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arXiv 2022年

作者： Zhu, Dafeng Yang, Bo Liu, Yuxiang Wang, Zhaojian Ma, Kai Guan, Xinping Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao066004 China

Owing to large industrial energy consumption, industrial production has brought a huge burden to the grid in terms of renewable energy access and power supply. Due to the coupling of multiple energy sources and the uncertainty of renewable energy and demand, centralized methods require large calculation and coordination overhead. Thus, this paper proposes a multi-energy management framework achieved by decentralized execution and centralized training for an industrial park. The energy management problem is formulated as a partially-observable Markov decision process, which is intractable by dynamic programming due to the lack of the prior knowledge of the underlying stochastic process. The objective is to minimize long-term energy costs while ensuring the demand of users. To solve this issue and improve the calculation speed, a novel multi-agent deep reinforcement learning algorithm is proposed, which contains the following key points: counterfactual baseline for facilitating contributing agents to learn better policies, soft actor-critic for improving robustness and exploring optimal solutions. A novel reward is designed by Lagrange multiplier method to ensure the capacity constraints of energy storage. In addition, considering that the increase in the number of agents leads to performance degradation due to large observation spaces, an attention mechanism is introduced to enhance the stability of policy and enable agents to focus on important energy-related information, which improves the exploration efficiency of soft actor-critic. Numerical results based on actual data verify the performance of the proposed algorithm with high scalability, indicating that the industrial park can minimize energy costs under different demands. © 2022, CC BY-NC-ND.

关键词： Multi agent systems

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Few-Shot Medical Image Segmentation with High-Fidelity Prototypes

arXiv

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arXiv 2024年

作者： Tang, Song Yan, Shaxu Qi, Xiaozhi Gao, Jianxin Ye, Mao Zhang, Jianwei Zhu, Xiatian IMI Group School of Health Sciences and Engineering University of Shanghai for Science and Technology Shanghai China TAMS Group Department of Informatics Universität Hamburg Hamburg Germany School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Surrey Institute for People-Centred Artificial Intelligence Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China

Few-shot Semantic Segmentation (FSS) aims to adapt a pretrained model to new classes with as few as a single labelled training sample per class. Despite the prototype based approaches have achieved substantial success, existing models are limited to the imaging scenarios with considerably distinct objects and not highly complex background, e.g., natural images. This makes such models suboptimal for medical imaging with both conditions invalid. To address this problem, we propose a novel Detail Self-refined Prototype Network (DSPNet) to constructing high-fidelity prototypes representing the object foreground and the background more comprehensively. Specifically, to construct global semantics while maintaining the captured detail semantics, we learn the foreground prototypes by modelling the multi-modal structures with clustering and then fusing each in a channel-wise manner. Considering that the background often has no apparent semantic relation in the spatial dimensions, we integrate channel-specific structural information under sparse channel-aware regulation. Extensive experiments on three challenging medical image benchmarks show the superiority of DSPNet over previous state-of-the-art methods. The code and data are available at https://***/tntek/DSPNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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MULTI-SCALE FEEDBACK FEATURE REFINEMENT U-NET FOR MEDICAL IMAGE SEGMENTATION

MULTI-SCALE FEEDBACK FEATURE REFINEMENT U-NET FOR MEDICAL IM...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Qin, Xiaofei Xu, Minmin Zheng, Chaoyang He, Changxiang Zhang, Xuedian School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China School of Mechanical Engineering University of Shanghai for Science and Technology Shanghai China Shanghai Key Laboratory of Contemporary Optics System Key Laboratory of Biomedical Optical Technology and Devices Ministry of Education Shanghai China College of Science University of Shanghai for Science and Technology Shanghai China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai China

ISBN: (纸本)9781665438643

Designing a novel and efficient architecture is the thrust of medical image segmentation. In this paper, we introduce a novel network named Multi-scale Feedback Feature Refinement U-Net (MFFRU-Net) for medical image segmentation. We design a simple and effective multi-scale feedback structure. Up-sampling and 1 × 1 convolution are used to feedback the feature maps of different scales in the decoder to the encoder, so that multiple high-level and low-level features are fused to obtain more representative features. Specifically, we propose a feature refinement module (FRM) based on the dual attention mechanism in the middle layer of the network. FRM block can enhance the use of spatial and channel information of image features. We evaluate the MFFRU-Net on two datasets. Comprehensive experimental results show that the proposed method is superior to the original U-Net method and other advanced methods. © 2021 IEEE

关键词： Image segmentation

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Quantitative Hemodynamics in Aortic Dissection: Comparing in Vitro MRI with FSI Simulation in a Compliant Model 11th

Quantitative Hemodynamics in Aortic Dissection: Comparing in...

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11th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2021

作者： Zimmermann, Judith Bäumler, Kathrin Loecher, Michael Cork, Tyler E. Kolawole, Fikunwa O. Gifford, Kyle Marsden, Alison L. Fleischmann, Dominik Ennis, Daniel B. Department of Radiology Stanford University Stanford United States Department of Computer Science Technical University of Munich Munich Germany Division of Radiology VA Palo Alto Health Care System Palo Alto United States Department of Pediatrics Stanford University Stanford United States Department of Mechanical Engineering Stanford University Stanford United States

ISBN: (纸本)9783030787097

The analysis of quantitative hemodynamics and luminal pressure may add valuable information to aid treatment strategies and prognosis for aortic dissections. This work directly compared in vitro 4D-flow magnetic resonance imaging (MRI), catheter-based pressure measurements, and computational fluid dynamics that integrated fluid-structure interaction (CFD FSI). Experimental data was acquired with a compliant 3D-printed model of a type-B aortic dissection (TBAD) that was embedded into a flow circuit with tunable boundary conditions. In vitro flow and relative pressure information were used to tune the CFD FSI Windkessel boundary conditions. Results showed overall agreement of complex flow patterns, true to false lumen flow splits, and pressure distribution. This work demonstrates feasibility of a tunable experimental setup that integrates a patient-specific compliant model and provides a test bed for exploring critical imaging and modeling parameters that ultimately may improve the prognosis for patients with aortic dissections. © 2021, Springer Nature Switzerland AG.

关键词： Fluid structure interaction

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MobiEdge: Mobile Service Provisioning for Edge Clouds with Time-varying Service Demands 27

MobiEdge: Mobile Service Provisioning for Edge Clouds with T...

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27th IEEE International Conference on Parallel and Distributed systems, ICPADS 2021

作者： Wu, Tao Fan, Xiaochen Qu, Yuben Yang, Panlong School of Electronic Engineering National University of Defense Technology China Centre for Assessment and Demonstration Research Academy of Military Science China Nanjing University of Aeronautics and Astronautics Ministry of Industry and Information Technology The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space China School of Computer Science and Technology University of Science and Technology of China China

ISBN: (纸本)9781665408783

With the proliferation of mobile and Internet of Things (IoT) devices, there has been an unprecedented growth of data consumption and computation requests at the network edge. To support latency-sensitive and resource-intensive mobile services, cellular base stations can be integrated with Mobile Edge Computing (MEC) technologies for service provisioning. MEC prompts flexible and configurable provisions of applications or services to make more efficient responses to mobile users' demands. Nevertheless, the time-varying nature of service demands inevitably becomes a vital challenge for existing service provisioning solutions. In this work, we propose MobiEdge, a multi-frame service provisioning scheme across two distinct timescales for MEC networks under various constraints of computation, communication and storage resources. We show that the large timescale indicated by 'frame' is more suitable for adjusting edge server activation and service placement, while the small timescale indicated by 'time slot' is more feasible to schedule users' requests. By leveraging the submodular techniques, we formulate a joint optimization problem and further propose an approximation algorithm with theoretical analysis and proofs. Synthetic and trace-driven evaluation results validate that MobiEdge can benefit both service providers and mobile users in MEC with high profits (e.g., 94% of the optimal) and a relatively low complexity. © 2021 IEEE.

关键词： Mobile edge computing

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Analysis of Negative Capacitance MOSFETs Characteristic with Spacer

Analysis of Negative Capacitance MOSFETs Characteristic with...

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2020 International Symposium on VLSI Technology, systems and Applications, VLSI-TSA 2020

作者： Lee, Yu-Chun Kola, Sekhar Reddy Chen, Chieh-Yang Chuang, Min-Hui Li, Yiming Parallel and Scientific Computing Laboratory Institute of Biomedical Engineering Institute of Communications Engineering Eecs International Graduate Program Department of Electrical and Computer Engineering Center for MmWave Smart Radar System and Technologies National Chiao Tung University 1001 Ta-Hsueh Rd Hsinchu300 Taiwan

ISBN: (纸本)9781728142326

In this work, our study comprises of design and investigation on negative capacitance (NC), metal-oxide-semiconductor (MOS) field effects transistors (MOSFETs) with spacer and source/drain (S/D) overlap engineering. The scope of the work is to boost the performance and high-energy efficiency of the studied NC-MOSFETs by using the ferro electric material (FE). The NC-MOSFETs with the spacer technology can achieve the admirable Ion/Ioff ratio and subthreshold swing (SS), compared with planar MOSFETs. It makes device scaling possible by eliminating the short channel effect (SCE). We further estimated the effect of FE thickness and spacer, which are another critical parameter of obtaining better electrical characteristics and reducing SS. © 2020 IEEE.

关键词： Capacitance

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On the Impact of Interlayer Misalignment for Dual-Layer Data Detection in Three Dimensional Magnetic Recording

SSRN

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SSRN 2024年

作者： Luo, Ke Liao, Yanzhe Zhang, Kezheng Jian, Yugen Wang, Shaobing Chen, Jincai Lu, Ping Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan430074 China School of Computer Science & Technology Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Huazhong University of Science and Technology Wuhan430074 China School of Public Health Hubei University of Medicine Shiyan442000 China

Three-dimensional magnetic recording (3DMR) is a crucial technology for significantly increasing the storage capacity of hard disk drives (HDDs). However, the presence of inter-symbol interference (ISI), inter-track interference (ITI), and inter-layer interference (ILI) poses significant challenges to the accurate detection of data stored on multiple layers. This study addresses the impact of interlayer misalignment on the bit error rate (BER) performance in 3DMR systems. We evaluate the performance based on a neural network estimator for reconstructing the top-layer read response signal using feedback from a Viterbi detector, enabling the separation of bottom-layer signals by subtraction from the mixed readback signal. We introduce a dual-layer partial response maximum likelihood (PRML) detector for simultaneous bit retrieval from both layers. We investigate methods of a per-layer binary classifier and a dual-layer four-class classifier based on neural networks. Our study demonstrates the BER performance of these detection schemes influenced by the interlayer misalignment, especially when the offset is 0, 10%, 50%, and 90% of the bit dimensions between the two layers. The results show that the neural network-based reconstruction and separation method achieves better bottom-layer BER performance under a slight interlayer misalignment. The BER performance benefits more from a mild downtrack offset compared with the crosstrack offset. Both the separation detection based on a neural network and the dual-layer PRML achieve the lowest top-layer BER and the worst bottom-layer BER when the interlayer misalignment is 50% of a bit size. © 2024, The Authors. All rights reserved.

关键词： Bit error rate

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Association of PM_(2.5)Reduction with Improved Kidney Function:A Nationwide Quasiexperiment among Chinese Adults

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Health Data Science 2022年第1期2022卷 115-123页

作者： Yiqun Han Tao Xue Frank J.Kelly Yixuan Zheng Yao Yao Jiajianghui Li Jiwei Li Chun Fan Pengfei Li Tong Zhu Environmental Research Group MRC Centre for Environment and HealthImperial College LondonLondonUK BIC-ESAT and SKL-ESPC College of Environmental Sciences and EngineeringCenter for Environment and HealthPeking UniversityBeijing 100871China Institute of Reproductive and Child Health/Ministry of Health Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics School of Public HealthPeking UniversityBeijingChina Center of Air Quality Simulation and System Analysis Chinese Academy of Environmental Planning100012 BeijingChina China Center for Health Development Studies Peking UniversityBeijingChina College of Computer Science and Technology Zhejiang UniversityHangzhouChina Computer Center Peking University and Peng Cheng LaboratoryChina Advanced Institute of Information Technology Peking UniversityChina

*** evidence from human studies has revealed the adverse impact of ambient fine particles(PM_(2.5))on health outcomes related to metabolic disorders and distant *** exposure to ambient PM_(2.5)leads to kidney impairment remains *** rapid air quality improvement driven by the clean air actions in China since 2013 provides an opportunity for a quasiexperiment to investigate the beneficial effect of PM_(2.5)reduction on kidney *** on two repeated nationwide surveys of the same population of 5115 adults in 2011 and 2015,we conducted a difference-indifference *** in long-term exposure to ambient PM_(2.5)were associated with changes in kidney function biomarkers,including estimated glomerular filtration rate by serum creatinine(GFRscr)or cystatin C(GFRcys),blood urea nitrogen(BUN),and uric acid(UA).*** a 10μg/m^(3)reduction in PM_(2.5),a significant improvement was observed for multiple kidney functional biomarkers,including GFRscr,BUN and UA,with a change of 0.42(95%confidence interval[CI]:0.06,0.78)mL/min/1.73m^(2),-0.38(-0.64,-0.12)mg/dL,and-0.06(-0.12,-0.00)mg/dL,respectively.A lower socioeconomic status,indicated by rural residence or low educational level,enhanced the adverse effect of PM_(2.5)on kidney *** results support a significant nephrotoxicity of PM_(2.5)based on multiple serum biomarkers and indicate a beneficial effect of improved air quality on kidney function.

关键词： Kidney organs ambient

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Flight planning system for UAV to collect 3D reconstruction data

Flight planning system for UAV to collect 3D reconstruction ...

引用

2020 International Conference on Virtual Reality and Visualization, ICVRV 2020

作者： Shi, Chu Luo, Xun Cadavid, Andres Navarro Lozano-Garzon, Carlos Alomia, Gustavo School of Computer Science and Engineering Tianjin University of Technology Tianjin China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Faculty of Engineering Icesi University Cali Colombia Los Andes University Department of System Engineering and Computation Bogota Colombia

ISBN: (纸本)9780738142524

Small unmanned aerial vehicles (UAVs) can use their flexible maneuverability to effectively collect images of urban buildings, and use the current multi-view stereo method to generate advanced 3D urban models. But the quality of the generated 3D model largely depends on a flight plan. This paper proposes a method to automatically generate a rough 3D contour based on the 2D contour of the building extracted from the orthophoto of the drone, and plan a shooting viewpoint for 3D reconstruction based on the contour information. The optimized ant colony algorithm is used to plan the optimal power consumption cost path of the viewpoint, and the UE4 game engine is used to simulate the planning result. This method can effectively simplify the flight planning process and minimize the small UAV in the acquisition process The problem of excessive consumption caused by alternate lifting. © 2020 IEEE.

关键词： Image reconstruction

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Memorization in deep learning: A survey

arXiv

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arXiv 2024年

作者： Wei, Jiaheng Zhang, Yanjun Zhang, Leo Yu Ding, Ming Chen, Chao Ong, Kok-Leong Zhang, Jun Xiang, Yang School of Accounting Information System and Supply Chain RMIT University MelbourneVIC3001 Australia School of Computer Science University of Technology Sydney SydneyNSW2007 Australia School of Information and Communication Technology Griffith University BrisbaneQLD4215 Australia Data61 CSIRO SydneyNSW2015 Australia School of Science Computing and Engineering Technologies Swinburne University of Technology MelbourneVIC3122 Australia

Deep Learning (DL) powered by Deep Neural Networks (DNNs) has revolutionized various domains, yet understanding the intricacies of DNN decision-making and learning processes remains a significant challenge. Recent investigations have uncovered an interesting memorization phenomenon in which DNNs tend to memorize specific details from examples rather than learning general patterns, affecting model generalization, security, and privacy. This raises critical questions about the nature of generalization in DNNs and their susceptibility to security breaches. In this survey, we present a systematic framework to organize memorization definitions based on the generalization and security/privacy domains and summarize memorization evaluation methods at both the example and model levels. Through a comprehensive literature review, we explore DNN memorization behaviors and their impacts on security and privacy. We also introduce privacy vulnerabilities caused by memorization and the phenomenon of forgetting and explore its connection with memorization. Furthermore, we spotlight various applications leveraging memorization and forgetting mechanisms, including noisy label learning, privacy preservation, and model enhancement. This survey offers the first-in-kind understanding of memorization in DNNs, providing insights into its challenges and opportunities for enhancing AI development while addressing critical ethical concerns. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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