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Multi-year polarimetric monitoring of four CHIME-discovered repeating fast radio bursts with FAST

science China(Physics,Mechanics & Astronomy) 2025年

作者： Yi Feng Yong-Kun Zhang Jintao Xie Yuan-Pei Yang Yuanhong Qu Dengke Zhou Di Li Bing Zhang Weiwei Zhu Wenbin Lu Jiaying Xu Chenchen Miao Shiyan Tian Pei Wang Ju-Mei Yao Chen-Hui Niu Jiarui Niu Heng Xu Jinchen Jiang Dejiang Zhou Zenan Liu Chao-Wei Tsai Zigao Dai Xuefeng Wu Fayin Wang Jinlin Han Kejia Lee Renxin Xu Yongfeng Huang Yuanchuan Zou Jinhuang Cao Xianglei Chen Jianhua Fang Dongzi Li Ye Li Wanjin Lu Jiawei Luo Jintao Luo Rui Luo Fen Lyu Bojun Wang Weiyang Wang Qin Wu Mengyao Xue Di Xiao Wenfei Yu Jianping Yuan Chunfeng Zhang Junshuo Zhang Lei Zhang Songbo Zhang Rushuang Zhao Yuhao Zhu Institute for Frontiers in Astronomy and Astrophysics Beijing Normal University Departments of Astronomy and Theoretical Astrophysics Center University of California Berkeley Department of Astronomy School of Physics Huazhong University of Science and Technology Xinjiang Astronomical Observatory Chinese Academy of Sciences Institute of Astrophysics Central China Normal University School of Astronomy and Space Science Nanjing University University of Chinese Academy of Sciences Department of Astronomy School of Physical Sciences University of Science and Technology of China Department of Astronomy Peking University Kavli Institute for Astronomy and Astrophysics Peking University Research Center for Astronomical Computing Zhejiang Laboratory Key Laboratory of Modern Astronomy and Astrophysics (Nanjing University) Ministry of Education Department of Astrophysical Sciences Princeton University College of Physics Hebei Normal University National Time Service Center Chinese Academy of Sciences Department of Astronomy School of Physics and Materials Science Guangzhou University Institute of Astronomy and Astrophysics Anqing Normal University Shanghai Astronomical Observatory Chinese Academy of Sciences Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Institute for Astronomy School of Physics Zhejiang University National Astronomical Observatories Chinese Academy of Sciences School of Computer Science and Engineering Sichuan University of Science and Engineering South-Western Institute for Astronomy Research Yunnan University Purple Mountain Observatory Chinese Academy of Sciences Nevada Center for Astrophysics University of Nevada Department of Physics and Astronomy University of Nevada New Cornerstone Science Laboratory Department of Astronomy Tsinghua University

Fast radio bursts（FRBs） are bright,millisecond-duration radio emissions originating from cosmological *** this study,we report multi-year polarization measurements of four repeating FRBs initially discovered by the Canadian Hydrogen Intensity Mapping Experiment（CHIME）:FRBs 20190117 A,20190208 A,20190303 A,and 20190417 *** observed the four repeating FRBs with the Five-hundred-meter Aperture Spherical Radio Telescope（FAST）,detecting a total of 66 *** bursts from FRB 20190417 A exhibit a circular polarization signal-to-noise ratio greater than 7,with the highest circular polarization fraction recorded at 35.7%.While the bursts from FRBs 20190208 A and 20190303 A are highly linearly polarized,those from FRBs 20190117 A and 20190417 A show depolarization due to multi-path propagation,with σRM=2.78±0.05 and 5.19±0.09 rad m-2,*** linear polarization distributions among five repeating FRBs—FRBs 20190208 A,20190303 A,20201124 A,20220912 A,and 20240114 A—are nearly identical but show distinct differences from those of nonrepeating *** 20190117 A,20190303 A,and 20190417 A exhibit substantial rotation measure（RM） variations between bursts,joining other repeating FRBs in this *** these findings with published results,64% of repeating FRBs show RM variations greater than 50 rad m-2,and 21% exhibit RM reversals.A significant proportion of repeating FRBs reside in a dynamic magneto-ionic *** structure function of RM variations shows a power-law index of γ～（0-0.8）,corresponding to a shallow power spectrum α=-（γ+2）～-（2.0-2.8） of turbulence,if the RM variations are attributed to *** suggests that the variations are dominated by small-scale RM density *** perform K-S tests to compare the RMs of repeating and non-repeating FRBs,which reveal a marginal dichotomy in the distribution of their *** caution that the observed dichotomy may be due to the small sample size and selection biases.

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Insect Identification in Pulsed Lidar Images Using Changepoint Detection Algorithms

Insect Identification in Pulsed Lidar Images Using Changepoi...

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Intermountain Engineering, Technology and computing (IETC)

作者： Nathaniel Sweeney Caroline Xu Joseph A. Shaw Toby D. Hocking Bradley M. Whitaker Electrical and Computer Engineering Department Montana State University USA Electrical Engineering and Computer Science Department University of Michigan USA Optical Technology Center Montana State University USA School of Informatics Computing and Cyber Systems Northern Arizona University USA

Noninvasive entomological insect monitoring often utilizes a variety of tools such as LiDAR to gather information without interfering with the insects in their habitat. These collection methods often result in large amounts of data that can be te-dious and lengthy to interpret and analyze. Machine learning has been previously used in the past in order to analyze Li-DAR images to detect insects, but often suffers from pitfalls such as long training times and large computational power requirements. In an attempt to offer an alternative that takes little to no training on the data and much less computational power, this paper looks at the use of changepoint detection algorithms to analyze LiDAR images containing insects. By analyzing the rows or columns of a LiDAR image, the algorithms should be able to detect abrupt changes in the image that would represent the insects. While not as accurate, the changepoint detection algorithms give comparable results to a machine learning algorithm tested on the same dataset without the need for supervised training.

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Gut Microbiome as a Risk Factor for Future CKD

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Kidney International Reports 2025年第6期10卷 1673-1682页

作者： Hellman, Tapio Yeo, Li-Fang Palmu, Joonatan Havulinna, Aki Jousilahti, Pekka Laitinen, Ville Pärnänen, Katariina Salomaa, Veikko Lahti, Leo Knight, Rob Niiranen, Teemu Kidney Center Turku University Hospital and University of Turku Turku Finland Division of Medicine University of Turku Turku Finland Department of Internal Medicine Turku University Hospital and University of Turku Turku Finland Department of Public Health Finnish Institute for Health and Welfare Helsinki Finland Institute for Molecular Medicine Finland (FIMM) HiLIFE University of Helsinki Helsinki Finland Department of Computing University of Turku Turku Finland Department of Pediatrics University of California San Diego La Jolla CA United States Center for Microbiome Innovation Joan and Irwin Jacobs School of Engineering University of California San Diego La Jolla CA United States Department of Bioengineering University of California San Diego La Jolla CA United States Department of Computer Science and Engineering University of California San Diego La Jolla CA United States Halıcıoğlu Data Science Institute University of California La Jolla San Diego CA United States

Introduction: Gut microbiome has been linked with chronic kidney disease (CKD) in several small cross-sectional studies. However, the relationship between baseline gut microbiome and long-term incident CKD remains unknown. Methods: We performed fecal sampling and measured serum creatinine (SCR) (N = 6699) and urine albumin-to-creatinine ratio (UACR) (N = 797) in a population-based cohort examined in the year 2002. We assessed the multivariable-adjusted associations of gut metagenome with baseline SCR, baseline UACR, and register-based incident CKD. Results: The mean age of the participants was 49.5 ± 12.9 years and 45.8% were men. During a median follow-up of 18.6 years, 108 participants developed incident CKD. In prospective analyses, increased baseline gut microbiome alpha diversity was associated with lower risk of incident CKD (hazard ratio per 1 SD: 0.84;95% confidence interval [CI]: 0.71–0.99;P = 0.04). Gut microbial beta diversity and taxa were not related to incident CKD (P ≥ 0.09 for all). In cross-sectional analyses, alpha diversity (beta per 1 SD: 1.28;95% CI: 0.64–1.98;P < 0.001) and beta diversity (P = 0.002;R² = 0.12%) were associated with SCR, whereas no associations were observed for UACR. In total, 43 significant species-level associations with SCR were observed and 16 negative associations (37.2%) for species belonging to the Lachnospiraceae family. Conclusion: Our results suggest that decreased gut microbial diversity may be related to risk of future CKD and that a potential link between the Lachnospiraceae family and desirable kidney health exists. Our results extend previous cross-sectional studies and help to establish the basis for examining gut microbiome as a CKD risk factor. © 2025 International Society of Nephrology

关键词： chronic kidney disease clinical study microbiology proteinuria

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Bc²FL: Double-Layer Blockchain-Driven Federated Learning Framework for Agricultural IoT

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IEEE Internet of Things Journal 2024年第4期12卷 4362-4374页

作者： Qingyang Ding Xiaofei Yue Qinnan Zhang Zehui Xiong Jinping Chang Hongwei Zheng School of Management Beijing Union University Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China Institute of Artificial Intelligence Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang University Beijing China Information Systems Technology and Design Pillar Singapore University of Technology and Design Tampines Singapore Blockchain and Privacy Computing Technology R&D Department Beijing Academy of Blockchain and Edge Computing Beijing China

With the flourishing of the Agricultural Internet of Things (AIoT), analyzing large-volume sensor data has become a regular requirement for agricultural decision-making. Federated learning (FL), which facilitates scattered AIoT devices to train models collaboratively, has gained significant attention. However, traditional FL poses challenges in AIoT scenarios, such as wide geo-distribution, heterogeneous data distribution, and high-device risks. Existing works tend to be one-sided and remain unclear on how to tackle these issues thoroughly in AIoT. To fill the gap, we present Bc2FL, a double-layer blockchain-based FL framework, which enhances both learning efficiency and security for AIoT. The double-layer blockchain, coupled with a two-stage consensus algorithm, drives the hierarchical FL process to enable efficient and reliable agricultural knowledge-sharing. In addition, Bc2FL adopts an adaptive model aggregation algorithm to dynamically tune noise levels based on the model quality, further improving the learning security and model credibility. Finally, the extensive experimental results demonstrate that Bc2FL not only improves the model accuracy by up to 21.17% compared with the state-of-the-art baselines, but also enhances the privacy protection within an additional error of only 2.1%.

关键词： Blockchains Internet of Things Privacy Computational modeling Federated learning Data models Biological system modeling Adaptation models Accuracy Servers

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A non-asymptotic analysis for stein variational gradient descent 34

A non-asymptotic analysis for stein variational gradient des...

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34th Conference on Neural Information Processing Systems, NeurIPS 2020

作者： Korba, Anna Salim, Adil Arbel, Michael Luise, Giulia Gretton, Arthur Gatsby Computational Neuroscience Unit University College London United Kingdom Visual Computing Center KAUST Computer Science Department University College London United Kingdom

We study the Stein Variational Gradient Descent (SVGD) algorithm, which optimises a set of particles to approximate a target probability distribution p ? e-V on Rd. In the population limit, SVGD performs gradient descent in the space of probability distributions on the KL divergence with respect to p, where the gradient is smoothed through a kernel integral operator. In this paper, we provide a novel finite time analysis for the SVGD algorithm. We provide a descent lemma establishing that the algorithm decreases the objective at each iteration, and rates of convergence for the averaged Stein Fisher divergence (also referred to as Kernel Stein Discrepancy). We also provide a convergence result of the finite particle system corresponding to the practical implementation of SVGD to its population version. © 2020 Neural information processing systems foundation. All rights reserved.

关键词： Probability distributions

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Parallel Approximations for High-Dimensional Multivariate Normal Probability Computation in Confidence Region Detection Applications

Parallel Approximations for High-Dimensional Multivariate No...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Xiran Zhang Sameh Abdulah Jian Cao Hatem Ltaief Ying Sun Marc G. Genton David E. Keyes Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Statistics Program King Abdullah University of Science and Technology Extreme Computing Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Mathematics The University of Houston Houston Texas USA

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Addressing the statistical challenge of computing the multivariate normal (MVN) probability in high dimensions holds significant potential for enhancing various applications. For example, the critical task of detecting confidence regions where a process probability surpasses a specific threshold is essential in diverse applications, such as pinpointing tumor locations in magnetic resonance imaging (MRI) scan images, determining hydraulic parameters in groundwater flow issues, and forecasting regional wind power to optimize wind turbine placement, among numerous others. One common way to compute high-dimensional MVN probabilities is the Separation-of-Variables (SOV) algorithm. This algorithm is known for its high computational complexity of O(n 3 ) and space complexity of O(n 2 ), mainly due to a Cholesky factorization operation for an n×n covariance matrix, where n represents the dimensionality of the MVN problem. This work proposes a high-performance computing framework that allows scaling the SOV algorithm and, subsequently, the confidence region detection algorithm. The framework leverages parallel linear algebra algorithms with a task-based programming model to achieve performance scalability in computing process probabilities, especially on large-scale systems. In addition, we enhance our implementation by incorporating Tile Low-Rank (TLR) approximation techniques to reduce algorithmic complexity without compromising the necessary accuracy. To evaluate the performance and accuracy of our framework, we conduct assessments using simulated data and a wind speed dataset. Our proposed implementation effectively handles high-dimensional multivariate normal (MVN) probability computations on shared and distributed-memory systems using finite precision arithmetics and TLR approximation computation. Performance results show a significant speedup of up to 20X in solving the MVN problem using TLR approximation compared to the reference dense solution without sacrificin

关键词： Accuracy Tensors Wind speed Magnetic resonance imaging Linear algebra Wind power generation Approximation algorithms

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Integrated Sensing, Localization, and Communication in Holographic MIMO-enabled Wireless Network: A Deep Learning Approach

TechRxiv

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TechRxiv 2023年

作者： Adhikary, Apurba Munir, Md. Shirajum Raha, Avi Deb Qiao, Yu Han, Zhu Hong, Choong Seon Department of Computer Science and Engineering School of Computing Kyung Hee University Yongin-si17104 Korea Republic of Virginia Modeling Analysis and Simulation Center Department of Electrical and Computer Engineering Old Dominion University SuffolkVA23435 United States Department of Computer Science and Engineering Kyung Hee University Yongin-si17104 Korea Republic of Department of Artificial Intelligence School of Computing Kyung Hee University Yongin-si17104 Korea Republic of Electrical and Computer Engineering Department University of Houston HoustonTX77004 United States

The impending sixth-generation wireless communication networks are anticipated to guarantee mass connectivity, high integration, and lower power consumption for generating the required beamforming. To achieve these goals, an artificial intelligence (AI) framework is proposed by utilizing holographic MIMO-assisted integrated sensing, localization, and communication. The proposed AI framework ensures lower power consumption to activate the minimum number of grids from the holographic grid array for the generation of holographic beamforming. An optimization problem is formulated to maximize the signal-to-interference-plus-noise ratio received by the users, which in turn maximizes the utility function for sensing considering the user distances, beampattern gains, sensing-communication loss, and dense locations controlling parameter. A novel AI-based framework is proposed to solve the formulated NP-hard optimization problem by decomposing it into two subproblems: the sensing problem and the communication resource allocation problem. First, a variational autoencoder (VAE) based mechanism is devised to solve the sensing problem mitigating the disputes to obtain the users’ exact location. Second, a sequential neural network-based scheme is utilized to allocate the communication resources to the heterogeneous users for generating the desired beamforming based on the findings of the VAE-based mechanism. Moreover, an extreme case power allocation strategy is presented once a large number of users enter the system. The extreme case power allocation strategy applies when the total power prediction exceeds the total system power for allocating the communication resources to the users. Finally, simulation results validate that the proposed AI-based framework outperforms the long short-term memory method with a cumulative power savings of 34.02% taking the ground truth power into account. Therefore, the proposed AI framework generates effective beamforming to serve the communicatio

关键词： Beamforming

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Feature Selection for Detecting ICMPv6-Based DDoS Attacks Using Binary Flower Pollination Algorithm

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computer Systems science & Engineering 2023年第10期47卷 553-574页

作者： Adnan Hasan Bdair Aighuraibawi Selvakumar Manickam Rosni Abdullah Zaid Abdi Alkareem Alyasseri Ayman Khallel Dilovan Asaad Zebari Hussam Mohammed Jasim Mazin Mohammed Abed Zainb Hussein Arif National Advanced IPv6 Centre(NAv6) Universiti Sains MalaysiaPenang11800Malaysia Baghdad College of Economic Sciences University BaghdadIraq School of Computer Sciences Universiti Sains MalaysPenang11800Malaysia Information Technology Research and Development Center University of KufaNajafIraq College of Engineering University of Warith Al-AnbiyaaKarbalaIraq Faculty of Computing and Informatics Universiti Malaysia SabahSabahMalaysia Business Administration College of Administration and Financial SciencesImam Ja’afar Al-Sadiq UniversityBaghdad10001Iraq College of Computer Science and Information Technology University of AnbarRamadi AnbarIraq Department of Computer Science College of ScienceNawroz UniversityDuhok42001Kurdistan RegionIraq

Internet Protocol version 6(IPv6)is the latest version of IP that goal to host 3.4×10^(38)unique IP addresses of devices in the ***6 has introduced new features like Neighbour Discovery Protocol(NDP)and Address Auto-configuration ***6 needed several protocols like the Address Auto-configuration Scheme and Internet Control Message Protocol(ICMPv6).IPv6 is vulnerable to numerous attacks like Denial of Service(DoS)and Distributed Denial of Service(DDoS)which is one of the most dangerous attacks executed through ICMPv6 messages that impose security and financial ***,an Intrusion Detection System(IDS)is a monitoring system of the security of a network that detects suspicious activities and deals with amassive amount of data comprised of repetitive and inappropriate features which affect the detection rate.A feature selection(FS)technique helps to reduce the computation time and complexity by selecting the optimum subset of *** paper proposes a method for detecting DDoS flooding attacks(FA)based on ICMPv6 messages using a Binary Flower PollinationAlgorithm(BFPA-FA).The proposed method(BFPA-FA)employs FS technology with a support vector machine(SVM)to identify the most relevant,influential ***,The ICMPv6-DDoS dataset was used to demonstrate the effectiveness of the proposed method through different attack *** results show that the proposed method BFPAFA achieved the best accuracy rate(97.96%)for the ICMPv6 DDoS detection with a reduced number of features(9)to half the total(19)*** proven proposed method BFPA-FAis effective in the ICMPv6 DDoS attacks via IDS.

关键词： IPv6 ICMPv6 DDoS feature selection flower pollination algorithm anomaly detection

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IRS-Enhanced UAV Communication Networks: Securing Data with Hybrid Genetic and Gradient Descent Algorithms

IRS-Enhanced UAV Communication Networks: Securing Data with ...

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IEEE Conference on Wireless Communications and Networking

作者： Zina Chkirbene Ala Gouissem Ridha Hamila Unal Devrim Arafat Al-Dweik Kaya Kuru Electrical Engineering Qatar University Qatar College of Computing and Information Technology University of Doha for Science and Technology Qatar KINDI Center for Computing Research College of Engineering Qatar University Qatar Department of Computer and Information Engineering 4 KU 6G Research Center Khalifa University Abu Dhabi UAE School of Engineering University of Central Lancashire Preston UK

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In the rapidly advancing field of wireless communication, Unmanned Aerial Vehicles (UAVs) have become indispensable due to their extensive coverage capabilities and ability to access remote locations. Whether deployed as mobile base stations (BSs) or relays, UAVs significantly enhance network throughput and reliability. Alongside UAVs, Intelligent Reflecting Surfaces (IRS) have emerged as a cost-effective solution for improving communication quality through passive modulation arrays. Despite these advancements, the potential misuse of UAVs poses serious security risks, particularly in the form of communication eavesdropping. To address these challenges, this paper introduces a novel communication framework that integrates a UAV equipped with an adaptive IRS. The primary aim is to boost communication secrecy between BSs and multiple users, even in the presence of several UAV eavesdroppers. This objective is formulated as an optimization problem focused on maximizing the secrecy rate while considering UAV mobility constraints. To solve this non-convex problem, we propose a hybrid strategy that combines Genetic Algorithms and Gradient Descent techniques. This innovative approach efficiently determines suboptimal reflection angles and UAV trajectories for IRS-equipped UAVs, thereby enhancing the security of the communication network. This method not only addresses the complexity of the optimization but also provides a practical pathway to secure communications in environments with high eavesdropping risks.

关键词： Wireless networks Autonomous aerial vehicles Throughput Security Communication networks Reliability Relays Trajectory optimization Eavesdropping Testing

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SSI−FL: Self-sovereign identity based privacy-preserving federated learning

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Journal of Parallel and Distributed computing 2024年 191卷

作者： Haque, Rakib Ul Touhidul Hasan, A.S.M. Al-Hababi, Mohammed Ali Mohammed Zhang, Yuqing Xu, Dianxiang Department of Electrical & Computer Engineering University of Texas at San Antonio San Antonio United States School of Computer Science & Technology University of Chinese Academy of Sciences Beijing100049 China Division of Computing Analytics and Mathematics School of Science and Engineering University of Missouri-Kansas City Kansas CityMO64110 United States School of Computer Science & Technology University of Chinese Academy of Sciences Beijing101408 China National Computer Network Intrusion Protection Center University of Chinese Academy of Sciences Beijing100049 China

Traditional federated learning (FL) raises security and privacy concerns such as identity fraud, data poisoning attacks, membership inference attacks, and model inversion attacks. In the conventional FL, any entity can falsify its identity and initiate data poisoning attacks. Besides, adversaries (AD) holding the updated global model parameters can retrieve the plain text of the dataset by initiating membership inference attacks and model inversion attacks. To the best of our knowledge, this is the first work to propose a self-sovereign identity (SSI) and differential privacy (DP) based FL namely SSI−FL for addressing all the above issues. The first step in the SSI−FL framework involves establishing a secure connection based on blockchain-based SSI. This secure connection protects against unauthorized access attacks of any AD and ensures the transmitted data's authenticity, integrity, and availability. The second step applies DP to protect against model inversion attacks and membership inference attacks. The third step focuses on establishing FL with a novel hybrid deep learning to achieve better scores than conventional methods. The SSI−FL performance analysis is done based on security, formal, scalability, and score analysis. Moreover, the proposed method outperforms all the state-of-art techniques. © 2024

关键词： Blockchain

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