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arXiv 2021年

作者： Liu, Hui Jiang, Cong Zhu, Hao-Tao Zou, Mi Yu, Zong-Wen Hu, Xiao-Long Xu, Hai Ma, Shizhao Han, Zhiyong Chen, Jiu-Peng Dai, Yunqi Tang, Shi-Biao Zhang, Weijun Li, Hao You, Lixing Wang, Zhen Zhou, Fei Zhang, Qiang Wang, Xiang-Bin Chen, Teng-Yun Pan, Jian-Wei Hefei National Laboratory for Physical Sciences at Microscale Department of Modern Physics University of Science and Technology ofChina HefeiAnhui230026 China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China HefeiAnhui230026 China Jinan Institute of Quantum Technology JinanShandong250101 China State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China Data Communication Science and Technology Research Institute Beijing100191 China QuantumCTek Corporation Limited HefeiAnhui230088 China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China

Quantum key distribution endows people with information-theoretical security in communications. Twin-field quantum key distribution (TF-QKD) has attracted considerable attention because of its outstanding key rates over long distances. Recently, several demonstrations of TF-QKD have been realized. Nevertheless, those experiments are implemented in the laboratory, remaining a critical question about whether the TF-QKD is feasible in real-world circumstances. Here, by adopting the sending-or-not-sending twin-field QKD (SNS-TF-QKD) with the method of actively odd parity pairing (AOPP), we demonstrate a field-test QKD over 428 km deployed commercial fiber and two users are physically separated by about 300 km in a straight line. To this end, we explicitly measure the relevant properties of the deployed fiber and develop a carefully designed system with high stability. The secure key rate we achieved breaks the absolute key rate limit of repeater-less QKD. The result provides a new distance record for the field test of both TF-QKD and all types of fiber-based QKD systems. Our work bridges the gap of QKD between laboratory demonstrations and practical applications, and paves the way for intercity QKD network with high-speed and measurement-device-independent security. Copyright © 2021, The Authors. All rights reserved.

关键词： Quantum cryptography

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On the connectivity of highly dynamic wireless sensor networks in smart factory

On the connectivity of highly dynamic wireless sensor networ...

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2019 International Conference on networking and network Applications, NaNA 2019

作者： Gao, Cong Wang, Zhongmin Chen, Yanping School of Computer Science and Technology Xi'an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an China Xi'An University of Posts and Telecommunications Xi'an China

ISBN: (纸本)9781728126296

With the development of smart manufacturing in Industry 4.0, large amount of heterogeneous data are generated from multiple sources. Various data processing techniques can be applied to these data for the purpose of extracting potential information about the status of the manufacturing network. Thus, data are valuable to the analysis and adjustment of the manufacturing processes. Due to the diversity of products, assembly lines and manufacturing processes are highly dynamic. In view of the flexible deployment of wireless sensor networks, they have been widely deployed to conduct data collection. However, due to the limited radio range of a wireless sensor node, the connectivity of a highly dynamic wireless sensor network is a key issue. In this paper, we analyzed the connectivity in a wireless sensor network and accorded three types of connectivity. Then, we developed two network connectivity models from the perspective of one dimension and two dimensions. The degrees of connectivity for both two nodes and the network are elaborated. Finally, the relation between the number of sink nodes and the average degree of connectivity of the network is analyzed based on experimental results. © 2019 IEEE.

关键词： data handling

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Abnormal Scanning Patterns Based on Eye Movement Entropy in Early Psychosis

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Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2024年

作者： Zhang, Dan Ma, Chunyan Xu, Lihua Liu, Xu Cui, Huiru Wei, Yanyan Zheng, Wensi Hong, Yawen Xie, Yuou Qian, Zhenying Hu, Yegang Tang, Yingying Li, Chunbo Liu, Zhi Chen, Tao Liu, Haichun Zhang, Tianhong Wang, Jijun Shanghai Key Laboratory of Psychotic Disorders Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicine People's Republic of China Shanghai China First Clinical Medical College of Nanjing Medical University People's Republic of China Nanjing China Shanghai Institute for Advanced Communication and Data Science Shanghai University People's Republic of China Shanghai China School of Communication and Information Engineering Shanghai University People's Republic of China Shanghai China Labor and Worklife Program Harvard University Cambridge Massachusetts United States Big Data Research Laboratory University of Waterloo Waterloo ON Canada Niacin (Shanghai) Technology Co. Ltd. People's Republic of China Shanghai China Department of Automation Shanghai Jiao Tong University People's Republic of China Shanghai China Shanghai Mental Health Center Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention Shanghai Key Laboratory of Psychotic Disorders Shanghai Jiaotong University School of Medicine People's Republic of China Shanghai China CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences People's Republic of China Shanghai China Institute of Psychology and Behavioral Science Shanghai Jiao Tong University People's Republic of China Shanghai China

Background: Restricted scan path mode is hypothesized to explain abnormal scanning patterns in patients with schizophrenia. Here, we calculated entropy scores (drawing on gaze data to measure the statistical randomness of eye movements) to quantify how strategical and random participants were when processing image stimuli. Methods: Eighty-six patients with first-episode schizophrenia (FES), 124 individuals at clinical high risk (CHR) for psychosis, and 115 healthy control participants (HCs) completed an eye-tracking examination while freely viewing 35 static images (each presented for 10 seconds) and cognitive assessments. We compared group differences in the overall entropy score, as well as entropy scores under various conditions. We also investigated the correlations between entropy scores and symptoms and cognitive function. Results: Increased overall entropy scores were noted in the FES and CHR groups compared with the HC group, and these differences were already apparent within 0 to 2.5 seconds. In addition, the CHR group exhibited higher entropy than the HC group when viewing low-meaning images. Moreover, the entropy within 0 to 2.5 seconds showed significant correlations with negative symptoms in the FES group, attention/vigilance scores in the CHR group, and speed of processing and attention/vigilance scores across all 3 groups. Conclusions: The results indicate that individuals with FES and those at CHR scanned pictures more randomly and less strategically than HCs. These patterns also correlated with clinical symptoms and neurocognition. The current study highlights the potential of the eye movement entropy measure as a neurophysiological marker for early psychosis. © 2024 Society of Biological Psychiatry

关键词： Biomarker Clinical high risk for psychosis Cognitive impairments Early psychosis Eye movement Schizophrenia

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Local and global structure-aware entropy regularized mean teacher model for 3d left atrium segmentation 23rd

Local and global structure-aware entropy regularized mean te...

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23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Hang, Wenlong Feng, Wei Liang, Shuang Yu, Lequan Wang, Qiong Choi, Kup-Sze Qin, Jing School of Computer Science and Technology Nanjing Tech University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Department of Radiation Oncology Stanford University StanfordCA94305 United States CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China School of Nursing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9783030597092

Emerging self-ensembling methods have achieved promising semi-supervised segmentation performances on medical images through forcing consistent predictions of unannotated data under different perturbations. However, the consistency only penalizes on independent pixel-level predictions, making structure-level information of predictions not exploited in the learning procedure. In view of this, we propose a novel structure-aware entropy regularized mean teacher model to address the above limitation. Specifically, we firstly introduce the entropy minimization principle to the student network, thereby adjusting itself to produce high-confident predictions of unannotated images. Based on this, we design a local structural consistency loss to encourage the consistency of inter-voxel similarities within the same local region of predictions from teacher and student networks. To further capture local structural dependencies, we enforce the global structural consistency by matching the weighted self-information maps between two networks. In this way, our model can minimize the prediction uncertainty of unannotated images, and more importantly that it can capture local and global structural information and their complementarity. We evaluate the proposed method on a publicly available 3D left atrium MR image dataset. Experimental results demonstrate that our method achieves outstanding segmentation performances than the state-of-the-art approaches in scenes with limited annotated images. © Springer Nature Switzerland AG 2020.

关键词： Forecasting

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Tile-based Multi-source Adaptive Streaming for 360-degree Ultra-High-Definition Videos

Tile-based Multi-source Adaptive Streaming for 360-degree Ul...

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International Conference on Mobile Ad-hoc and Sensor networks, MSN

作者： Xinjing Yuan Lingjun Pu Ruilin Yun Jingdong Xu Institute of Systems and Networks College of Computer Science Nankai University Tianjin China State Key Laboratory of Integrated Services Networks (Xidian University) Xi’an China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China

ISBN: (数字)9781728199160

ISBN: (纸本)9781728192369

360° UHD videos have absorbed great attention in recent years. However, as they are of significant size and usually watched from a close range, they require extremely high bandwidth for a good immersive experience, which poses a great challenge on the current single-source adaptive streaming strategies. Realizing the great potentials of tile-based video streaming and pervasive edge services, we advocate a tile-based multi-source adaptive streaming strategy for 360° UHD videos over edge networks. In order to reap its benefits, we consider a comprehensive model which captures the key components of tile-based multi-source adaptive streaming. Then we formulate a joint bitrate selection and request scheduling problem, aiming at maximizing the system utility (i.e., user QoE minus service overhead) while satisfying the service integrity and latency constraints. To solve the formulated non-linear integer programming problem efficiently, we decouple the control variables and resort to matroid theory to design an optimal master-slave algorithm. In addition, we improve our proposed algorithm with a deep learning-based bitrate selection algorithm, which can achieve a rationalization result in a short running time. Extensive datadriven simulations validate the superior performance of our proposed algorithm.

关键词： Integer programming Deep learning Adaptation models Adaptive systems Bit rate Master-slave Videos

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Field Test of Twin-Field Quantum key Distribution through Sending-or-Not-Sending over 428 km

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Physical Review Letters 2021年第25期126卷 250502-250502页

作者： Hui Liu Cong Jiang Hao-Tao Zhu Mi Zou Zong-Wen Yu Xiao-Long Hu Hai Xu Shizhao Ma Zhiyong Han Jiu-Peng Chen Yunqi Dai Shi-Biao Tang Weijun Zhang Hao Li Lixing You Zhen Wang Yong Hua Hongkun Hu Hongbo Zhang Fei Zhou Qiang Zhang Xiang-Bin Wang Teng-Yun Chen Jian-Wei Pan Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics University of Science and Technology ofChina Hefei Anhui 230026 People’s Republic of China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Hefei Anhui 230026 People’s Republic of China Jinan Institute of Quantum Technology Jinan Shandong 250101 People’s Republic of China State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing 100084 People’s Republic of China Data Communication Science and Technology Research Institute Beijing 100191 People’s Republic of China QuantumCTek Corporation Limited Hefei Anhui 230088 People’s Republic of China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai 200050 People’s Republic of China Chongqing Optoelectronics Research Institute Chongqing 400060 People’s Republic of China

Quantum key distribution endows people with information-theoretical security in communications. Twin-field quantum key distribution (TF-QKD) has attracted considerable attention because of its outstanding key rates over long distances. Recently, several demonstrations of TF-QKD have been realized. Nevertheless, those experiments are implemented in the laboratory, and therefore a critical question remains about whether the TF-QKD is feasible in real-world circumstances. Here, by adopting the sending-or-not-sending twin-field QKD (SNS-TF-QKD) with the method of actively odd parity pairing (AOPP), we demonstrate a field-test QKD over 428 km of deployed commercial fiber and two users are physically separated by about 300 km in a straight line. To this end, we explicitly measure the relevant properties of the deployed fiber and develop a carefully designed system with high stability. The secure key rate we achieved breaks the absolute key rate limit of repeaterless QKD. The result provides a new distance record for the field test of both TF-QKD and all types of fiber-based QKD systems. Our work bridges the gap of QKD between laboratory demonstrations and practical applications and paves the way for an intercity QKD network with measurement-device-independent security.

关键词： Optical quantum information processing Quantum cryptography Quantum optics

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Weakly Supervised Fine-Grained Image Classification via Guassian Mixture Model Oriented Discriminative Learning

Weakly Supervised Fine-Grained Image Classification via Guas...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhihui Wang Shijie Wang Shuhui Yang Haojie Li Jianjun Li Zezhou Li International School of Information Science & Engineering Dalian University of Technology China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province China School of Computer Science and Technology Hangzhou Dianzi University China Shanghai Pinlan Data Technology

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Existing weakly supervised fine-grained image recognition (WFGIR) methods usually pick out the discriminative regions from the high-level feature maps directly. We discover that due to the operation of stacking local receptive filed, Convolutional Neural network causes the discriminative region diffusion in high-level feature maps, which leads to inaccurate discriminative region localization. In this paper, we propose an end-to-end Discriminative Feature-oriented Gaussian Mixture Model (DF-GMM), to address the problem of discriminative region diffusion and find better fine-grained details. Specifically, DF-GMM consists of 1) a low-rank representation mechanism (LRM), which learns a set of low-rank discriminative bases by Gaussian Mixture Model (GMM) to accurately select discriminative details and filter more irrelevant information in high-level semantic feature maps, 2) a low-rank representation reorganization mechanism (LR2M) which resumes the space information of low-rank discriminative bases to reconstruct the low-rank feature maps. By recovering the low-rank discriminative bases into the same embedding space of highlevel feature maps, LR2M alleviates the discriminative region diffusion problem in high-level feature map and discriminative regions can be located more precisely on the new low-rank feature maps. Extensive experiments verify that DF-GMM yields the best performance under the same settings with the most competitive approaches, in CUBBird, Stanford-Cars datasets, and FGVC Aircraft.

关键词： Gaussian mixture model Correlation Feature extraction Kernel Image recognition Semantics

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Generating High-quality Movie Tags from Social Reviews: A Learning-driven Approach

Generating High-quality Movie Tags from Social Reviews: A Le...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Zhenxiao Luo Guopin Tang Chen Wang Yipeng Zhou Xi Zheng Jessie Hui Wang Gang Liu Di Wu School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Faculty of Science and Engineering Macquarie University Sydney Australia Institute for Network Sciences and Cyberspace Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781665417631

As a kind of keywords to describe video contents, tags are extremely beneficial for viewers to locate videos when they search the site. Additionally, tags also help video platform operators to better organize and recommend videos to platform users. Previous approaches are mainly based on manually tagging or tag propagation via video content analysis, which is time-consuming and resource-consuming. Especially, given the high volume of fresh movies generated per year, it is difficult to accurately tag all the movies manually. Moreover, video content analysis is also not easy considering typical features (e.g., long duration, complicated scenarios) of commercial movies. In this paper, we propose an automatic tagging algorithm called TagRec that exploits crowdsourced user reviews to generate accurate movie tags. We observe that user reviews contain rich information about movies (e.g., quality, actors) which can be learned to generate high-quality movie tags. Inspired by the above observation, we choose to transform the movie video tagging problem into a tag recommendation problem, in which tags are recommended to different movies by extracting knowledge from crowdsourced movie reviews. We take latent topics, tag co-occurrence probability and tag semantics into account, and formulate the problem as a recommendation optimization problem. We evaluate the performance of our pro-posed TagRec algorithm with a large-scale real-world dataset. Extensive experiments demonstrate that TagRec achieves 7.1 % and 9.6% improvement compared with other state-of-the-art methods in terms of Hit Ratio and Normalized Discounted Cumulative Gain respectively.

关键词： Social computing Filtering Semantics Transforms Tagging Motion pictures Optimization

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Intelligence’s Dilemma: AI Algorithms May Cause Anti-Intelligence 1

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4th International Conference on Cognitive Systems and Information Processing, ICCSIP 2018

作者： Zhang, Yuhong Nauman, Umer Network and Data Security Key Laboratory of Sichuan University of Electronic Science and Technology of China Chengdu610054 China Information Science and Engineering College Henan University of Technology Zhengzhou450001 China

ISBN: (数字)9789811379833

ISBN: (纸本)9789811379826

As the intelligent age continues and technology gets more advanced, algorithms offer some of the solutions for our better work and life. However, research suggests that the proliferation and abuse of intelligent algorithms may lead us to a decline in cognitive performance. In this paper, we first discuss the role of intelligent algorithms in terms of highlighting the value of data. Next, we analyse the non-neutrality of these data-driven algorithms. Further, we analyze the intrinsic discrimination of AI algorithm based on prejudice psychology. Next, we argue that overusing AI algorithms and strengthening the dependency on them will lead to lower our cognitive level in terms of slow thinking. Thus, the conclusion is that, in the intelligent age, the more intelligent algorithms we use, the lower our cognitive level will be. This is the dilemma of intelligence. Finally, we offer some possible principles that may apply to alleviate the above disadvantageous scenarios. © 2019, Springer Nature Singapore Pte Ltd.

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Correction: A scale-adaptive estimation for mixed geographically and temporally weighted regression models

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Journal of Geographical Systems 2025年第2期27卷 337-337页

作者： Hong, Zhimin Wang, Zhiwen Wang, Huhu Wang, Ruoxuan Department of Mathematics School of Science Inner Mongolia University of Technology Hohhot China Statistics Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling Hohhot China Institute for Infectious Disease and Endemic Disease Control Inner Mongolia Autonomous Region Center for Disease Control and Prevention Hohhot China

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