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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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Multi-source Signal Fusion with Contrastive AutoEncoder for Emotion Classification

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IEEE Journal of Biomedical and Health Informatics 2025年

作者： Zhao, Shen Hu, Yuzhu Chen, Jian Wang, Wei Hu, Xiping Sun Yat-sen University School of Intelligent Systems Engineering Shenzhen518000 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China Shenzhen MSUBIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen518172 China Beijing Institute of Technology School of Medical Technology Beijing100081 China

Emotion recognition is of great importance for human-computer interaction. Emotion recognition technology based on physiological signals has shown great potential because of its strong objectivity and real-time capability. One of the most challenging tasks in this field is how to better fuse multi-source signals to extract information as comprehensively as possible. We propose a new framework for multi-source signal fusion and emotion recognition to address key challenges in feature alignment and representation learning. First, to reduce the distance between multi-source homogeneous signals in the feature space, we design a novel Contrastive Pairs AutoEncoder (CPAE), which is for feature alignment before aggregating the signals obtained from the Dual-LSTM. We also propose a designed cross-modal frequency module (CMF-Module), using a multi-layer perceptron (MLP) to learn the real and imaginary components of the signal's frequency representation, which integrates Resblock to achieve dual-channel time-domain and frequency-domain feature extraction. Furthermore, we incorporate the hidden ordinal relationships among emotional categories into the feature space through regression loss, and constrain the feature distribution using the Wasserstein distance. Experiments on public datasets show the best performance of our proposed method by comparing with baselines. We also conduct ablation studies to better verify the effect of the proposed method. © 2013 IEEE.

关键词： Emotion Recognition

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Correction to: Enhancing cyber defense strategies with discrete multi-dimensional Z-numbers: a multi-attribute decision-making approach

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Complex & Intelligent Systems 2025年第7期11卷 1-1页

作者： Yao, Aiting Huang, Chen Zhang, Weiqi Dong, Chengzu Lu, Meiqu Mao, Junjun Liu, Xiao Li, Xuejun Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging School of Computer Science and Technology Anhui University Hefei China AI Techplus Pty Ltd Organization Melbourne Australia School of Information Technology Deakin University Geelong Australia School of Data Science Lingnan University Hongkong China School of Artificial Intelligence Guangxi Minzu University Nanning China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China

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Universal deep network for steganalysis of color image based on channel representation

arXiv

引用

arXiv 2021年

作者： Wei, Kangkang Luo, Weiqi Tan, Shunquan Huang, Jiwu The Guangdong Key Lab of Information Security Technology School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China

Up to now, most existing steganalytic methods are designed for grayscale images, and they are not suitable for color images that are widely used in current social networks. In this paper, we design a universal color image steganalysis network (called UCNet) in spatial and JPEG domains. The proposed method includes preprocessing, convolutional, and classification modules. To preserve the steganographic artifacts in each color channel, in preprocessing module, we firstly separate the input image into three channels according to the corresponding embedding spaces (i.e. RGB for spatial steganography and YCbCr for JPEG steganography), and then extract the image residuals with 62 fixed high-pass filters, finally concatenate all truncated residuals for subsequent analysis rather than adding them together with normal convolution like existing CNN-based steganalyzers. To accelerate the network convergence and effectively reduce the number of parameters, in convolutional module, we carefully design three types of layers with different shortcut connections and group convolution structures to further learn high-level steganalytic features. In classification module, we employ a global average pooling and fully connected layer for classification. We conduct extensive experiments on ALASKA II to demonstrate that the proposed method can achieve state-of-the-art results compared with the modern CNN-based steganalyzers (e.g., SRNet and J-YeNet) in both spatial and JPEG domains, while keeping relatively few memory requirements and training time. Furthermore, we also provide necessary descriptions and many ablation experiments to verify the rationality of the network design. Copyright © 2021, The Authors. All rights reserved.

关键词： Steganography

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On the practicality of differential privacy in federated learning by tuning iteration times

arXiv

引用

arXiv 2021年

作者： Fu, Yao Zhou, Yipeng Wu, Di Yu, Shui Wen, Yonggang Li, Chao Department of Computer Science Sun Yatsen University Guangzhou510006 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China Department of Computing FSE Macquarie University 2122 Australia School of Computer Science University of Technology Sydney Australia School of Computer Science and Engineering Nanyang Technological University Singapore Co. Ltd China

In spite that Federated Learning (FL) is well known for its privacy protection when training machine learning models among distributed clients collaboratively, recent studies have pointed out that the naive FL is susceptible to gradient leakage attacks. In the meanwhile, Differential Privacy (DP) emerges as a promising countermeasure to defend against gradient leakage attacks. However, the adoption of DP by clients in FL may significantly jeopardize the model accuracy. It is still an open problem to understand the practicality of DP from a theoretic perspective. In this paper, we make the first attempt to understand the practicality of DP in FL through tuning the number of conducted iterations. Based on the FedAvg algorithm, we formally derive the convergence rate with DP noises in FL. Then, we theoretically derive: 1) the conditions for the DP based FedAvg to converge as the number of global iterations (GI) approaches infinity;2) the method to set the number of local iterations (LI) to minimize the negative influence of DP noises. By further substituting the Laplace and Gaussian mechanisms into the derived convergence rate respectively, we show that: 3) The DP based FedAvg with the Laplace mechanism cannot converge, but the divergence rate can be effectively prohibited by setting the number of LIs with our method;4) The learning error of the DP based FedAvg with the Gaussian mechanism can converge to a constant number finally if we use a fixed number of LIs per GI. To verify our theoretical findings, we conduct extensive experiments using two real-world datasets. The results not only validate our analysis results, but also provide useful guidelines on how to optimize model accuracy when incorporating DP into FL. Copyright © 2021, The Authors. All rights reserved.

关键词： Laplace transforms

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Lane Marking Detection Algorithm Based on High-Precision Map 6th

Lane Marking Detection Algorithm Based on High-Precision Map

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6th International Conference on data Science, ICDS 2019

作者： Yao, Haichang Chen, Chen Liu, Shangdong Li, Kui Ji, Yimu Wang, Ruchuan School of Computer and Software Nanjing Institute of Industry Technology Nanjing210023 China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Institute of High Performance Computing and Big Data Nanjing University of Posts and Telecommunications Nanjing210003 China Nanjing Center of HPC China Nanjing210003 China Jiangsu HPC and Intelligent Processing Engineer Research Center Nanjing210003 China

ISBN: (纸本)9789811528095

In case of sharp road illumination changes, bad weather such as rain, snow or fog, wear or missing of the lane marking, the reflective water stain on the road surface, the shadow obstruction of the tree, and mixed lane markings and other signs, missing detection or wrong detection will occur for the traditional lane marking detection algorithm. In this manuscript, a lane marking detection algorithm based on high-precision map is proposed. The basic principle of the algorithm is to use the centimeter-level high-precision positioning combined with high-precision map data to complete the detection of lane markings. The experimental results show that the algorithm has lower false detection rate in case of bad road conditions, and the algorithm is robust. © Springer Nature Singapore Pte Ltd 2020.

关键词： Road and street markings

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Probing Globular Cluster with MeerKAT and FAST: A Pulsar Polarization Census

arXiv

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arXiv 2025年

作者： Zhang, Lei Abbate, Federico Li, Di Possenti, Andrea Bailes, Matthew Ridolfi, Alessandro Freire, Paulo C.C. Ransom, Scott M. Zhang, Yong-Kun Guo, Meng Ni, Meng-Meng Hu, Jia-Le Feng, Yi Wang, Pei Zhang, Jie Zhi, Qi-Jun National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Centre for Astrophysics and Supercomputing Swinburne University of Technology VIC3122 Australia INAF Osservatorio Astronomico di Cagliari Via della Scienza 5 SelargiusI-09047 Italy Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany Department of Astronomy Tsinghua University Beijing100190 China Swinburne University of Technology VIC3122 Australia Faculty of Physics University of Bielefeld Bielefeld33501 Germany National Radio Astronomy Observatory 520 Edgemont Road Charlottesville22903 United States National Supercomputing Center in Jinan Qilu University of Technology Jinan250103 China Jinan Institute of Supercomputing Technology 28666 East Jingshi Road Jinan250103 China Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China Institute for Astronomy School of Physics Zhejiang University Hangzhou310027 China College of Physics and Electronic Engineering Qilu Normal University Jinan250200 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550001 China

Only one globular cluster (GC), 47 Tuc, has been found to containintracluster medium, with an electron density 100 times higher than that of theISM in its vicinity. The characteristics of this intracluster medium areclosely related to GC evolution and the compact objects within. However,significant knowledge gaps remain regarding the ionized gas content of GCs,particularly in Galactic halo clusters. We carried out a polarization census ofGC pulsars using MeerKAT and FAST. This first combined effort of observationsfrom these two major radio telescopes resulted in high signal-to-noise ratio,full polarization pulse profiles for 43 pulsars in 8 GCs, doubling the numberof rotation measures (RMs) known in these clusters. The accuracy of dispersionmeasures (DMs) was improved by a factor of 8 compared to previous *** intracluster medium was found, and at least two halo GCs showed morestringent upper limits on electron density than that detected in 47 Tuc. Thesurprising barrenness of GCs suggests effective gas removal mechanisms, such asstrong winds from millisecond pulsars and/or ionizing radiation from post-AGBstars and young white dwarfs. Copyright © 2025, The Authors. All rights reserved.

关键词： White dwarfs

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Study of Star/Galaxy Classification Based on the XGBoost Algorithm

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Chinese Astronomy and Astrophysics 2019年第4期43卷 539-548页

作者： Chao, Li Wen-hui, Zhang Ji-ming, Lin College of Information and Communication Engineering Guilin University of Electronic Technology Guilin 541004 Key Laboratory of Cognitive Radio and Information Processing the Ministry of Education Guilin University of Electronic Technology Guilin 541004 Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin 541004 Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems Guilin University of Electronic Technology Guilin 541004

Machine learning has achieved great success in many areas today. The lifting algorithm has a strong ability to adapt to various scenarios with a high accuracy, and has played a great role in many fields. But in astronomy, the application of lifting algorithms is still rare. In response to the low classification accuracy of the dark star/galaxy source set in the Sloan Digital Sky Survey (SDSS), a new research result of machine learning, eXtreme Gradient Boosting (XGBoost), has been introduced. The complete photometric data set is obtained from the SDSS-DR7, and divided into a bright source set and a dark source set according to the star magnitude. Firstly, the ten-fold cross-validation method is used for the bright source set and the dark source set respectively, and the XGBoost algorithm is used to establish the star/galaxy classification model. Then, the grid search and other methods are used to adjust the XGBoost parameters. Finally, based on the galaxy classification accuracy and other indicators, the classification results are analyzed, by comparing with the models of function tree (FT), Adaptive boosting (Adaboost), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Stacked Denoising AutoEncoders (SDAE), and Deep Belief Nets (DBN). The experimental results show that, the XGBoost improves the classification accuracy of galaxies in the dark source classification by nearly 10% as compared to the function tree algorithm, and improves the classification accuracy of sources with the darkest magnitudes in the dark source set by nearly 5% as compared to the function tree algorithm. Compared with other traditional machine learning algorithms and deep neural networks, the XGBoost also has different degrees of improvement. © 2019

关键词： Stars: fundamental parameters—galaxies: fundamental parameters—techniques: photometric—methods: data analysis

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Reverse Perspective Network for Perspective-Aware Object Counting

Reverse Perspective Network for Perspective-Aware Object Cou...

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yifan Yang Guorong Li Zhe Wu Li Su Qingming Huang Nicu Sebe School of Computer Science and Technology UCAS Beijing China Key Lab of Big Data Mining and Knowledge Management UCAS Beijing China Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing China University of Trento Trento Italy

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

One of the critical challenges of object counting is the dramatic scale variations, which is introduced by arbitrary perspectives. We propose a reverse perspective network to solve the scale variations of input images, instead of generating perspective maps to smooth final outputs. The reverse perspective network explicitly evaluates the perspective distortions, and efficiently corrects the distortions by uniformly warping the input images. Then the proposed network delivers images with similar instance scales to the regressor. Thus the regression network doesn't need multi-scale receptive fields to match the various scales. Besides, to further solve the scale problem of more congested areas, we enhance the corresponding regions of ground-truth with the evaluation errors. Then we force the regressor to learn from the augmented ground-truth via an adversarial process. Furthermore, to verify the proposed model, we collected a vehicle counting dataset based on Unmanned Aerial Vehicles (UAVs). The proposed dataset has fierce scale variations. Extensive experimental results on four benchmark datasets show the improvements of our method against the state-of-the-arts.

关键词： Distortion Training Feature extraction Estimation Convolution Kernel Adaptation models

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Structure-aware random fourier kernel for graphs 21

Structure-aware random fourier kernel for graphs

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Proceedings of the 35th International Conference on Neural Information processing Systems

作者： Jinyuan Fang Qiang Zhang Zaiqiao Meng Shangsong Liang School of Computer Science and Engineering Sun Yat-sen University China and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Hangzhou Innovation Center Zhejiang University China and College of Computer Science and Technology Zhejiang University China and AZFT Knowledge Engine Lab China School of Computing Science University of Glasgow United Kingdom and Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates School of Computer Science and Engineering Sun Yat-sen University China and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China and Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates

ISBN: (纸本)9781713845393

Gaussian Processes (GPs) define distributions over functions and their generalization capabilities depend heavily on the choice of kernels. In this paper, we propose a novel structure-aware random Fourier (SRF) kernel for GPs that brings several benefits when modeling graph-structured data. First, SRF kernel is defined with a spectral distribution based on the Fourier duality given by the Bochner's theorem, transforming the kernel learning problem to a distribution inference problem. Second, SRF kernel admits a random Fourier feature formulation that makes the kernel scalable for optimization. Third, SRF kernel enables to leverage geometric structures by taking subgraphs as inputs. To effectively optimize GPs with SRF kernel, we develop a variational EM algorithm, which alternates between an inference procedure (E-step) and a learning procedure (M-step). Experimental results on five real-world datasets show that our model can achieve state-of-the-art performance in two typical graph learning tasks, i.e., object classification and link prediction.

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