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arXiv 2024年

作者： Cui, Qimei You, Xiaohu Ni, Wei Nan, Guoshun Zhang, Xuefei Zhang, Jianhua Lyu, Xinchen Ai, Ming Tao, Xiaofeng Feng, Zhiyong Zhang, Ping Wu, Qingqing Tao, Meixia Huang, Yongming Huang, Chongwen Liu, Guangyi Peng, Chenghui Pan, Zhiwen Sun, Tao Niyato, Dusit Chen, Tao Khan, Muhammad Khurram Jamalipour, Abbas Guizani, Mohsen Yuen, Chau National Engineering Research Center of Mobile Network Technologies Beijing University of Posts and Telecommunications Beijing100876 China Department of Broadband Communication Peng Cheng Laboratory Shenzhen518055 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Fudan University Shanghai200433 China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China CICT Mobile Communication Technology Co. Ltd. Beijing100020 China Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China Shanghai Jiao Tong University Shanghai200240 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310058 China China Mobile Research Institute Beijing100053 China Huawei Technologies Shanghai201206 China College of Computing and Data Science Nanyang Technological University Singapore117583 Singapore VTT Technical Research Centre of Finland Ltd. EspooFI-02044 VTT Finland Center of Excellence in Information Assurance King Saud University Riyadh11362 Saudi Arabia School of Electrical and Information Engineering University of Sydney NSW2006 Australia Abu Dhabi United Arab Emirates School of Electrical and Electronics Engineering Nanyang Technological University Singapore639798 Singapore

With the growing demand for seamless connectivity and intelligent communication, the integration of artificial intelligence (AI) and sixth-generation (6G) communication networks has emerged as a transformative paradigm. By embedding AI capabilities across various network layers, this integration enables optimized resource allocation, improved efficiency, and enhanced system robust performance, particularly in intricate and dynamic environments. This paper presents a comprehensive overview of AI and communication for 6G networks, with a focus on emphasizing their foundational principles, inherent challenges, and future research opportunities. We first review the integration of AI and communications in the context of 6G, exploring the driving factors behind incorporating AI into wireless communications, as well as the vision for the convergence of AI and 6G. The discourse then transitions to a detailed exposition of the envisioned integration of AI within 6G networks, delineated across three progressive developmental stages. The first stage, AI for network, focuses on employing AI to augment network performance, optimize efficiency, and enhance user service experiences. The second stage, network for AI, highlights the role of the network in facilitating and buttressing AI operations and presents key enabling technologies, such as digital twins for AI and semantic communication. The final stage, AI as a Service, it is anticipated that future 6G networks will innately provide AI functions as services, supporting application scenarios like immersive communication and intelligent industrial robots. Specifically, we have defined the quality of AI service, which refers to a framework for measuring AI services within the network. We further summarize the standardization process of AI for wireless networks, highlighting key milestones and ongoing efforts. In addition, we conduct an in-depth analysis of the critical challenges faced by the integration of AI and communications

关键词： Resource allocation

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Average Channel Capacity Analysis for Visible Light Communications with Random Receivers

Average Channel Capacity Analysis for Visible Light Communic...

引用

International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Xian-Tao Fu Rong-Rong Lu Sheng-Hong Lin Jin-Yuan Wang Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Key Laboratory of Opto-technology and Intelligent Control Ministry of Education Lanzhou Jiaotong University Lanzhou China National Local Joint Engineering Research Center for Communication and Network Technologies Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9781728172361

ISBN: (纸本)9781728172378

For indoor visible light communications (VLC), the receivers are usually set to be fixed ones in previous studies. However, the locations and orientations of receivers are affected by random factors in practical VLC systems. In this paper, the performance of VLC with random receivers is investigated. We consider an VLC system, which consists of a transmitter and a receiver. The main noise is additive white Gaussian noise. Two random factors (i.e., random location and random orientation) of the receiver are considered. Based on the established system model, closed-form expressions of the average channel capacity for systems with random location and random orientation are derived, respectively. Numerical results verify the accuracy of the derived theoretical expressions. Moreover, the impacts of the nominal optical intensity, the dimming target, the transmitter height and the Lambertian emission order on system performance are also discussed.

关键词： Visible light communication Optical transmitters Optical receivers Light emitting diodes Channel capacity High-speed optical techniques Wireless communication

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Novel Scheme Towards Interfacial Charge Transfer between Znin2s4 and Biobr for Efficient Photocatalytic Removal of Organics and Chromium (Vi) from Water

SSRN

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SSRN 2022年

作者： He, Zuming Yang, Hanpei Sunarso, Jaka Wong, Ngie Hing Huang, Zhengyi Xia, Yongmei Wang, Yong Su, Jiangbin Wang, Lina Kang, Li Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes Ministry of Education College of Environment Hohai University Nanjing210098 China Huaide School Changzhou University Jingjiang214500 China School of Microelectronics and Control Engineering Changzhou University Changzhou213164 China Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Jalan Simpang Tiga Sarawak Kuching93350 Malaysia School of Materials and Engineering Jiangsu University of Technology Changzhou213001 China School of Pharmaceutical and Materials Engineering Taizhou University Jiaojiang318000 China

Construction of Z-scheme heterostructure is an effective strategy to enhance the charge carriers' separation. However, successfully achieving this on the defect heterojunction to improve the photocatalytic activity remains challenging. This work successfully obtained sulfur vacancy in the ZnIn2S4/BiOBr (SZIS/BOB) heterojunction composites with S-O covalent bonding using a hydrothermal method. As a result, they exhibited superior photocatalytic and stability performance. The optimized SZIS/BOB-10 exhibited excellent rhodamine B degradation (95.2%) and chromium (VI) reduction (97.8%) within 100 minutes under visible light. The enhanced composites with S-vacancies, S-O bond, and internal electric field induced the Z-scheme charge transfer mechanism. We had verified this mechanism based on the surface photovoltage spectra, electron spin response spectra, and density functional theory calculations. This work not only provides valuable insights into designing photocatalysts with a direct Z scheme heterostructure but also delineates a promising strategy for developing efficient photocatalysts to degrade organic pollutants. © 2022, The Authors. All rights reserved.

关键词： Density functional theory

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Applications of nanogenerators for biomedical engineering and healthcare systems

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InfoMat 2022年第2期4卷 53-109页

作者： Wanli Wang Jinbo Pang Jie Su Fujiang Li Qiang Li Xiaoxiong Wang Jingang Wang Bergoi Ibarlucea Xiaoyan Liu Yufen Li Weijia Zhou Kai Wang Qingfang Han Lei Liu Ruohan Zang Mark HRümmeli Yang Li Hong Liu Han Hu Gianaurelio Cuniberti College of Electrical Engineering Qingdao UniversityQingdaoChina Collaborative Innovation Center of Technology and Equipment for Biological Diagnosis and Therapy in Universities of Shandong Institute for Advanced Interdisciplinary Research(iAIR)University of JinanJinanChina College of Electronic Information Qingdao UniversityQingdaoChina Department of Pediatric Surgery The Affiliated Hospital of Qingdao UniversityQingdaoChina College of Physics Qingdao UniversityQingdaoChina Institute for Materials Science and Max Bergmann Center of Biomaterials Technische Universität DresdenDresdenGermany Center for Advancing Electronics Dresden Technische Universität DresdenDresdenGermany College of Biological Science and Technology University of JinanJinanChina School of Chemistry and Chemical Engineering University of JinanShandongJinanChina College of Energy Soochow Institute for Energy and Materials InnovationsSoochow UniversitySuzhouChina Key Laboratory of Advanced Carbon Materials and Wearable Energy Technologies of Jiangsu Province Soochow UniversitySuzhouChina Centre of Polymer and Carbon Materials Polish Academy of SciencesZabrzePoland Institute for Complex Materials IFW DresdenDresdenGermany Institute of Environmental Technology VŠB-Technical University of OstravaOstravaCzech Republic School of Information Science and Engineering University of JinanJinanChina Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of JinanJinanChina State Key Laboratory of Crystal Materials Center of Bio&Micro/Nano Functional MaterialsShandong UniversityJinanChina College of Chemical Engineering China University of Petroleum(East China)QingdaoChina Dresden Center for Computational Materials Science Technische Universität DresdenDresdenGermany The Dresden Center for Intelligent Materials(DCIM) Technische Universität DresdenDresdenGermany

The dream of human beings for long living has stimulated the rapid development of biomedical and healthcare ***,conventional biomedical and healthcare devices have shortcomings such as short service life,large equipment size,and high potential safety ***,the power supply for conventional implantable device remains predominantly *** emerging nanogenerators,which harvest micro/nanomechanical energy and thermal energy from human beings and convert into electrical energy,provide an ideal solution for self-powering of biomedical *** combination of nanogenerators and biomedicine has been accelerating the development of self-powered biomedical *** article first introduces the operating principle of nanogenerators and then reviews the progress of nanogenerators in biomedical applications,including power supply,smart sensing,and effective ***,the microbial disinfection and biodegradation performances of nanogenerators have been ***,the protection devices have been discussed such as face mask with air filtering function together with real-time monitoring of human health from the respiration and heat ***,the nanogenerator devices have been categorized by the types of mechanical energy from human beings,such as the body movement,tissue and organ activities,energy from chemical reactions,and gravitational potential ***,the challenges and future opportunities in the applications of nanogenerators are delivered in the conclusive remarks.

关键词： biomedical engineering healthcare implantable devices nanogenerators self-powered devices sensors

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SLV: Spatial Likelihood Voting for Weakly Supervised Object Detection

SLV: Spatial Likelihood Voting for Weakly Supervised Object ...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Ze Chen Zhihang Fu Rongxin Jiang Yaowu Chen Xian-Sheng Hua Zhejiang University Institute of Advanced Digital Technology and Instrument Zhejiang University Embedded System Engineering Research Center Ministry of Education of China Alibaba DAMO Academy Alibaba Group Zhejiang University the State Key Laboratory of Industrial Control Technology Zhejiang Provincial Key Laboratory for Network Multimedia Technologies

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Based on the framework of multiple instance learning (MIL), tremendous works have promoted the advances of weakly supervised object detection (WSOD). However, most MIL-based methods tend to localize instances to their discriminative parts instead of the whole content. In this paper, we propose a spatial likelihood voting (SLV) module to converge the proposal localizing process without any bounding box annotations. Specifically, all region proposals in a given image play the role of voters every iteration during training, voting for the likelihood of each category in spatial dimensions. After dilating alignment on the area with large likelihood values, the voting results are regularized as bounding boxes, being used for the final classification and localization. Based on SLV, we further propose an end-to-end training framework for multi-task learning. The classification and localization tasks promote each other, which further improves the detection performance. Extensive experiments on the PASCAL VOC 2007 and 2012 datasets demonstrate the superior performance of SLV.

关键词： Proposals Training Detectors Object detection Task analysis Feature extraction Computer vision

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SLV: Spatial Likelihood Voting for Weakly Supervised Object Detection

arXiv

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arXiv 2020年

作者： Chen, Ze Fu, Zhihang Jiang, Rongxin Chen, Yaowu Hua, Xian-sheng Zhejiang University Institute of Advanced Digital Technology and Instrument Zhejiang University Embedded System Engineering Research Center Ministry of Education of China Zhejiang University State Key Laboratory of Industrial Control Technology Zhejiang Provincial Key Laboratory for Network Multimedia Technologies Alibaba DAMO Academy Alibaba Group

关键词： Object recognition

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Construction of Cu7s4@Cuco2o4 Yolk-Shell Microspheres Composite and Elucidation of its Enhanced Photocatalytic Activity, Mechanism, and Pathway for Carbamazepine Degradation

SSRN

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SSRN 2022年

作者： He, Zuming Yang, Hanpei Wong, Ngie Hing Ernawati, Lusi Sunarso, Jaka Huang, Zhengyi Xia, Yongmei Wang, Yong Su, Jiangbin Fu, Xiaofei Wu, Mi Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes Ministry of Education College of Environment Hohai University Nanjing210098 China Huaide School Changzhou University Jingjiang214500 China School of Microelectronics and Control Engineering Changzhou University Changzhou213164 China Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Jalan Simpang Tiga Sarawak Kuching93350 Malaysia Department of Chemical Engineering Institut Teknologi Kalimantan Balikpapan76127 Indonesia School of Materials and Engineering Jiangsu University of Technology Changzhou213001 China School of Pharmaceutical and Materials Engineering Taizhou University Jiaojiang318000 China

Water pollution caused by the massive use of medicines has caused major environmental problems. This work first reported the synthesis and characterization of the Cu7S4/CuCo2O4 (CS/CCO) yolk-shell microspheres via hydrothermal and annealing methods, then investigated their photocatalytic performance in removing organic water pollutants. The 10-CS/CCO composite with yolk-shell microspheres exhibited the highest photodegradation rate of carbamazepine (CBZ), reaching 96.3% within 2 h. The 10-CS/CCO also demonstrated more than 2 times higher photodegradation rates than the pure (Cu7S4) CS and (CuCo2O4) CCO. This outstanding photocatalytic performance can be attributed to the unique yolk-shell structure and the Z-scheme charge transfer pathway, reducing multiple reflections of the acting light. These factors enhanced the light absorption efficiency and efficiently transferred photoexcited charge carriers. In-depth photocatalytic degradation pathways of CBZ were systematically evaluated via the identification of degradation intermediates with Fukui index calculation. © 2022, The Authors. All rights reserved.

关键词： Microspheres

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Randomised benchmarking for characterizing and forecasting correlated processes

arXiv

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arXiv 2023年

作者： Zhang, Xinfang Wu, Zhihao White, Gregory A.L. Xiang, Zhongcheng Hu, Shun Peng, Zhihui Liu, Yong Zheng, Dongning Fu, Xiang Huang, Anqi Poletti, Dario Modi, Kavan Wu, Junjie Deng, Mingtang Guo, Chu Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China School of Physics and Astronomy Monash University VIC3800 Australia Dahlem Center for Complex Quantum Systems Freie Universität Berlin Berlin14195 Germany Institute of Physics Chinese Academy of Sciences Beijing100190 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China Science Mathematics and Technology Cluster and Engineering Product Development Pillar Singapore University of Technology and Design 8 Somapah Road 487372 Singapore Centre for Quantum Technologies National University of Singapore 117543 Singapore MajuLab CNRS-UNS-NUS-NTU International Joint Research Unit UMI 3654 Singapore Quantum for NSW Sydney2000 Australia

The development of fault-tolerant quantum processors relies on the ability to control noise. A particularly insidious form of noise is temporally correlated or non-Markovian noise. By combining randomized benchmarking with supervised machine learning algorithms, we develop a method to learn the details of temporally correlated noise. In particular, we can learn the time-independent evolution operator of system plus bath and this leads to (i) the ability to characterize the degree of non-Markovianity of the dynamics and (ii) the ability to predict the dynamics of the system even beyond the times we have used to train our model. We exemplify this by implementing our method on a superconducting quantum processor. Our experimental results show a drastic change between the Markovian and non-Markovian regimes for the learning accuracies. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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DPNet: Dynamic Pooling network for Accurate and Efficient Size-Aware Tiny Object Detection

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IEEE Internet of Things Journal 2025年

作者： Gong, Luqi Chen, Haotian Chen, Yikun Yao, Tianliang Li, Chao Zhao, Shuai Han, Guangjie Beijing University of Posts and Telecommunications School of Computer Science Beijing China Zhejiang Lab Research Center for Space Computing System Hangzhou China Southwest Jiaotong University SWJTU-LEEDS Joint School Chengdu China LTD Guangdong Zhiyun City construction Technology Co Zhuhai China Tongji University College of Electronic and Information Engineering Department of Control Science and Engineering Shanghai China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

In unmanned aerial systems, especially in complex environments, accurately detecting tiny objects is crucial. Resizing images is a common strategy to improve detection accuracy, particularly for small objects. However, simply enlarging images significantly increases computational costs and the number of negative samples, severely degrading detection performance and limiting its applicability. This paper proposes a Dynamic Pooling network (DPNet) for tiny object detection to mitigate these issues. DPNet employs a flexible down-sampling strategy by introducing a factor (df) to relax the fixed down-sampling process of the feature map to an adjustable one. Furthermore, we design a lightweight predictor to predict df for each input image, which will be used to decrease the resolution of feature map in backbone. Thus, we achieve input-aware down-sampling. We design an Adaptive Normalization Module (ANM) to make a unified detector well compatible with different dfs. At the same time, we also design a guidance loss to supervise the predictor’s training. DPNet realizes the dynamic allocation of computing resources to trade off detection accuracy and efficiency through this. Experiments on the TinyCOCO and TinyPerson datasets show that our DPNet can save over 35% and 25% GFLOPs, respectively, while maintaining comparable detection *** code will be made publicly available. © 2014 IEEE.

关键词： Object detection

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Redefining pandemic preparedness:Multidisciplinary insights from the CERP modelling workshop in infectious diseases,workshop report

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Infectious Disease Modelling 2024年第2期9卷 501-518页

作者： Marta C.Nunes Edward Thommes Holger Fröhlich Antoine Flahault Julien Arino Marc Baguelin Matthew Biggerstaff Gaston Bizel-Bizellot Rebecca Borchering Giacomo Cacciapaglia Simon Cauchemez Alex Barbier-Chebbah Carsten Claussen Christine Choirat Monica Cojocaru Catherine Commaille-Chapus Chitin Hon Jude Kong Nicolas Lambert Katharina B.Lauer Thorsten Lehr Cédric Mahe Vincent Marechal Adel Mebarki Seyed Moghadas Rene Niehus Lulla Opatowski Francesco Parino Gery Pruvost Andreas Schuppert Rodolphe Thiébaut Andrea Thomas-Bachli Cecile Viboud Jianhong Wu Pascal Crépey Laurent Coudeville Center of Excellence in Respiratory Pathogens(CERP) Hospices Civils de Lyon(HCL)and Centre International de Recherche en Infectiologie(CIRI)Équipe SantéPubliqueÉpidémiologie etÉcologieÉvolutive des Maladies Infectieuses(PHE3ID)Inserm U1111CNRS UMR5308ENS de LyonUniversitéClaude Bernard Lyon 1LyonFrance South African Medical Research Council Vaccines&Infectious Diseases Analytics Research UnitFaculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa New Products and Innovation(NPI) Sanofi Vaccines(Global)TorontoOntarioCanada Department of Mathematics and Statistics University of GuelphGuelphOntarioCanada Fraunhofer Institute for Algorithms and Scientific Computing(SCAI) Department of BioinformaticsSchloss BirlinghovenSankt AugustinGermany University of Bonn Bonn-Aachen International Center for IT(b-it)BonnGermany Institute of Global Health Faculty of MedicineUniversity of GenevaGenevaSwitzerland and Swiss School of Public HealthZürichSwitzerland Department of Mathematics University of ManitobaWinnipegManitobaCanada MRC Centre for Global Infectious Disease Analysis School of Public HealthImperial College LondonLondonUK Centre for Mathematical Modelling of Infectious Diseases Department of Infectious Disease EpidemiologyLondon School of Hygiene&Tropical MedicineLondonUK National Center for Immunization and Respiratory Diseases(NCIRD) US Centers for Disease Control and Prevention(CDC)AtlantaGAUSA Departement of Computational Biology Departement of Global HealthInstitut PasteurParisFrance Institut de Physique des Deux Infinis de Lyon(IP2I) UMR5822IN2P3/CNRSUniversitéClaude Bernard Lyon 1VilleurbanneFrance Mathematical Modelling of Infectious Diseases Unit Institut PasteurUniversitéParis CitéUMR2000 CNRSParisFrance Decision and Bayesian Computation Institut PasteurUniversitéParis CitéCNRS UMR 3571France Fraunhofer-Institute for Translational Medicine and Pharmacology HamburgGermany Institute of Global Health Faculty of MedicineUniversity of

In July 2023,the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 *** report summarizes the rich discussions that occurred during the *** workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data,social media,and wastewater *** advancements were noted in the development of predictive models,with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease *** role of open collaboration between various stakeholders in modelling was stressed,advocating for the continuation of such partnerships beyond the pandemic.A major gap identified was the absence of a common international framework for data sharing,which is crucial for global pandemic ***,the workshop underscored the need for robust,adaptable modelling frameworks and the integration of different data sources and collaboration across sectors,as key elements in enhancing future pandemic response and preparedness.

关键词： Modelling Covid-19 Infectious diseases Pandemic preparedness Workshop

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