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Network Topology Overlapping Group Detection Based on h-Core Pruning

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Network Topology Overlapping Group Detection Based on h-Core...

6th International Conference on Intelligent computing and Signal Processing (ICSP)

作者： Qi Zhang Yan Liu Yuheng Cai Zhong Yuan Network Security Research Institute Zhengzhou University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing PLA Strategic Support Force Information Engineering University Zhengzhou China

ISBN: (纸本)9781665447300

The network group detection has a large number of application researches and has achieved certain results in network security, IP entity location and load balancing. However, due to the large scale of the network topology graph and the large number of terminal nodes, most of the existing overlapping group detection algorithms are relatively complex, and the data size that can be processed is limited, and cannot be directly applied to the network topology. This paper proposes a network topology overlapping group detection algorithm based on h-core pruning. This algorithm decomposes the network into smaller strong connected components, reduces the interference of noisy nodes to overlapping group discovery, and more efficiently detect the largest k-plex group on the target network. A large number of experiments show that the method in this paper is effective. Compared with the full-enum algorithm, our method increases the network size that can be handled by the largest k-plex group by two orders of magnitude, and can detect overlapping groups in the network topology more efficiently.

关键词： Network topology Signal processing algorithms Interference Signal processing Load management Topology Noise measurement

Automatic extraction of coronary arteries using deep learning in invasive coronary angiograms

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arXiv 2022年

作者： Meng, Yinghui Du, Zhenglong Zhao, Chen Dong, Minghao Pienta, Drew Xu, Zhihui Zhou, Weihua School of Computer and Communication Engineering Zhengzhou University of Light Industry Henan Zhengzhou China Department of Applied Computing Michigan Technological University HoughtonMI49931 United States DepartmentMechanical Engineering-Engineering Mechanics Michigan Technological University HoughtonMI49931 United States Department of Cardiology The First Affiliated Hospital of Nanjing Medical University Jiangsu Nanjing210000 China Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI49931 United States

Background. Accurate extraction of coronary arteries from invasive coronary angiography (ICA) is important in clinical decision-making for the diagnosis and risk stratification of coronary artery disease (CAD). In this study, we develop a method using deep learning to automatically extract the coronary artery lumen. Methods. A deep learning model U-Net 3+, which incorporates the full-scale skip connections and deep supervisions, was proposed for automatic extraction of coronary arteries from ICAs. Transfer learning and a hybrid loss function were employed in this novel coronary artery extraction framework. Results. A data set containing 616 ICAs obtained from 210 patients was used. In the technical evaluation, the U-Net 3+ achieved a Dice score of 0.8942 and a sensitivity of 0.8735, which is higher than U-Net ++ (Dice score: 0.8814, the sensitivity of 0.8331) and U-net (Dice score: 0.8799, the sensitivity of 0.8305). Conclusion. Our study demonstrates that the U-Net 3+ is superior to other segmentation frameworks for the automatic extraction of the coronary arteries from ICAs. This result suggests great promise for clinical use. © 2022, CC BY.

关键词： Image segmentation

Propagating Gottesman-Kitaev-Preskill states encoded in an optical oscillator

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arXiv 2023年

作者： Konno, Shunya Asavanant, Warit Hanamura, Fumiya Nagayoshi, Hironari Fukui, Kosuke Sakaguchi, Atsushi Ide, Ryuhoh China, Fumihiro Yabuno, Masahiro Miki, Shigehito Terai, Hirotaka Takase, Kan Endo, Mamoru Marek, Petr Filip, Radim van Loock, Peter Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan Advanced ICR Research Institute National Institute of Information and Communications Technology 588-2 Iwaoka Nishi Kobe651-2492 Japan Graduate School of Engineering Kobe University 1-1 Rokko-dai Nada Kobe657-0013 Japan Department of Optics Palacky University 17. listopadu 1192/12 Olomouc77146 Czech Republic Institute of Physics Johannes-Gutenberg University of Mainz Staudingerweg 7 Mainz55128 Germany

A quantum computer with low-error, high-speed quantum operations and capability for interconnections is required for useful quantum computations. A logical qubit called Gottesman-Kitaev-Preskill (GKP) qubit1 in a single Bosonic harmonic oscillator is efficient for mitigating errors in a quantum computer. The particularly intriguing prospect of GKP qubits is that entangling gates as well as syndrome measurements for quantum error correction only require efficient, noise-robust linear operations. To date, however, GKP qubits have been only demonstrated at mechanical and microwave frequency in a highly nonlinear physical system2,3. The physical platform that naturally provides the scalable linear toolbox is optics, including near-ideal loss-free beam splitters and near-unit efficiency homodyne detectors that allow to obtain the complete analog syndrome for optimized quantum error correction4. Additional optical linear amplifiers5,6 and specifically designed GKP qubit states are then all that is needed for universal quantum computing7–9. In this work, we realize a GKP state in propagating light at the telecommunication wavelength and demonstrate homodyne meausurements on the GKP states for the first time without any loss corrections. Our GKP states do not only show non-classicality and non-Gaussianity at room temperature and atmospheric pressure, but unlike the existing schemes with stationary qubits, they are realizable in a propagating wave system. This property permits large-scale quantum computation10–14 and interconnections15,16, with strong compatibility to optical fibers and 5G telecommunication technology17 Copyright © 2023, The Authors. All rights reserved.

关键词： Atmospheric pressure

Dual-View Pyramid Pooling in Deep Neural Networks for Improved Medical Image Classification and Confidence Calibration

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arXiv 2024年

作者： Zhang, Xiaoqing Nie, Qiushi Xiao, Zunjie Zhao, Jilu Wu, Xiao Guo, Pengxin Li, Runzhi Liu, Jin Wei, Yanjie Pan, Yi Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Statistics and Actuarial Science The University of Hong Kong 999077 Hong Kong Cooperative Innovation Center of Internet Healthcare Zhengzhou University Zhengzhou450001 China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi830046 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

Spatial pooling (SP) and cross-channel pooling (CCP) operators have been applied to aggregate spatial features and pixel-wise features from feature maps in deep neural networks (DNNs), respectively. Their main goal is to reduce computation and memory overhead without visibly weakening the performance of DNNs. However, SP often faces the problem of losing the subtle feature representations, while CCP has a high possibility of ignoring salient feature representations, which may lead to both miscalibration of confidence issues and suboptimal medical classification results. To address these problems, we propose a novel dual-view framework, the first to systematically investigate the relative roles of SP and CCP by analyzing the difference between spatial features and pixel-wise features. Based on this framework, we propose a new pooling method, termed dual-view pyramid pooling (DVPP), to aggregate multi-scale dual-view features. DVPP aims to boost both medical image classification and confidence calibration performance by fully leveraging the merits of SP and CCP operators from a dual-axis perspective. Additionally, we discuss how to fulfill DVPP with five parameter-free implementations. Extensive experiments on six 2D/3D medical image classification tasks show that our DVPP surpasses state-of-the-art pooling methods in terms of medical image classification results and confidence calibration across different DNNs. © 2024, CC BY.

关键词： Deep neural networks

Optically-Sampled Superconducting-Nanostrip Photon-Number Resolving Detector for Non-Classical Quantum State Generation

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arXiv 2024年

作者： Endo, Mamoru Takahashi, Kazuma Nomura, Takefumi Sonoyama, Tatsuki Yabuno, Masahiro Miki, Shigehito Terai, Hirotaka Kashiwazaki, Takahiro Inoue, Asuka Umeki, Takeshi Nehra, Rajveer Takase, Kan Asavanant, Warit Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan Advanced ICT Research Institute National Institute of Information and Communications Technology 588-2 Iwaoka Nishi Kobe651-2492 Japan NTT Device Technology Labs NTT Corporation 3-1 Morinosato Wakamiya Kanagawa Atsugi243-0198 Japan Department of Electrical and Computer Engineering University of Massachusetts Amherst AmherstMA01003 United States Department of Physics University of Massachusetts Amherst AmherstMA01003 United States College of Information and Computer Science University of Massachusetts Amherst AmherstMA01003 United States

Photon number-resolving detectors (PNRDs) are the ultimate optical sensors. Superconducting-nanostrip photon detectors (SNSPDs), traditionally known as ON-OFF detectors, have recently been found to have photon number resolving capability without multiplexing. This discovery positions them to become true PNRDs. However, their practical use is limited by the need to precisely detect tiny signal differences with low signal-to-noise ratios within sub-nanosecond time frames. We overcome this challenge using optical sampling with a dual-output Mach Zehnder modulator (DO-MZM) and ultra-short pulsed laser. By adjusting the DO-MZM’s bias voltage to nearly balance the outputs, this method enables sensitive detection of picosecond-order signal differences, achieving a temporal resolution of 1.9 ps and facilitating real-time photon number resolution. We applied this method to produce various non-classical quantum states, enhancing their non-classicality through photon number resolution. This advancement marks a significant shift from principle verification to practical application for SNSPD-type PNRDs in diverse quantum optics fields. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum optics

High-rate Generation and State Tomography of Non-Gaussian Quantum States for Ultra-fast Clock Frequency Quantum Processors

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arXiv 2024年

作者： Kawasaki, Akito Ide, Ryuhoh Brunel, Hector Suzuki, Takumi Nehra, Rajveer Nakashima, Katsuki Kashiwazaki, Takahiro Inoue, Asuka Umeki, Takeshi China, Fumihiro Yabuno, Masahiro Miki, Shigehito Terai, Hirotaka Yamashima, Taichi Sakaguchi, Atsushi Takase, Kan Endo, Mamoru Asavanant, Warit Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Department of Physics Ecole Normale Supérieure 24 rue Lhomond Paris75005 France Department of Electrical and Computer Engineering University of Massachusetts Amherst AmherstMA01003 United States Department of Physics University of Massachusetts Amherst AmherstMA01003 United States NTT Device Technology Labs NTT Corporation 3-1 Morinosato Wakamiya Kanagawa Atsugi243-0198 Japan Advanced ICR Research Institute National Institute of Information and Communications Technology 588-2 Iwaoka Nishi Kobe651-2492 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan

Quantum information processors greatly benefit from high clock frequency to fully harnessing the quantum advantages before they get washed out by the decoherence. In this pursuit, all-optical systems offer unique advantages due to their inherent 100 THz carrier frequency, permitting one to develop THz clock frequency processors. In practice, the bandwidth of the quantum light sources and the measurement devices has been limited to the MHz range and the generation rate of nonclassical states to kHz order—a tiny fraction of what can be achieved. In this work, we go beyond this limitation by utilizing optical parametric amplifier (OPA) as a squeezed-light source and optical phase-sensitive amplifiers (PSA) to realize high-rate generation of broadband non-Gaussian states and their quantum tomography. Our state generation and measurement system consists of a 6-THz squeezed-light source, a 6-THz PSA, and a 66-GHz homodyne detector. With this system, we have successfully demonstrated non-Gaussian state generation at a 0.9 MHz rate—almost three orders of magnitude higher than the current state-of-the-art experiments—with a sub-nanosecond wave packet using continuous-wave laser. The performance is constrained only by the superconducting detector’s jitter which currently limits the usable bandwidth of the squeezed light to 1 GHz, rather than the optical and electronic systems. Therefore, if we can overcome the limitation of the timing jitter of superconducting detector, non-Gaussian state generation and detection at GHz rate, or even THz rate, for optical quantum processors might be possible with OPAs. Copyright © 2024, The Authors. All rights reserved.

关键词： Optical parametric amplifiers

Erratum - Modeling for Improved Performance of Ultra-Fast Nonvolatile Toggle Spin Torque MRAM Bit-Cell (SPIN (2022) 12(2):2250013 DOI: 10.1142/S2010324722500138)

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SPIN 2022年第3期12卷

作者： Jain, Divyansh Dahiya, Sunil Verma, Gaurav Department of VLSI Design Center for Development of Advanced Computing Punjab Mohali 160071 India Department of Physical Sciences Indian Institute of Science Education and Research Punjab Mohali 140306 India Department of Electronics and Communication Engineering Indian Institute of Technology Uttarakhand Roorkee 247667 India

Incorrect author list: Divyansh Jain, Sunil Dahiya and Gaurav Verma Correct author list: Divyansh Jain and Sunil Dahiya All the co-authors are aware of the updated list of authors, to which the Publisher is not liable to be responsible for any future dispute on the authorship of the paper. © 2022 World Scientific Publishing Co. Pte Ltd. All rights reserved.

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FedVQCS: Federated Learning via Vector Quantized Compressed Sensing

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arXiv 2022年

作者： Oh, Yongjeong Jeon, Yo-Seb Chen, Mingzhe Saad, Walid The Department of Electrical Engineering POSTECH Gyeongbuk Pohang37673 Korea Republic of The Institute for Convergence Research and Education in Advanced Technology Yonsei University Incheon21983 Korea Republic of The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Wireless@VT Bradley Department of Electrical and Computer Engineering Virginia Tech ArlingtonVA22203 United States

In this paper, a new communication-efficient federated learning (FL) framework is proposed, inspired by vector quantized compressed sensing. The basic strategy of the proposed framework is to compress the local model update at each device by applying dimensionality reduction followed by vector quantization. Subsequently, the global model update is reconstructed at a parameter server by applying a sparse signal recovery algorithm to the aggregation of the compressed local model updates. By harnessing the benefits of both dimensionality reduction and vector quantization, the proposed framework effectively reduces the communication overhead of local update transmissions. Both the design of the vector quantizer and the key parameters for the compression are optimized so as to minimize the reconstruction error of the global model update under the constraint of wireless link capacity. By considering the reconstruction error, the convergence rate of the proposed framework is also analyzed for a non-convex loss function. Simulation results on the MNIST and FEMNIST datasets demonstrate that the proposed framework provides more than a 2.4% increase in classification accuracy compared to state-of-the-art FL frameworks when the communication overhead of the local model update transmission is 0.1 bit per local model entry. Copyright © 2022, The Authors. All rights reserved.

关键词： Vector quantization

Incremental Value and Interpretability of Radiomics Features of Both Lung and Epicardial Adipose Tissue for Detecting the Severity of COVID-19 Infection

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arXiv 2023年

作者： Yao, Ni Tian, Yanhui das Neves, Daniel Gama Zhao, Chen Mesquita, Claudio Tinoco de Andrade Martins, Wolney dos Santos, Alair Augusto Sarmet Moreira Damas Li, Yanting Han, Chuang Zhu, Fubao Dai, Neng Zhou, Weihua School of Computer and Communication Engineering Zhengzhou University of Light Industry Henan Zhengzhou China Department of Radiology Universidade Federal Fluminense Rio de Janeiro Brazil DASA Complexo Hospitalar de Niterói Rio de Janeiro Brazil Department of Applied Computing Michigan Technological University HoughtonMI United States Department of Cardiology Universidade Federal Fluminense Rio de Janeiro Brazil Department of Cardiology Zhongshan Hospital Fudan University Shanghai Institute of Cardiovascular Diseases Shanghai China National Clinical Research Center for Interventional Medicine Shanghai China Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI United States

Epicardial adipose tissue (EAT) is known for its pro-inflammatory properties and association with Coronavirus Disease 2019 (COVID-19) severity. However, current EAT segmentation methods do not consider positional information. Additionally, the detection of COVID-19 severity lacks consideration for EAT radiomics features, which limits interpretability. This study investigates the use of radiomics features from EAT and lungs to detect the severity of COVID-19 infections. A retrospective analysis of 515 patients with COVID-19 (Cohort1: 415, Cohort2: 100) was conducted using a proposed three-stage deep learning approach for EAT extraction. Lung segmentation was achieved using a published method. A hybrid model for detecting the severity of COVID-19 was built in a derivation cohort, and its performance and uncertainty were evaluated in internal (125, Cohort1) and external (100, Cohort2) validation cohorts. For EAT extraction, the Dice similarity coefficients (DSC) of the two centers were 0.972 (±0.011) and 0.968 (±0.005), respectively. For severity detection, the hybrid model with radiomics features of both lungs and EAT showed improvements in AUC, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) compared to the model with only lung radiomics features. The hybrid model exhibited an increase of 0.1 (p © 2023, CC BY.

关键词： COVID-19