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Modeling viral evolution:A novel SIRSVIDE framework with application to SARS-CoV-2 dynamics

hLife 2024年第5期2卷 227-245页

作者： Kaichun Jin Xiaolu Tang Zhaohui Qian Zhiqiang Wu Zifeng Yang Tao Qian Chitin Hon Jian Lu State Key Laboratory of Protein and Plant Gene Research Center for BioinformaticsSchool of Life SciencesPeking UniversityBeijingChina NHC Key Laboratory of Systems Biology of Pathogens Institute of Pathogen BiologyChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina State Key Laboratory of Respiratory Disease National Clinical Research Center for Respiratory DiseaseGuangzhou Institute of Respiratory HealthThe First Affiliated Hospital of Guangzhou Medical UniversityGuangdongChina Respiratory Disease AI Laboratory on Epidemic and Medical Big Data Instrument Applications Department of Engineering ScienceFaculty of Innovation EngineeringMacao University of Science and TechnologyMacaoChina Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases KingMed School of Laboratory MedicineGuangzhou Medical UniversityGuangdongChina Macao Center for Mathematical Sciences Macao University of Science and TechnologyMacaoChina Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacaoChina

Understanding evolutionary trends in emerging viruses,such as severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),is crucial for effective public health management and ***,extensive debates have arisen concerning viral evolutionary trends,particularly the interplay between transmissibility,pathogenicity,and immune *** this context,we have developed a novel computational model named SIRSVIDE(Susceptible-Infected-Recovered-Susceptible-Variation-Immune Decay-Immune Escape)to simulate the transmission and evolutionary dynamics of viral *** simulation results indicate that under conditions of high mutation rates,elevated transmission rates,and larger susceptible host populations,viral populations exhibit prolonged increases in transmissibility and immune escape,accompanied by reductions in pathogenicity and noticeable short-term fluctua***,when the total susceptible population size and mutation rate decrease,substantial uncertainty in the evolutionary trends of viral populations becomes *** summary,the SIRSVIDE model establishes a comprehensive framework for generating both short-and long-term viral epidemiological and evolutionary *** simulation outcomes align with existing evidence indicating that SARS-CoV-2 is undergoing selection for heightened transmissibility,decreased pathogenicity,and enhanced immune ***,the model sheds light on the possible evolutionary dynamics of other viruses.

关键词： viral evolution severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) transmissibility-pathogenicity trade-off immune escape

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WMMSE Beamforming for User-Centric Cell-Free Networks with Non-Coherent Joint Transmission

WMMSE Beamforming for User-Centric Cell-Free Networks with N...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Xi Wang Xiaotong Zhao Juncheng Wang Qingjiang Shi School of Software Engineering Tongji University Shanghai China Department of Computer Science Hong Kong Baptist University Hong Kong China Shenzhen Research Institute of Big Data Shenzhen China

We consider downlink beamforming design to maximize the weighted sum rate (WSR) in a user-centric cell-free network, where distributed access points (APs) are organized in a cluster to jointly serve each user equipment (UE). This architecture ensures uniform service quality even at the cell edge, but synchronization between APs can be problematic, necessitating the utilization of non-coherent joint transmission (NCJT) that eliminates the need for strict synchronization. Most existing works on beamforming design for NCJT assume that the classic weighted minimum mean square error (WMMSE) approach is not applicable, and design their beamforming algorithms based on the successive convex approximation (SCA) method with high computational complexity. In this work, we for the first time demonstrate the applicability of the WMMSE approach for NCJT in cell-free networks. Based on the unique observations on the structures of the WSR maximization problem for NCJT, we propose an efficient WMMSE based beamforming algorithm. Our proposed algorithm is guaranteed to converge to a stationary point of the WSR maximization problem. Furthermore, our beamforming updates are in closed form with low computational complexity. Simulation results demonstrate substantial performance gain of our proposed algorithm over the current best SCA based alternatives in both computational complexity and convergence time.

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Energy-Efficient SLAM via Joint Design of Sensing, Communication, and Exploration Speed

arXiv

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arXiv 2024年

作者： Han, Zidong Jin, Ruibo Li, Xiaoyang Zhou, Bingpeng Zhang, Qinyu Gong, Yi Southern University of Science and Technology Shenzhen China Shenzhen China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China The School of Electronics and Communication Engineering Sun Yat-sen University Shenzhen China

To support future spatial machine intelligence applications, lifelong simultaneous localization and mapping (SLAM) has drawn significant attentions. SLAM is usually realized based on various types of mobile robots performing simultaneous and continuous sensing and communication. This paper focuses on analyzing the energy efficiency of robot operation for lifelong SLAM by jointly considering sensing, communication and mechanical factors. The system model is built based on a robot equipped with a 2D light detection and ranging (LiDAR) and an odometry. The cloud point raw data as well as the odometry data are wirelessly transmitted to data center where real-time map reconstruction is realized based on an unsupervised deep learning based method. The sensing duration, transmit power, transmit duration and exploration speed are jointly optimized to minimize the energy consumption. Simulations and experiments demonstrate the performance of our proposed method. © 2024, CC BY.

关键词： SLAM robotics

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Transparent Four-Port MIMO DRAs Decoupled by Conformal Strips

Transparent Four-Port MIMO DRAs Decoupled by Conformal Strip...

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2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024

作者： He, Chao Yang, Nan Leung, Kwok Wa Liaoning Technical University Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Huludao125105 China Sun Yat-sen University School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Guangzhou China City University of Hong Kong State Key Laboratory of Terahertz and Millimeter Waves Department of Electrical Engineering Kowloon Hong Kong CityU Shenzhen Research Institute Information and Communication Technology Centre Shenzhen China

ISBN: (纸本)9798350355581

A four-port multi-input multi-output (MIMO) dielectric resonator (DR) antenna (DRA) is presented in this paper. Combing four rectangular DRs together can form a single DR element. With shape modification and conformal strips, the four ports can be decoupled. To verify the design idea, a transparent prototype can be designed and fabricated for 2.4-GHz band applications. The measured results show an impedance bandwidth of 5.8% and an isolation level of 24 dB. © 2024 IEEE.

关键词： Microwave antennas

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Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference

arXiv

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arXiv 2023年

作者： Lin, Zhidi Sun, Yiyong Yin, Feng Thiéry, Alexandre Hoang The School of Science & Engineering The Future Network of Intelligence Institute The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen518172 China The School of Science & Engineering The Chinese University of Hong Kong Shenzhen Shenzhen518172 China The Department of Statistics & Data Science National University of Singapore Singapore117546 Singapore

The Gaussian process state-space models (GPSSMs) represent a versatile class of data-driven nonlinear dynamical system models. However, the presence of numerous latent variables in GPSSM incurs unresolved issues for existing variational inference approaches, particularly under the more realistic non-mean-field (NMF) assumption, including extensive training effort, compromised inference accuracy, and infeasibility for online applications, among others. In this paper, we tackle these challenges by incorporating the ensemble Kalman filter (EnKF), a well-established model-based filtering technique, into the NMF variational inference framework to approximate the posterior distribution of the latent states. This novel marriage between EnKF and GPSSM not only eliminates the need for extensive parameterization in learning variational distributions, but also enables an interpretable, closed-form approximation of the evidence lower bound (ELBO). Moreover, owing to the streamlined parameterization via the EnKF, the new GPSSM model can be easily accommodated in online learning applications. We demonstrate that the resulting EnKF-aided online algorithm embodies a principled objective function by ensuring data-fitting accuracy while incorporating model regularizations to mitigate overfitting. We also provide detailed analysis and fresh insights for the proposed algorithms. Comprehensive evaluation across diverse real and synthetic datasets corroborates the superior learning and inference performance of our EnKF-aided variational inference algorithms compared to existing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Parameterization

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Joint Passive Beamforming and Deployment Design for Multi-intelligent Refracting Surface Aided High-Speed Railway Communication

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IEEE Wireless Communications Letters 2025年

作者： Wang, Lei Zhu, Lipeng Sun, He Niu, Yong Ai, Bo Zhang, Rui Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China 117583 Singapore School of Science and Engineering Shenzhen Research Institute of Big Data Chinese University of Hong Kong Guangdong Shenzhen518172 China National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore

This letter investigates a multi-intelligent refracting surface (IRS) aided high-speed railway communication system that can provide cooperative passive beamforming gains without incurring additional channel estimation overhead. A novel multi-IRS passive beamforming framework is designed to simultaneously track the time-varying channels between the remote radio head and a number of IRSs mounted on the top of the train. These IRSs cooperatively enhance the signal coverage within the train carriage. Specifically, the multi-IRS passive beamforming and deployment are jointly optimized to maximize the minimum average channel power across all discretized grids within the carriage, thereby ensuring fair coverage throughout the train. An alternating optimization algorithm, utilizing the semidefinite relaxation and one-dimensional line search techniques, is employed to solve this non-convex optimization problem efficiently. Numerical results reveal that the proposed distributed IRS deployment scheme outperforms its centralized deployment counterpart with the same total number of IRS elements. Moreover, the proposed multi-IRS passive beamforming and deployment joint design can significantly improve the signal coverage performance in the train carriage. © 2012 IEEE.

关键词： Signaling

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UniCell:Universal Cell Nucleus Classification via Prompt Learning

arXiv

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arXiv 2024年

作者： Huang, Junjia Li, Haofeng Wan, Xiang Li, Guanbin School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China GuangDong Province Key Laboratory of Information Security Technology China

The recognition of multi-class cell nuclei can significantly facilitate the process of histopathological diagnosis. Numerous pathological datasets are currently available, but their annotations are inconsistent. Most existing methods require individual training on each dataset to deduce the relevant labels and lack the use of common knowledge across datasets, consequently restricting the quality of recognition. In this paper, we propose a universal cell nucleus classification framework (UniCell), which employs a novel prompt learning mechanism to uniformly predict the corresponding categories of pathological images from different dataset domains. In particular, our framework adopts an end-to-end architecture for nuclei detection and classification, and utilizes flexible prediction heads for adapting various datasets. Moreover, we develop a Dynamic Prompt Module (DPM) that exploits the properties of multiple datasets to enhance features. The DPM first integrates the embeddings of datasets and semantic categories, and then employs the integrated prompts to refine image representations, efficiently harvesting the shared knowledge among the related cell types and data sources. Experimental results demonstrate that the proposed method effectively achieves the state-of-the-art results on four nucleus detection and classification benchmarks. Code and models are available at https://***/lhaof/UniCell Copyright © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

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Multi-Scale Clinical-Guided Binocular Fusion Framework for Predicting New-Onset Hypertension Over a Four-Year Period

Multi-Scale Clinical-Guided Binocular Fusion Framework for P...

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IEEE International Symposium on Biomedical Imaging

作者： Haoshen Li Zifan Chen Jie Zhao Heyun Chen Hexin Dong Mingze Yuan Bin Dong Li Zhang Center for Data Science Peking University China National Engineering Laboratory for Big Data Analysis and Applications Peking University China Peking University Changsha Institute for Computing and Digital Economy China Beijing International Center for Mathematical Research Peking University China Center for Machine Learning Research Peking University China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Hypertension is a major global health concern, linked to various cardiovascular diseases and associated with distinct ocular manifestations. While recent advances in artificial intelligence have enabled accurate diagnosis of current hypertension through fundus images, predicting the future onset of hypertension remains an uncharted domain. In this study, we introduce the multi-scale clinical-guided binocular fusion framework (MCBO), designed to predict the likelihood of developing hypertension within the next four years. MCBO uniquely integrates left and right fundus images and clinical data, utilizing a shared-weight multi-stage Transformer-based encoder. Our multi-scale clinical-guided module (MCM) ensures image feature extraction is clinically contextualized based on clinical information, and our binocular fusion module (BFM) fuses binocular information. Comparative performance against seven baseline models establishes MCBO’s supremacy, with improvements of 6.7% in Area Under Curve (AUC), 6.9% in Accuracy (ACC), 5.1% in Sensitivity (SEN) and 5.5% in Specificity (SPE). This approach offers a promising avenue for proactive hypertension management, underscoring the potential of integrating Deep Learning with clinical data for enhanced healthcare outcomes. Our code is available at https://***/HaoshenLi/MCBO.

关键词： Hypertension Deep learning Accuracy Sensitivity Codes Fuses Feature extraction

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A deformation-based framework for learning solution mappings of PDEs defined on varying domains

arXiv

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arXiv 2024年

作者： Xiao, Shanshan Jin, Pengzhan Tang, Yifa LSEC ICMSEC Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing100871 China Chongqing Research Institute of Big Data Peking University Chongqing401329 China

In this work, we establish a deformation-based framework for learning solution mappings of PDEs defined on varying domains. The union of functions defined on varying domains can be identified as a metric space according to the deformation, then the solution mapping is regarded as a continuous metric-to-metric mapping, and subsequently can be represented by another continuous metric-to-Banach mapping using two different strategies, referred to as the D2D framework and the D2E framework, respectively. We point out that such a metric-to-Banach mapping can be learned by neural networks, hence the solution mapping is accordingly learned. With this framework, a rigorous convergence analysis is built for the problem of learning solution mappings of PDEs on varying domains. As the theoretical framework holds based on several pivotal assumptions which need to be verified for a given specific problem, we study the star domains as a typical example, and other situations could be similarly verified. There are three important features of this framework: (1) The domains under consideration are not required to be diffeomorphic, therefore a wide range of regions can be covered by one model provided they are homeomorphic. (2) The deformation mapping is unnecessary to be continuous, thus it can be flexibly established via combining a primary identity mapping and a local deformation mapping. This capability facilitates the resolution of large systems where only local parts of the geometry undergo change. (3) If a linearity-preserving neural operator such as MIONet is adopted, this framework still preserves the linearity of the surrogate solution mapping on its source term for linear PDEs, thus it can be applied to the hybrid iterative method. We finally present several numerical experiments to validate our theoretical results. Copyright © 2024, The Authors. All rights reserved.

关键词： Iterative methods

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Digital earth:yesterday,today,and tomorrow

引用

International Journal of Digital Earth 2023年第1期16卷 1022-1072页

作者： Alessandro Annoni Stefano Nativi ArzuÇöltekin Cheryl Desha Eugene Eremchenko Caroline M.Gevaert Gregory Giuliani Min Chen Luis Perez-Mora Joseph Strobl Stephanie Tumampos International Society for Digital Earth BeijingPeople’s Republic of China National Research Council RomeItaly University of Applied Sciences and Arts Northwestern Switzerland FHNW School of EngineeringInstitute of Interactive Technologies IITBrugg-WindischSwitzerland Nathan Campus Griffith UniversityCities Research InstituteBrisbaneAustralia Lomonosov Moscow State University MoscowRussia Department of Earth Observation Science University of TwenteTwenteThe Netherland Institute for Environmental Sciences/enviroSPACE University of GenevaGenevaSwitzerland Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjingPeople’s Republic of China Cities Research Institute Nathan CampusGriffith UniversityBrisbaneAustralia Department of Geoinformatics University of SalzburgSalzburgAustria Chair of Remote Sensing Technology Technical University of MunichMunichGermany Big Geospatial Data Management Technical University of MunichMunichGermany

The concept of Digital Earth(DE)was formalized by Al Gore in *** that time the technologies needed for its implementation were in an embryonic stage and the concept was quite *** then digital technologies have progressed significantly and their speed and pervasiveness have generated and are still causing the digital transformation of our *** creates new opportunities and challenges for the realization of DE.‘What is DE today?’,‘What could DE be in the future?’,and‘What is needed to make DE a reality?’.To answer these questions it is necessary to examine DE considering all the technological,scientific,social,and economic aspects,but also bearing in mind the principles that inspired its *** understanding the lessons learned from the past,it becomes possible to identify the remaining scientific and technological challenges,and the actions needed to achieve the ultimate goal of a‘Digital Earth for all’.This article reviews the evolution of the DE vision and its multiple definitions,illustrates what has been achieved so far,explains the impact of digital transformation,illustrates the new vision,and concludes with possible future scenarios and recommended actions to facilitate full DE implementation.

关键词： Digital Earth digital transformation sustainable development data governance and ethic citizen empowerment and industry engagement

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