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

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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Dynamic Time-Threshold Based Receive Buffer for Vehicle-to-Cloud Multipath Transmission

Dynamic Time-Threshold Based Receive Buffer for Vehicle-to-C...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Yuyang Zhang Ping Dong Xiaojiang Du Hongbin Luo Hongke Zhang Mohsen Guizani State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University P. R. China School of Electronic and Information Engineering Beijing Jiaotong University P. R. China Dept. of Computer and Information Sciences Temple University USA School of Computer Science and Engineering Beihang University P. R. China Dept. of Computer Science and Engineering Qatar University Qatar

ISBN: (数字)9781728149059

ISBN: (纸本)9781728149066

Vehicle-to-cloud is a network access model that aims to share computing resources for vehicles. In vehicle-to-cloud communication, reliable links are critical. Through multipath transmission, comprehensive utilization of heterogeneous wireless networks between the vehicle and the cloud server can effectively improve the reliability and efficiency of transmission. A large number of studies have pointed out that a receive butter can greatly improve the performance of multipath transmission in vehicle-to-cloud communication. However, most of these studies, which use receive buffers to improve multipath transmission performance, do not explicitly specify the design details of the receive butter. A classic design idea is to use a fixed length receive butter. However, this idea makes it difficult to determine the appropriate butter length in a complex vehicle-to-cloud communication scenario. This paper proposes a Dynamic Time-Threshold (DTT) receive buffer. The DTT receive buffer takes into account the instability of vehicular wireless networks, and dynamically adjusts the time-threshold so that the packets in the receive butter do not affect the overall communication performance due to premature departure or excessive waiting. We verified the performance of the DTT receive butter through a large number of simulations and real tests. By comparison, the performance of DTT receive butter is significantly better than the fixed length receive butter.

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Innovations in Simulation: Experiences With Cloud-Based Simulation Experimentation

Innovations in Simulation: Experiences With Cloud-Based Simu...

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Simulation Winter Conference

作者： Simon J. E. Taylor Anastasia Anagnostou Nura Tijjani Abubakar Tamas Kiss James DesLauriers Gabor Terstyanszky Peter Kacsuk Jozsef Kovacs Shane Kite Gary Pattison James Petry Modelling & Simulation Group Brunel University London Uxbridge UK Centre for Parallel Computing School of Computer Science and Engineering University of Westminster London UK Institute for Computer Science and Control (SZTAKI) Budapest HUNGARY Saker Solutions Ltd Warwickshire UK

ISBN: (数字)9781728194998

ISBN: (纸本)9781728195001

The amount of simulation experimentation that can be performed in a project can be restricted by time, especially if a model takes a long time to simulate and many replications are required. Cloud Computing presents an attractive proposition to speeding up, or extending, simulation experimentation as computing resources can be hired on demand rather than having to invest in costly infrastructure. However, it is not common practice for simulation users to take advantage of this and, arguably, rather than speeding up simulation experimentation users tend to make compromises by using unnecessary model simplification techniques. This may be due to a lack of awareness of what Cloud Computing can offer. Based on several years' experience of innovation in this area, this article presents our experiences in developing Cloud Computing applications for simulation experimentation and discusses what future innovations might be created for the widespread benefit of our simulation community.

关键词： Technological innovation Cloud computing Computational modeling Optimization

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DPNet: Dynamic Pooling Network for Tiny Object Detection

arXiv

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arXiv 2025年

作者： Gong, Luqi Chen, Haotian Chen, Yikun Yao, Tianliang Li, Chao Zhao, Shuai Han, Guangjie School of Computer Science Beijing University of Posts and Telecommunications Beijing China Research Center for Space Computing System Zhejiang Lab Hangzhou China SWJTU-LEEDS Joint School Southwest Jiaotong University Chengdu China Guangdong Zhiyun City construction Technology Co. LTD Zhuhai China Department of Control Science and Engineering College of Electronic and Information Engineering Tongji University Shanghai China Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education Hohai University 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 downsampling 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 downsampling. 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. Copyright © 2025, The Authors. All rights reserved.

关键词： Image coding

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DeepACC:Automate Chromosome Classification based on Metaphase Images using Deep Learning Framework Fused with Prior Knowledge

arXiv

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arXiv 2020年

作者： Luo, Chunlong Yu, Tianqi Luo, Yufan Wang, Manqing Yu, Fuhai Li, Yinhao Tian, Chan Qiao, Jie Xiao, Li Key Laboratory of Intelligent Information Processing Advanced Computer Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China Peking University Third Hospital Beijing China School of Computer and Control Engineering University of Chinese Academy of China

Chromosome classification is an important but difficult and tedious task in karyotyping. Previous methods only classify manually segmented single chromosome, which is far from clinical practice. In this work, we propose a detection based method, DeepACC, to locate and fine classify chromosomes simultaneously based on the whole metaphase image. We firstly introduce the Additive Angular Margin Loss to enhance the discriminative power of model. To alleviate batch effects, we transform decision boundary of each class case-by-case through a siamese network which make full use of prior knowledges that chromosomes usually appear in pairs. Furthermore, we take the clinically seven group criterion as a prior knowledge and design an additional Group Inner-Adjacency Loss to further reduce inter-class similarities. 3390 metaphase images from clinical laboratory are collected and labelled to evaluate the performance. Results show that the new design brings encouraging performance gains comparing to the state-of-the-art baselines. Copyright © 2020, The Authors. All rights reserved.

关键词： Chromosomes

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The impact of generative artificial intelligence on socioeconomic inequalities and policy making

arXiv

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arXiv 2023年

作者： Capraro, Valerio Lentsch, Austin Acemoglu, Daron Akgun, Selin Akhmedova, Aisel Bilancini, Ennio Bonnefon, Jean-François Brañas-Garza, Pablo Butera, Luigi Douglas, Karen M. Everett, Jim A.C. Gigerenzer, Gerd Greenhow, Christine Hashimoto, Daniel A. Holt-Lunstad, Julianne Jetten, Jolanda Johnson, Simon Kunz, Werner H. Longoni, Chiara Lunn, Pete Natale, Simone Paluch, Stefanie Rahwan, Iyad Selwyn, Neil Singh, Vivek Suri, Siddharth Sutcliffe, Jennifer Tomlinson, Joe van der Linden, Sander Van Lange, Paul A.M. Wall, Friederike Van Bavel, Jay J. Viale, Riccardo Department of Psychology University of Milan-Bicocca Italy Department of Economics MIT United States Institute Professor and Department of Economics MIT United States College of Education Michigan State University United States IMT School of Advanced Studies Lucca Italy Toulouse School of Economics France Department of Economics Loyola Andalucia University Spain Department of Economics Copenhagen Business School Denmark School of Psychology University of Kent United Kingdom Max Planck Institute for Human Development Berlin Germany Penn Computer Assisted Surgery and Outcomes Laboratory Department of Surgery Perelman School of Medicine University of Pennsylvania United States Department of Computer and Information Science School of Engineering and Applied Science University of Pennsylvania United States Department of Psychology and Neuroscience Brigham Young University United States School of Psychology University of Queensland Australia MIT Sloan School of Management United States Department of Marketing University of Massachusetts Boston United States Department of Marketing Bocconi University Italy Economic & Social Research Institute Dublin Ireland Department of Humanities University of Turin Italy Department of Service and Technology Marketing Aarhus University Denmark Center for Humans and Machines Max Planck Institute for Human Development Berlin Germany Faculty of Education Monash University Australia Microsoft Research United States York Law School United Kingdom Department of Psychology University of Cambridge United Kingdom Department of Experimental and Applied Psychology Vrije Universiteit Amsterdam Netherlands Department of Management Control and Strategic Management University of Klagenfurt Austria Department of Psychology Center for Neural Science New York University United States Norwegian School of Economics Bergen Norway Department of Economics University of Milan-Bicocca Italy

Generative artificial intelligence has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this article, we provide a state-of-the-art interdisciplinary overview of the potential impacts of generative AI on (mis)information and three information-intensive domains: work, education, and healthcare. Our goal is to highlight how generative AI could worsen existing inequalities while illuminating how AI may help mitigate pervasive social problems. In the information domain, generative AI can democratize content creation and access, but may dramatically expand the production and proliferation of misinformation. In the workplace, it can boost productivity and create new jobs, but the benefits will likely be distributed unevenly. In education, it offers personalized learning, but may widen the digital divide. In healthcare, it might improve diagnostics and accessibility, but could deepen pre-existing inequalities. In each section we cover a specific topic, evaluate existing research, identify critical gaps, and recommend research directions, including explicit trade-offs that complicate the derivation of a priori hypotheses. We conclude with a section highlighting the role of policymaking to maximize generative AI’s potential to reduce inequalities while mitigating its harmful effects. We discuss strengths and weaknesses of existing policy frameworks in the European Union, the United States, and the United Kingdom, observing that each fails to fully confront the socioeconomic challenges we have identified. We propose several concrete policies that could promote shared prosperity through the advancement of generative AI. This article emphasizes the need for interdisciplinary collaborations to understand and address the complex challenges of generative AI. © 2023, CC BY.

关键词： Artificial intelligence

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Retraction notice to "An intrusion identification and prevention for cloud computing: From the perspective of deep learning" [Optik 270 (2022) 170044] (Optik (2022) 270, (S003040262201302X), (10.1016/***.2022.170044))

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Optik 2025年 335卷

作者： Kumar, Anil Umurzoqovich, Rajabov Sherzod Duong, Nguyen Duc Kanani, Pratik Kuppusamy, Arulmani Praneesh, M. Hieu, Minh Ngyen Data Science Research Group School of Computing DIT University Dehradun India The Head of the Department of Quality Control of Education Tashkent Institute of Finance Tashkent Uzbekistan East Asia University of Technology Hanoi Viet Nam Department of Computer Engineering D. J. Sanghvi College of Engineering Mumbai400056 India SASTRA Deemed to be University Thanjavur India PG & Research Department of Computer Science Sri Ramakrishna College of Arts & Science Tamil Nadu Coimbatore India Pusa Research Lab Viet Nam

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://***/locate/withdrawalpolicy). This article has been retracted at the request of the Editor-in-Chief. Post-publication, an investigation conducted on behalf of the journal by Elsevier's Research Integrity & Publishing Ethics team discovered suspicious changes in authorship between the original submission and the revised version of this paper. During revision, the authors Anil Kumar, Rajabov Sherzod Umurzoqovich, Nguyen Duc Duong, Pratik Kanani, Arulmani Kuppusamy, and Praneesh M were added to the revised paper;and three authors were removed;without explanation and without exceptional approval by the journal editor, which is contrary to the journal policy on changes to authorship. None of the Authors responded to three emails that were sent related to this investigation and requesting further information. As such and due to the nature of the irregularities uncovered, the Editor has lost confidence in the validity/integrity and findings of the article and decided to retract the article. The scientific community takes a very strong view on this matter and apologies are offered to readers. © 2025 Elsevier GmbH

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Wide-angle giant photonic spin Hall effect

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Physical Review B 2022年第7期106卷 075409-075409页

作者： Zhihao Chen Yu Chen Yaodong Wu Xinxing Zhou Handong Sun Tony Low Hongsheng Chen Xiao Lin Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Synergetic Innovation Center for Quantum Effects and Applications School of Physics and Electronics Hunan Normal University Changsha 410081 China International Collaborative Laboratory of 2D Materials for Optoelectronics Science and Technology Engineering Technology Research Center for 2D Material Information Function Devices and Systems of Guangdong Province Institute of Microscale Optoelectronics Shenzhen University Shenzhen 518060 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore 637371 Singapore Centre for Disruptive Photonic Technologies (CDPT) School of Physical and Mathematical Sciences Nanyang Technological University Singapore 637371 Singapore Department of Electrical and Computer Engineering University of Minnesota Minneapolis Minnesota 55455 USA Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center College of Information Science and Electronic Engineering Zhejiang University Hangzhou 310027 China International Joint Innovation Center ZJU-UIUC Institute The Electromagnetics Academy at Zhejiang University Zhejiang University Haining 314400 China

The photonic spin Hall effect is a manifestation of the spin-orbit interaction of light and can be measured by a transverse shift δ of photons with opposite spins. The precise measurement of transverse shifts can enable many spin-related applications, such as precise metrology and optical sensing. However, this transverse shift is generally small (i.e., δ/λ<10−1, where λ is the wavelength), which impedes its precise measurement. To date, proposals to generate a giant spin Hall effect (namely, with δ/λ>102) have severe limitations, particularly its occurrence only over a narrow angular cone (with a width of Δθ<1∘). Here we propose a universal scheme to realize the wide-angle giant photonic spin Hall effect with Δθ>70∘ by exploiting the interface between free space and uniaxial epsilon-near-zero media. The underlying mechanism is ascribed to the almost-perfect polarization splitting between s and p polarized waves at the designed interface. Remarkably, this almost-perfect polarization splitting does not resort to the interference effect and is insensitive to the incident angle, which then gives rise to the wide-angle giant photonic spin Hall effect.

关键词： Light-matter interaction Photonics Spin Hall effect Spin-orbit coupling

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Neural-Network-Based Terminal Sliding Mode control for Frequency Stabilization of Renewable Power Systems

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IEEE/CAA Journal of Automatica Sinica 2018年第3期5卷 706-717页

作者： Dianwei Qian Guoliang Fan School of Control and Computer Engineering North China Electric Power University Beijing 102206 China Institute of Automation Chinese Academy of Sciences Beijing 100190 China

This paper addresses a terminal sliding mode control(T-SMC) method for load frequency control(LFC) in renewable power systems with generation rate constraints(GRC).A two-area interconnected power system with wind turbines is taken into account for simulation studies. The terminal sliding mode controllers are assigned in each area to achieve the LFC goal. The increasing complexity of the nonlinear power system aggravates the effects of system uncertainties. Radial basis function neural networks(RBF NNs) are designed to approximate the entire uncertainties. The terminal sliding mode controllers and the RBF NNs work in parallel to solve the LFC problem for the renewable power system. Some simulation results illustrate the feasibility and validity of the presented scheme.

关键词： Generation rate constraint(GRC) load frequency control(LFC) radial basis function neural networks(RBF NNs) renewable power system terminal sliding mode control(T-SMC)

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Accessing the syndemic of COVID-19 and malaria in tervention in Africa

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Infectious Diseases of Poverty 2021年第1期10卷 14-25页

作者： Benyun Shi Jinxin Zheng Shang Xia Shan Lin Xinyi Wang Yang Liu Xiao-Nong Zhou Jiming Liu School of Computer Science and Technology Nanjing Tech UniversityNanjing 211800JiangsuChina National Institute of Parasitic Diseases Chinese Center for Disease Control and PreventionShanghai 200025China Key Laboratory of Parasite and Vector Biology Ministry of HealthShanghai 200025China Chinese Center for Tropical Diseases Research Shanghai 200025China WHO Collaborating Centre for Tropical Diseases Shanghai 200025China National Center for International Research on Tropical Diseases Ministry of Science and TechnologyShanghai 200025China School of Global Health Chinese Center for Tropical Diseases ResearchShanghai Jiao Tong University School of MedicineShanghai 200025China College of Information Engineering Nanjing University of Finance EconomicsNanjing 210003JiangsuChina Department of Computer Science Hong Kong Baptist UniversityHong KongChina.

Background:The pandemic of the coronavirus disease 2019(COVID-19)has caused substantial disruptions to health services in the low and middle-income countries with a high burden of other diseases,such as malaria in sub-Saharan *** aim of this study is to assess the impact of COVID-19 pandemic on malaria transmission potential in malaria-endemic countries in ***:We present a data-driven method to quantify the extent to which the COVID-19 pandemic,as well as various non-pharmaceutical interventions(NPIs),could lead to the change of malaria transmission potential in ***,we adopt a particle Markov Chain Monte Carlo method to estimate epidemiological parameters in each country by fitting the time series of the cumulative number of reported COVID-19 ***,we simulate the epidemic dynamics of COVID-19 under two groups of NPIs:(1)contact restriction and social distanci ng,and(2)early ide ratification and isolation of *** on the simulated epidemic curves,we quantify the impact of COVID-19 epidemic and NPIs on the distribution of insecticide-treated nets(ITNs).Finally,by treating the total number of ITNs available in each country in 2020,we evaluate the negative effects of COVID-19 pandemic on malaria transmission potential based on the notion of vectorial ***:We con duct case studies in four malaria-endemic coun tries,Ethiopia,Nigeria,Tanza nia,and Zambia,in *** epidemiological parameters(i.e.;the basic reproduction number R°and the duration of infection D1)of COVID-19 in each country are estimated as follows:Ethiopia(Rq=1.57,D1=5.32),Nigeria(Ro=2.18,D1=6.58),Tanzania(Ro=2.47,D1=6.01),and Zambia(R0=2.12,D1=6.96).Based on the estimated epidemiological parameters,the epidemic curves simulated under various NPIs indicated that the earlier the interventions are implemented,the better the epidemic is ***,the effect of combined NPIs is better than contact restriction and social di st a ncing *** treating

关键词： COVID-19 pandemic Non-pharmaceutical interventions Particle Markov chain Monte Carlo Insecticidetreated nets Vectorial capacity Malaria transmission potential

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