandong College of Computer Science National University of Defense Technology Changsha410073 China State Key Laboratory of High Performance Computing Changsha410073 China National SuperComputing Center in Tianjin Tianjin300000 China School of Engineering Lancaster University LancasterLA1 4YW United Kingdom

As the key step of anomaly detection for multivariate time-series (MTS) data, learning the relations among different variables has been explored by many approaches. However, most of the existing approaches do not consider the heterogeneity between variables, that is, different types of variables (continuous numerical variables, discrete categorical variables or hybrid variables) may have different and distinctive edge distributions. In this paper, we propose a novel semi-supervised anomaly detection framework based on a heterogeneous feature network (HFN) for MTS. Specifically, we first combine the embedding similarity subgraph generated by sensor embedding and the feature value similarity subgraph generated by sensor values to construct a time-series heterogeneous graph, which fully utilizes the rich heterogeneous mutual information among variables. Then, a prediction model containing nodes and channel attentions is jointly optimized to obtain better time-series representations. This approach fuses the state-of-the-art technologies of heterogeneous graph structure learning (HGSL) and representation learning. The experiments on four sensor datasets from real-world applications demonstrate that our approach detects the anomalies more accurately than those baseline approaches, thus providing a basis for the rapid positioning of anomalies. © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

Understanding Wireless Charger Networks: Concepts, Current Research, and Future Directions

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IEEE Communications Surveys and Tutorials 2024年

作者： Ren, Meixuan Dai, Haipeng Liu, Tang Deng, Xianjun Dou, Wanchun Yang, Yuanyuan Chen, Guihai Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China Sichuan Normal University College of Computer Science Sichuan Chengdu610101 China Sichuan Normal University Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Sichuan Chengdu610068 China Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan430074 China Stony Brook University Department of Electrical and Computer Engineering NY11794 United States

Wireless Charger Network (WCN) emerges as a promising networking paradigm, employing wireless chargers with Wireless Power Transfer (WPT) technology to provide long-term and sustainable energy supply for future networks. Although extensive research has been conducted in this area over the last decade, there is currently no comprehensive survey to compile the latest literature and provide insights into future research directions. To fill this gap, our survey explores the recent developments in the active research area of WCNs. This paper starts by providing a framework of WCNs in detail, covering aspects of network architecture, various charging models, network design issues, and typical applications of WCNs. Then, we give an overview of charger deployment schemes, focusing on omnidirectional, directional, non-radiative, and heterogeneous charger deployments. We also provide an overview of charging scheduling schemes, encompassing power control, time allocation, energy beamforming, and multi-resource scheduling. Moreover, we explore communication optimization schemes, including Medium Access Control (MAC) protocols, routing protocols, broadcast transmission, and data collection. Finally, we highlight some future research directions and present corresponding open issues to advance the research on WCNs. © 1998-2012 IEEE.

关键词： Inductive power transmission

HPCR: Holistic Proxy-based Contrastive Replay for Online Continual Learning

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arXiv 2023年

作者： Lin, Huiwei Feng, Shanshan Zhang, Baoquan Li, Xutao Ye, Yunming Depart ment of Computer Science Harbin Institute of Technology Shenzhen518055 China Shenzhen Key Laboratory of Internet Informatio Collaboration Shenzhen518055 China Centre for Frontier AI Research Insti tute of High Performance Computing A*STAR Singapore

Online continual learning, aimed at developing a neural network that continuously learns new data from a single pass over an online data stream, generally suffers from catastrophic forgetting. Existing replay-based methods alleviate forgetting by replaying partial old data in a proxy-based or contrastive-based replay manner, each with its own shortcomings. Our previous work proposes a novel replay-based method called proxy-based contrastive replay (PCR), which handles the shortcomings by achieving complementary advantages of both replay manners. In this work, we further conduct gradient and limitation analysis of PCR. The analysis results show that PCR still can be further improved in feature extraction, generalization, and anti-forgetting capabilities of the model. Hence, we develop a more advanced method named holistic proxy-based contrastive replay (HPCR). HPCR consists of three components, each tackling one of the limitations of PCR. The contrastive component conditionally incorporates anchor-to-sample pairs to PCR, improving the feature extraction ability. The second is a temperature component that decouples the temperature coefficient into two parts based on their gradient impacts and sets different values for them to enhance the generalization ability. The third is a distillation component that constrains the learning process with additional loss terms to improve the anti-forgetting ability. Experiments on four datasets consistently demonstrate the superiority of HPCR over various state-of-the-art methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Contrastive Learning

Characterization of Intensity Correlation via Single-photon Detection in Quantum key Distribution

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arXiv 2024年

作者： Xing, Tianyi Liu, Junxuan Zhang, Likang Wang, Min-Yan Li, Yu-Huai Liu, Ruiyin Peng, Qingquan Wang, Dongyang Wang, Yaxuan Liu, Hongwei Li, Wei Cao, Yuan Huang, Anqi Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Hefei National Research Center for Physical Sciences The Microscale and School of Physical Sciences University of Science and Technology of China Hefei230026 China Shanghai Research Center for Quantum Science CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai201315 China Hefei National Laboratory University of Science and Technology of China Hefei230088 China China Information Technology Security Evaluation Center Beijing100085 China

One of the most significant vulnerabilities in the source unit of quantum key distribution (QKD) is the correlation between quantum states after modulation, which shall be characterized and evaluated for its practical security performance. In this work, we propose a methodology to characterize the intensity correlation according to the single-photon detection results in the measurement unit without modifying the configuration of the QKD system. In contrast to the previous research that employs extra classical optical detector to measure the correlation, our method can directly analyse the detection data generated during the raw key exchange, enabling to characterize the feature of correlation in real-time system operation. The basic method is applied to a BB84 QKD system and the characterized correlation significantly decreases the secure key rate shown by the security proof. Furthermore, the method is extended and applied to characterize the correlation from the result of Bell-state measurement, which demonstrates its applicability to a running full-scheme MDI QKD system. This study provides an approach for standard certification of a QKD system. Copyright © 2024, The Authors. All rights reserved.

关键词： Photons