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On Star Coloring of Several Corona Graphs

Journal of Physics: Conference Series 2021年第1期1738卷

作者： Jin Cai Shuangliang Tian Zhuomo An School of Mathematics and Computer Science Northwest for Minzu University Lanzhou 730030 China Key Laboratory of Streaming Data Computing Technologies and Applications Northwest Minzu University Lanzhou 730030 China

Let G be a simple graph with vertex set V(G) and edge set E(G). A vertex coloring of G is called a star coloring of G if any of the paths of 4 order are bicolored. The minimum number of colors required for a star coloring of G is denoted by χs(G). The corona product of simple graphs G of order m and H of order n is graph G ∘ H with vertex set V(G ∘ H) = {vi|i = 1,2,⋯m}∪{vij|i = 1,2,⋯m, j = 1,2,⋯n}, in which vi is adjacent to every vertex of Hi if and only if, vi ∈ V(G), vij ∈ V(Hi). According to the existing graph dyeing literature, it has become a very important technical means to study the graph dyeing problem by using the graph structure operation. Therefore, it is of great significance to study the star coloring of graphs for studying the acyclic coloring and distance coloring of graphs, the study has strong application background and great theoretical value for computing graphs. In this paper, we find the upper bound of χs(G ∘ H) and the exact values of χs(G ∘ H) of the corona product G ∘ H of two graphs G and H as: χs(G ∘ H) ≤ χs(G) + χs(H); χs(Pm ∘ H) = χs(H) + 2; χs(K1,m ∘ H) = χs(H) + 2; χs(Cn ∘ H) = χs(H) + 2, where n ≠ 5.

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Corrigendum to “Existence ofSolutions to Non-Coercivity Quasilinear Elliptic Problem”

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Journal of Function Spaces 2021年第1期2021卷

作者： Jimao Xiawu Shuibo Huang Yingyuan Mi Maoji Ri School of Mathematics and Computer Science Northwest Minzu University Lanzhou Gansu 730030 *** Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University Lanzhou Gansu 730030 ***

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Non-invasively identifying candidates of active surveillance for prostate cancer using magnetic resonance imaging radiomics

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Visual computing for Industry,Biomedicine,and Art 2024年第1期7卷 209-219页

作者： Yuwei Liu Litao Zhao Jie Bao Jian Hou Zhaozhao Jing Songlu Liu Xuanhao Li Zibing Cao Boyu Yang Junkang Shen Ji Zhang Libiao Ji Zhen Kang Chunhong Hu Liang Wang Jiangang Liu Department of Radiology Beijing Friendship HospitalCapital Medical UniversityBeijing 100050China School of Engineering Medicine Beihang UniversityBeijing 100191China Key Laboratory of Big Data-Based Precision Medicine(Beihang University) Ministry of Industry and Information Technology of the People’s Republic of ChinaBeijing 100191China School of Biological Science and Medical Engineering Beihang UniversityBeijing 100191China Department of Radiology the First Affiliated Hospital of Soochow UniversitySuzhou 215006Jiangsu ProvinceChina Department of CT-MR Center the People’s Hospital of JimoQingdao 266200Shandong ProvinceChina Department of Radiology Sinopharm Tongmei General HospitalDatong 037003Shanxi ProvinceChina Department of Urology Beijing Friendship HospitalCapital Medical UniversityBeijing 100050China Department of Radiology the Second Affiliated Hospital of Soochow UniversitySuzhou 215004Jiangsu ProvinceChina Department of Radiology the People’s Hospital of TaizhouTaizhou 225399Jiangsu ProvinceChina Department of Radiology Changshu No.1 People’s HospitalChangshu 215501Jiangsu ProvinceChina Department of Radiology Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030Hubei ProvinceChina Beijing Engineering Research Center of Cardiovascular Wisdom Diagnosis and Treatment Beijing 100191China

Active surveillance(AS)is the primary strategy for managing patients with low or favorable-intermediate risk prostate cancer(PCa).Identifying patients who may benefit from AS relies on unpleasant prostate biopsies,which entail the risk of bleeding and *** the current study,we aimed to develop a radiomics model based on prostate magnetic resonance images to identify AS candidates non-invasively.A total of 956 PCa patients with complete biopsy reports from six hospitals were included in the current multicenter retrospective *** National Comprehensive Cancer Network(NCCN)guidelines were used as reference standards to determine the AS *** discriminate between AS and non-AS candidates,five radiomics models(i.e.,eXtreme Gradient Boosting(XGBoost)AS classifier(XGB-AS),logistic regression(LR)AS classifier,random forest(RF)AS classifier,adaptive boosting(Ada-Boost)AS classifier,and decision tree(DT)AS classifier)were developed and externally validated using a three-fold cross-center validation based on five classifiers:XGBoost,LR,RF,AdaBoost,and *** under the receiver operating characteristic curve(AUC),accuracy(ACC),sensitivity(SEN),and specificity(SPE)were calculated to evaluate the performance of these ***-AS exhibited an average of AUC of 0.803,ACC of 0.693,SEN of 0.668,and SPE of 0.841,showing a better comprehensive performance than those of the other included radiomic ***,the XGB-AS model also presented a promising performance for identifying AS candidates from the intermediate-risk cases and the ambiguous cases with diagnostic discordance between the NCCN guidelines and the Prostate Imaging-Reporting and data System *** results suggest that the XGB-AS model has the potential to help identify patients who are suitable for AS and allow non-invasive monitoring of patients on AS,thereby reducing the number of annual biopsies and the associated risks of bleeding and infection.

关键词： Active surveillance Prostate cancer Magnetic resonance imaging Radiomics

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Image reconstruction from photoacoustic projections

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Photonics Insights 2024年第3期3卷 145-209页

作者： Chao Tian Kang Shen Wende Dong Fei Gao Kun Wang Jiao Li Songde Liu Ting Feng Chengbo Liu Changhui Li Meng Yang Sheng Wang Jie Tian Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefeiChina School of Engineering Science University of Science and Technology of ChinaHefeiChina Department of Anesthesiology the First Affiliated Hospital of USTCDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiChina Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing HefeiChina College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjingChina School of Information Science and Technology ShanghaiTech UniversityShanghaiChina Shanghai Clinical Research and Trial Center ShanghaiChina CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijingChina School of Precision Instruments and Optoelectronics Engineering Tianjin UniversityTianjinChina Academy for Engineering and Technology Fudan UniversityShanghaiChina Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhenChina Department of Biomedical Engineering College of Future TechnologyPeking UniversityBeijingChina National Biomedical Imaging Center Peking UniversityBeijingChina Departments of Ultrasound State Key Laboratory of Complex Severe and Rare DiseasesPeking Union Medical College HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina School of Engineering Medicine Beihang UniversityBeijingChina Key Laboratory of Big Data-Based Precision Medicine Beihang UniversityMinistry of Industry and Information TechnologyBeijingChina

Photoacoustic computed tomography(PACT)is a rapidly developing biomedical imaging modality and has attracted substantial attention in recent *** reconstruction from photoacoustic projections plays a critical role in image formation in *** we review six major classes of image reconstruction approaches developed in the past three decades,including delay and sum,filtered back projection,series expansion,time reversal,iterative reconstruction,and deep-learning-based *** principal ideas and implementations of the algorithms are summarized,and their reconstruction performances under different imaging scenarios are *** challenges,future directions,and perspectives for the development of image reconstruction algorithms in PACT are also *** review provides a self-contained reference guide for beginners and specialists in the photoacoustic community,to facilitate the development and application of novel photoacoustic image reconstruction algorithms.

关键词： optical imaging photoacoustic tomography image reconstruction deep learning

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Energy-Efficient Cell-Free Massive MIMO Through Sparse Large-Scale Fading Processing

arXiv

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arXiv 2022年

作者： Chen, Shuaifei Zhang, Jiayi Björnson, Emil Demir, Özlem Tuğfe Ai, Bo The School of Electronic and Information Engineering The Frontiers Science Center for Smart Highspeed Railway System Beijing Jiaotong University Beijing100044 China Purple Mountain Laboratories Nanjing211111 China The Department of Computer Science KTH Royal Institute of Technology Kista164 40 Sweden The Department of Electrical and Electronics Engineering TOBB University of Economics and Technology Ankara Turkey The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China

Cell-free massive multiple-input multiple-output (CF mMIMO) systems serve the user equipments (UEs) by geographically distributed access points (APs) by means of joint transmission and reception. To limit the power consumption due to fronthaul signaling and processing, each UE should only be served by a subset of the APs, but it is hard to identify that subset. Previous works have tackled this combinatorial problem heuristically. In this paper, we propose a sparse distributed processing design for CF mMIMO, where the AP-UE association and long-term signal processing coefficients are jointly optimized. We formulate two sparsity-inducing mean-squared error (MSE) minimization problems and solve them by using efficient proximal approaches with block-coordinate descent. For the downlink, more specifically, we develop a virtually optimized large-scale fading precoding (V-LSFP) scheme using uplink-downlink duality. The numerical results show that the proposed sparse processing schemes work well in both uplink and downlink. In particular, they achieve almost the same spectral efficiency as if all APs would serve all UEs, while the energy efficiency is 2-4 times higher thanks to the reduced processing and signaling. Copyright © 2022, The Authors. All rights reserved.

关键词： Energy efficiency

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Event-triggering-based H_∞ load frequency control for multi-area cyber–physical power system under DoS attacks

Franklin Open

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Franklin Open 2023年 3卷

作者： Liu, Xingyue Shi, Kaibo Zhou, Kun Wen, Shiping Tang, Yiqian Tang, Lin School of Electronic Information and Electrical Engineering Chengdu University Chengdu 610106 China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang University Hangzhou 310018 China Australian AI Institute Faculty of Engineering and Information Technology University of Technology Sydney Sydney 2007 NSW Australia Engineering Research Center of Power Quality of Ministry of Education Anhui University Hefei 230601 China Institute of Electronic and Information Engineering of University of Electronic Science and Technology of China in Guangdong 523808 China

In this work, the event-triggering-based resilient H∞ load frequency control (LFC) scheme under denial-of-service (DoS) attacks is developed for multi-area cyber–physical power system (MA-CPPS) with electric vehicle... 详细信息

In this work, the event-triggering-based resilient H_∞ load frequency control (LFC) scheme under denial-of-service (DoS) attacks is developed for multi-area cyber–physical power system (MA-CPPS) with electric vehicles (EVs). In MA-CPPS, the sampled measurements are transmitted through the communication network, which may be subjected to the energy-limited DoS attack with a resilient index of maximum allowable continuous data losses. Meanwhile, a novel adaptive event-triggering mechanism (AETM) characterized by parameters disturbances and dynamic adjustment mechanism of triggering threshold can effectively reduce the occupation of network bandwidth and computational burden in practical operation of sampled-data LFC in MA-CPPS. Hence a sampled-data LFC model of considering energy-limited DoS attack and AETM is established firstly. Then based on a novel AETM transmission interval-dependent loop functional approach, a resilient H_∞ state-feedback controller is designed with guaranteed H_∞ performance and resilient index of energy-limited DoS attack. Finally, the effectiveness and superiority of our proposed scheme are verified by one-area and three-areas LFC CPPS. © 2023 The Authors

关键词： Adaptive event-triggering mechanism Cyber–physical power system Denial-of-service attacks Resilient H∞ load frequency control scheme Transmission interval-dependent loop functional approach

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Erratum to "Multi-View Face Synthesis via Progressive Face Flow"

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IEEE transactions on image processing : a publication of the IEEE Signal Processing Society 2021年 30卷 6700页

作者： Yangyang Xu Xuemiao Xu Jianbo Jiao Keke Li Cheng Xu Shengfeng He School of Computer Science and Engineering South China University of Technology Guangzhou China State Key Laboratory of Subtropical Building Science the Key Laboratory of Big Data and Intelligent Robot Ministry of Education and the Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information South China University of Technology Guangzhou China University of Oxford Oxford U.K.

In the above article [1], unfortunately, Fig. 5 was not displayed correctly with many empty images. The correct version is supplemented here.

关键词： Faces

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Hypercomplex Low Rank Reconstruction for Nmr Spectroscopy with Cloud computing ⋆

SSRN

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SSRN 2022年

作者： Guo, Yi Zhan, Jiaying Tu, Zhangren Zhou, Yirong Wu, Jianfan Hong, Qing Orekhov, Vladislav Qu, Xiaobo Guo, Di School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361005 China China Mobile Group Xiamen361005 China Swedish NMR Center Department of Chemistry and Molecular Biology University of Gothenburg Gothenburg41390 Sweden

Nuclear magnetic resonance (NMR) spectroscopy serves as an important tool to analyze chemicals and proteins in bioengineering. Multi-dimensional NMR offers a major improvement in resolution with multi-dimensional spectrum, but significantly increases data acquisition time and produces hypercomplex data that is difficult to be handled. To reduce this time, non-uniformly sampling can be applied to obtain undersampled data and using a reconstruction approach, such as the state-of-the-art low-rank method, to remove the spectral artifacts introduced by undersampling. However, only complex format of signal, including the real and imaginary parts, is considered in previous low-rank approach, which is less efficient when dealing with hypercomplex data that has multiple components. To deal with this problem, a hypercomplex low-rank model is proposed by introducing an adjoint matrix operation and then solved with a fast matrix factorization algorithm. This method efficiently uses redundant information among all the components of hypercomplex signal. Using simulated data and real protein data, we demonstrate that the proposed method provides a fast and high-fidelity reconstruction of hypercomplex NMR spectroscopy. Furthermore, an open-access and easy-to-use cloud-computing platform is developed for this method. © 2022, The Authors. All rights reserved.

关键词： Nuclear magnetic resonance spectroscopy

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An Improved Adam Optimization Algorithm Combining Adaptive Coefficients and Composite Gradients Based on Randomized Block Coordinate Descent

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Computational intelligence and neuroscience 2023年第1期2023卷 4765891页

作者： Miaomiao Liu Dan Yao Zhigang Liu Jingfeng Guo Jing Chen School of Computer and Information Technology Northeast Petroleum University Daqing 163318 China. Heilongjiang Key Laboratory of Petroleum Big Data and Intelligent Analysis Daqing 163318 China. College of Information Science and Engineering Yanshan University Qinhuangdao 066004 China.

An improved Adam optimization algorithm combining adaptive coefficients and composite gradients based on randomized block coordinate descent is proposed to address issues of the Adam algorithm such as slow convergence, the tendency to miss the global optimal solution, and the ineffectiveness of processing high-dimensional vectors. The adaptive coefficient is used to adjust the gradient deviation value and correct the search direction firstly. Then, the predicted gradient is introduced, and the current gradient and the first-order momentum are combined to form a composite gradient to improve the global optimization ability. Finally, the random block coordinate method is used to determine the gradient update mode, which reduces the computational overhead. Simulation experiments on two standard datasets for classification show that the convergence speed and accuracy of the proposed algorithm are higher than those of the six gradient descent methods, and the CPU and memory utilization are significantly reduced. In addition, based on logging data, the BP neural networks optimized by six algorithms, respectively, are used to predict reservoir porosity. Results show that the proposed method has lower system overhead, higher accuracy, and stronger stability, and the absolute error of more than 86% data is within 0.1%, which further verifies its effectiveness.

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Corrigendum to “Manifold Adaptive Kernelized Low-Rank Representation for Semisupervised Image Classification”

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Complexity 2018年第1期2018卷

作者： Yong Peng Wanzeng Kong Feiwei Qin Feiping Nie School of Computer Science Hangzhou Dianzi University Hangzhou 310018 *** Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing 210023 *** Center for OPTical IMagery Analysis and Learning (OPTIMAL) Northwestern Polytechnical University Xi’an 710072 ***

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