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A two-stage multi-fidelity design optimization for K-mer-based pattern recognition (KPR) in image processing

A two-stage multi-fidelity design optimization for K-mer-bas...

ASME 2020 International Design engineering Technical Conferences and Computers and Information in engineering Conference, IDETC-CIE 2020

作者： Yao, Yu-Ta Wu, Yu-Wei Lin, Po Ting Department of Mechanical Engineering National Taiwan University of Science and Technology Taipei10607 Taiwan Graduate Institute of Biomedical Informatics Taipei Medical University Taipei11031 Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei11031 Taiwan Center for Cyber-Physical System Innovation National Taiwan University of Science and Technology Taipei10607 Taiwan

ISBN: (纸本)9780791884010

Pattern recognition has been widely used in various applications of image processing. It is used to extract meaningful image features from the given image samples and to build classification systems with the intelligence of human recognition. Convolutional Neural Network (CNN) [1] has been one of the most popular and widely used methods for image pattern recognition applications. However, CNN was known not to be rotation-invariant to image patterns. It usually required a larger amount of training image dataset with greater variations in positions and orientations, or additional numerical treatments of spatial transformations [2]. On the other hand, K-mer-based Pattern Recognition (KPR) [3] has been developed to apply an unique way of rotation-invariant sampling to the inspected image pattern and analyze the frequency of the captured pattern features. A classification system was then built based on the Kmer frequency for the desired pattern recognition. In this paper, a series of tests and verifications of the KPR was done. It was found that finding the appropriate design parameters of the KPR for a specific application of image pattern recognition could be costly. A two-stage multi-fidelity design optimization was utilized to improve the efficiency of finding the parameters of KPR. In each iteration of the multi-fidelity design optimization procedure, the first stage was to evaluate the accuracy and efficiency of the design parameters in the K-mer-based pattern classification based on a full set of the given images. The second stage was to find a newer set of design parameters that performed the best based on a smaller set of images, which provided a classification set with lower fidelity than the original one. As a result, the proposed strategy of the multi-fidelity design optimization was more efficient than finding the optimal design parameters based on the full set of the given images. Copyright © 2020 ASME.

关键词： Iterative methods

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Multi-task deep learning for large-scale building detail extraction from high-resolution satellite imagery

arXiv

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arXiv 2023年

作者： Qian, Zhen Chen, Min Sun, Zhuo Zhang, Fan Xu, Qingsong Guo, Jinzhao Xie, Zhiwei Zhang, Zhixin Nanjing Normal University Nanjing210023 China State Key Laboratory Cultivation Base of Geographical Environment Evolution Nanjing210023 China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing210023 China Institute of Remote Sensing and Geographical Information System School of Earth and Space Sciences Peking University China Department of Aerospace and Geodesy Data Science in Earth Observation Technical University of Munich Arcisstraße 21 Munich Bavaria80333 Germany School of Transportation and Geomatics Engineering Shenyang Jianzhu University Shenyang China College of Geography & Marine Nanjing University PO Box Nanjing2100913 China

Understanding urban dynamics and promoting sustainable development requires comprehensive insights about buildings, including their spatial locations, rooftop configurations, physical forms, and urban functions. While geospatial artificial intelligence has advanced the extraction of such details from Earth observational data, existing methods often suffer from computational inefficiencies and inconsistencies when compiling unified building-related datasets for practical applications. To bridge this gap, we introduce the Multi-task Building Refiner (MT-BR), an adaptable neural network tailored for simultaneous extraction of spatial and attributional building details from high-resolution satellite imagery, exemplified by building rooftops, urban functional types, and roof architectural types. Notably, MT-BR can be fine-tuned to incorporate additional building details, extending its applicability. For large-scale applications, we devise a novel spatial sampling scheme that strategically selects limited but representative image samples. This process optimizes both the spatial distribution of samples and the urban environmental characteristics they contain, thus enhancing extraction effectiveness while curtailing data preparation expenditures. We further enhance MT-BR’s predictive performance and generalization capabilities through the integration of advanced augmentation techniques. Our quantitative results highlight the efficacy of the proposed methods. Specifically, networks trained with datasets curated via our sampling method demonstrate improved predictive accuracy relative to those using alternative sampling approaches, with no alterations to network architecture. Moreover, MT-BR consistently outperforms other state-of-the-art methods in extracting building details across various metrics, achieving gains between 2% and 20%, with a further 5% improvement when augmentation strategies were adopted. The real-world practicality of our approaches is also demonstrated in

关键词： Buildings

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Secure and Privacy-preserving data-sharing Framework based on Blockchain Technology for Al-Najaf/Iraq Oil Refinery

Secure and Privacy-preserving Data-sharing Framework based o...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Samir M. Umran SongFeng Lu Zaid Ameen Abduljabbar Zhi Lu Bingyan Feng Lu Zheng Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Ministry of Industry and Minerals Iraqi Cement State Company Baghdad Iraq Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China College of Education for Pure Sciences University of Basrah Technical Computer Engineering Department Al-Kunooze University College Basrah Iraq School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Wuhan Huazhong Numerical Control Co. Ltd Industrial Internet Research Institute Wuhan China College of Computer Science South-Central University for Nationalities Wuhan China

The Industrial Internet of Things or Industry 4.0 efficiently enhances the manufacturing process in terms of raising productivity, system performance, cost reduction, and building large-scale systems. It enables the connection of numerous heterogeneous devices and sensors into the internet network through the utilization of revolutionary techniques that transfer the manufacturing paradigm to intelligent processes. The traditional industrial internet of things adopts centralized architectures and complex encryption algorithms that suffer from several cyber-security threats and lead to data leakage, privacy disclosure, and high computational and communicational costs. In our work, we proposed a decentralized, efficient, low-power, scalable, secure, privacy-preserving, and trusting architecture based on a private blockchain network/smart contract and interplanetary file system technology for the Al-NajafyIraq oil refinery factory. That enables autonomous working and provides a high level of security authentication and privacy preservation, P2P communication, remote access, immutability and information backtracking. Traditional blockchain technology has storage limitation issues, most recent works adopted external servers or the cloud for storage purposes. Our proposed scheme addresses the limitations of blockchain data storage by adopting an interplanetary file system (IPFS) network; provides an efficient data encryption mechanism at the perceptual layer by adopting a lightweight encryption algorithm, high-performance, and ultra-low-power consumption ARM Cortex-M microcontroller. In addition, our architecture succeeds in merging blockchain technology, IoTs, and an IPFS with the oil refinery as an industrial application area. Moreover, it provides an efficient framework that resists common cyber-security attacks and realizes cyber-security requirements represented by data availability, integrity, and confidentiality.

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

arXiv

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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Fusing Bluetooth with Pedestrian Dead Reckoning: A Floor Plan-Assisted Positioning Approach

arXiv

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arXiv 2025年

作者： Pan, Wenxuan Yang, Yang Chen, Mingzhe Wei, Dong Guo, Caili Mao, Shiwen Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical and Computer Engineering the Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Wireless Engineering Research and Education Center Auburn University AuburnAL36849 United States

Floor plans can provide valuable prior information that helps enhance the accuracy of indoor positioning systems. However, existing research typically faces challenges in efficiently leveraging floor plan information and applying it to complex indoor layouts. To fully exploit information from floor plans for positioning, we propose a floor plan-assisted fusion positioning algorithm (FP-BP) using Bluetooth low energy (BLE) and pedestrian dead reckoning (PDR). In the considered system, a user holding a smartphone walks through a positioning area with BLE beacons installed on the ceiling, and can locate himself in real time. In particular, FP-BP consists of two phases. In the offline phase, FP-BP programmatically extracts map features from a stylized floor plan based on their binary masks, and constructs a mapping function to identify the corresponding map feature of any given position on the map. In the online phase, FP-BP continuously computes BLE positions and PDR results from BLE signals and smartphone sensors, where a novel grid-based maximum likelihood estimation (GML) algorithm is introduced to enhance BLE positioning. Then, a particle filter is used to fuse them and obtain an initial estimate. Finally, FP-BP performs post-position correction to obtain the final position based on its specific map feature. Experimental results show that FP-BP can achieve a real-time mean positioning accuracy of 1.19 m, representing an improvement of over 28% compared to existing floor plan-fused baseline algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Photomapping

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Society for Cardiovascular Magnetic Resonance recommendations toward environmentally sustainable cardiovascular magnetic resonance

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Journal of Cardiovascular Magnetic Resonance 2025年第1期27卷 101840页

作者： Hanneman, Kate Picano, Eugenio Campbell-Washburn, Adrienne E Zhang, Qiang Browne, Lorna Kozor, Rebecca Battey, Thomas Omary, Reed Saldiva, Paulo Ng, Ming Rockall, Andrea Law, Meng Kim, Helen Lee, Yoo Jin Mills, Rebecca Ntusi, Ntobeko Bucciarelli-Ducci, Chiara Markl, Michael Department of Medical Imaging University of Toronto Toronto ON Canada University Clinical Center of Serbia Cardiology Division University of Belgrade Serbia Cardiovascular Branch Division of Intramural Research National Heart Lung and Blood Institute National Institutes of Health Bethesda MD United States RDM Division of Cardiovascular Medicine & NDPH Big Data Institute University of Oxford Oxford United Kingdom Dept of Radiology Division of Pediatric Radiology Children's Hospital Colorado University of Colorado School of Medicine United States University of Sydney and Royal North Shore Hospital Sydney Australia Department of Radiology and Medical Imaging University of Virginia Health System Charlottesville VA United States Departments of Radiology & Biomedical Engineering Vanderbilt University Nashville TN United States Department of Pathology University of Sao Paulo School of Medicine Sao Paulo Brazil Department of Diagnostic Radiology School of Clinical Medicine Li Ka Shing Faculty of Medicine The University of Hong Kong Hong Kong Dept of Surgery and Cancer Faculty of Medicine Imperial College London United Kingdom Departments of Neuroscience Electrical and Computer Systems Engineering Monash University Australia Department of Radiology University of Washington WA United States Department of Radiology and Biomedical Engineering UCSF San Francisco CA United States University of Oxford Centre for Clinical Magnetic Resonance Research Oxford United Kingdom Groote Schuur Hospital Department of Medicine University of Cape Town Cape Town South Africa Royal Brompton and Harefield Hospitals Guys’ & St Thomas NHS Trust London United Kingdom Department of Radiology Feinberg School of Medicine Northwestern University Chicago IL United States Greenwell Project Nashville TN United States Department of Radiology Alfred Health Melbourne Australia School of Biomedical Engineering and Imaging Sciences Faculty of Life Sciences and Medicine King's College U

Delivery of health care, including medical imaging, generates substantial global greenhouse gas emissions. The cardiovascular magnetic resonance (CMR) community has an opportunity to decrease our carbon footprint, mitigate the effects of the climate crisis, and develop resiliency to current and future impacts of climate change. The goal of this document is to review and recommend actions and strategies to allow for CMR operation with improved sustainability, including efficient CMR protocols and CMR imaging workflow strategies for reducing greenhouse gas emissions, energy, and waste, and to decrease reliance on finite resources, including helium and waterbody contamination by gadolinium-based contrast agents. The article also highlights the potential of artificial intelligence and new hardware concepts, such as low-helium and low-field CMR, in achieving these aims. Specific actions include powering down magnetic resonance imaging scanners overnight and when not in use, reducing low-value CMR, and implementing efficient, non-contrast, and abbreviated CMR protocols when feasible. data on estimated energy and greenhouse gas savings are provided where it is available, and areas of future research are highlighted. © 2025 The Authors

关键词： Climate change Energy consumption Greenhouse gas emission Planetary health Sustainability

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Energy Management and Trajectory Optimization for UAV-Enabled Legitimate Monitoring systems

arXiv

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arXiv 2020年

作者： Hu, Shuyan Wu, Qingqing Wang, Xin State Key Laboratory of ASIC and System School of Information Science and Technology Fudan University Shanghai200433 China Department of Electrical and Computer Engineering National University of Singapore Singapore119077 Singapore State Key Laboratory of ASIC and System Shanghai Institute for Advanced Communication and Data Science Department of Communication Science and Engineering Fudan University Shanghai200433 China

Thanks to their quick placement and high flexibility, unmanned aerial vehicles (UAVs) can be very useful in the current and future wireless communication systems. With a growing number of smart devices and infrastructure-free communication networks, it is necessary to legitimately monitor these networks to prevent crimes. In this paper, a novel framework is proposed to exploit the flexibility of the UAV for legitimate monitoring via joint trajectory design and energy management. The system includes a suspicious transmission link with a terrestrial transmitter and a terrestrial receiver, and a UAV to monitor the suspicious link. The UAV can adjust its positions and send jamming signal to the suspicious receiver to ensure successful eavesdropping. Based on this model, we first develop an approach to minimize the overall jamming energy consumption of the UAV. Building on a judicious (re)formulation, an alternating optimization approach is developed to compute a locally optimal solution in polynomial time. Furthermore, we model and include the propulsion power to minimize the overall energy consumption of the UAV. Leveraging the successive convex approximation method, an effective iterative approach is developed to find a feasible solution fulfilling the Karush-Kuhn-Tucker (KKT) conditions. Extensive numerical results are provided to verify the merits of the proposed schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Low-latency federated learning over wireless channels with differential privacy

arXiv

引用

arXiv 2021年

作者： Wei, Kang Li, Jun Ma, Chuan Ding, Ming Chen, Cailian Jin, Shi Han, Zhu Poor, H. Vincent School of Electrical and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China Data61 CSIRO SydneyNSW2015 Australia The Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul Korea Republic of The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

In federated learning (FL), model training is distributed over clients and local models are aggregated by a central server. The performance of uploaded models in such situations can vary widely due to imbalanced data distributions, potential demands on privacy protections, and quality of transmissions. In this paper, we aim to minimize FL training delay over wireless channels, constrained by overall training performance as well as each client's differential privacy (DP) requirement. We solve this problem in the framework of multi-agent multiarmed bandit (MAMAB) to deal with the situation where there are multiple clients confornting different unknown transmission environments, e.g., channel fading and interferences. Specifically, we first transform the long-term constraints on both training performance and each client's DP into a virtual queue based on the Lyapunov drift technique. Then, we convert the MAMAB to a max-min bipartite matching problem at each communication round, by estimating rewards with the upper confidence bound (UCB) approach. More importantly, we propose two efficient solutions to this matching problem, i.e., modified Hungarian algorithm and greedy matching with a better alternative (GMBA), in which the first one can achieve the optimal solution with a high complexity while the second one approaches a better tradeoff by enabling a verified low-complexity with little performance loss. In addition, we develop an upper bound on the expected regret of this MAMAB based FL framework, which shows a linear growth over the logarithm of communication rounds, justifying its theoretical feasibility. Extensive experimental results are conducted to validate the effectiveness of our proposed algorithms, and the impacts of various parameters on the FL performance over wireless edge networks are also discussed. Copyright © 2021, The Authors. All rights reserved.

关键词： Multi agent systems

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Comprehensive Theoretical Quantification of Secondary Electron Emission from Silicon

SSRN

引用

SSRN 2023年

作者： Khan, Muhammad Saadat Shakoor Mao, Shifeng Zou, Yanbo Lu, Dabao Da, Bo Li, Yonggang Ding, Zejun Department of Physics University of Science and Technology of China Anhui Hefei230026 China Hefei National Research Center for Physical Sciences at the Microscale University of Science and Technology of China Anhui Hefei230026 China Department of Nuclear Science and Engineering University of Science and Technology of China Anhui Hefei230026 China School of Physics and Electronic Engineering Xinjiang Normal University Xinjiang Urumchi830054 China Microscope Science and Technology Limited Anhui Hefei230093 China Research and Services Division of Materials Data and Integrated System National Institute for Materials Science 1-1 Namiki Ibaraki Tsukuba305-0044 Japan Research Center for Advanced Measurement and Characterization National Institute for Materials Science 1-2-1 Sengen Ibaraki Tsukuba305-0047 Japan Key Laboratory of Materials Physics Insititute of Solid State Physics Chinese Academy of Sciences Anhui Hefei230031 China

Though intensive experimental studies have been carried out on electron emission properties in past decades, the reliable data from accurate experimental measurements for clean and smooth surfaces are still quite limited. In this work, we have performed a comprehensive Monte Carlo simulation of electron emission yields, the secondary electron yield (SEY,δ), backscattered electron coefficient (BSC,η) and total electron yield (TEY,σ=δ+η), from silicon and the uncertainty quantification of the theoretical calculation results for the first time. The considered uncertainty factors in the physical modelling include work function data, optical energy loss function data, dielectric function models for electron inelastic scattering and the scattering potentials for electron elastic scattering. The calculation results show that δ is significantly influenced by the work function while the dielectric function model has a slight effect. Elastic scattering potential and energy loss function affects both δ and η. Our simulatedσ η data agree with most of the experimental measurements while the simulated mean σ data are well below most of the experimental data. The experimental σ data measured by Goto lie within the uncertainty range of our simulation results. This work has provided sufficient ground for the necessity of building a theoretical database of electron emission yield. © 2023, The Authors. All rights reserved.

关键词： Silicon

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Monthly methane emissions in Chinese mainland provinces from 2013-2022

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Scientific data 2025年第1期12卷 948页

作者： Duo Cui Siyao Yang Xuanren Song Xiaoting Huang Cuncun Duan Mingrui Ji Zhongyan Li Siqi Yu Zhu Deng Piyu Ke Xinyu Dou Taochun Sun Zhu Liu International Research Center of Big Data for Sustainable Development Goals Beijing 100094 China. duocui@hku.hk. Department of Earth Sciences The University of Hong Kong Hong Kong SAR China. duocui@hku.hk. Department of Earth System Science Tsinghua University Beijing 100084 China. Faculty of Information Technology Monash University Melbourne 3168 Australia. College of Water Sciences Beijing Normal University Beijing 100875 China. College of Environmental & Resource Sciences Zhejiang University Hangzhou Zhejiang 310058 China. Department of Civil and Environmental Engineering Stanford University Stanford CA 94305 USA. Department of Geography The University of Hong Kong Hong Kong SAR China. Department of Earth System Science Stanford University Stanford CA 94305 USA. International Research Center of Big Data for Sustainable Development Goals Beijing 100094 China. zhuliu@***. Department of Earth System Science Tsinghua University Beijing 100084 China. zhuliu@***.

As the world's largest source of methane emissions, accurately measuring and tracking China's emissions across various sectors is essential for global climate change efforts. Methane, a potent greenhouse gas, is emitted from diverse anthropogenic and natural sources, many of which exhibit pronounced temporal variability. In particular, emissions from rice cultivation, energy use, and livestock management show strong seasonal patterns, yet high-frequency and spatially detailed methane emission inventories have been lacking. This study introduces the Monthly Methane Emission Inventory for China's Provinces (MMCP), a comprehensive dataset covering the period from January 2013 to December 2022. The dataset includes emissions data from eight key sectors: coal mining, oil and gas systems, energy combustion, rice cultivation, livestock, solid waste, wastewater, and wetlands. By offering sector-specific and temporally resolved emission estimates, MMCP serves as a valuable resource for scientific research, policy evaluation, and emission mitigation planning. This inventory facilitates improved understanding of emission trends and supports more accurate modeling of atmospheric methane concentrations and climate feedbacks.

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