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arXiv 2024年

作者： Nie, Qiushi Zhang, Xiaoqing Hu, Yan Gong, Mingdao Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Singapore Eye Research Institute 169856 Singapore State Key Laboratory of Ophthalmology Optometry and Visual Science Eye Hospital Wenzhou Medical University Wenzhou325027 China

Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse information of images, which may be captured under different times, angles, or modalities. Although several surveys have reviewed the development of medical image registration, these surveys have not systematically summarized methodologies of existing medical image registration methods. To this end, we provide a comprehensive review of these methods from traditional and deep learning-based directions, aiming to help audiences understand the development of medical image registration quickly. In particular, we review recent advances in retinal image registration at the end of each section, which has not attracted much attention. Additionally, we also discuss the current challenges of retinal image registration and provide insights and prospects for future research. © 2024, CC BY-NC-SA.

关键词： Deep learning

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Evaluation of Trade-Off Between Compression Ratio and Hardware Cost for Adaptive Bandwidth Compression Hardware Platform

Evaluation of Trade-Off Between Compression Ratio and Hardwa...

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IEEE Cool Chips

作者： Tomohiro Ueno Kaito Kitazume Masato Kiyama Kazutomo Yoshii Kento Sato Norihisa Fujita Ryohei Kobayashi Taisuke Boku Kentaro Sano Center for Computational Science Riken Kobe Japan Degree Programs in Systems and Information Engineering University of Tsukuba Tsukuba Japan Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan Mathematics and Computer Science Argonne National Laboratory Lemont IL USA Center for Computational Sciences University of Tsukuba Tsukuba Japan Supercomputing Research Center Institute of Integrated Research Institute of Science Tokyo Kanagawa Japan

ISBN: (数字)9798331534844

ISBN: (纸本)9798331534851

Hardware-based, high-throughput data compression is a promising approach to reduce data movement costs in largescale systems and networks, thereby improving overall performance and power efficiency. However, since a data compression algorithm is only effective if it is suitable for the characteristics of the target data, data compression hardware with a fixed algorithm is unrealistic for use in general-purpose environments. To address this challenge, we have been researching adaptive bandwidth compression (ABC) hardware that flexibly provides an effective algorithm depending on the input data. This paper presents the design and the Chisel-based implementation of the ABC hardware platform for encoding compressed data and generating output blocks, as well as introducing a quantization parameter to reduce the circuit area. Our evaluation shows that the proposed quantization parameter can not only reduce the hardware cost, but also control the trade-off between the effective compression ratio and the hardware cost. In addition, based on the evaluation results, we discuss the design optimization of the ABC hardware platform.

关键词： Performance evaluation Costs Quantization (signal) Data compression Bandwidth Hardware Space exploration Resource management Compression algorithms Field programmable gate arrays

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Design and Evaluation of a Low-profile Haptic Interface Based on Surface Electrical Nerve Stimulation

Design and Evaluation of a Low-profile Haptic Interface Base...

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Telepresence, IEEE Conference on

作者： Rachel S. Jakes Luis Mesias Veronica J. Santos Michael J. Fu Dustin J. Tyler Human Fusions Institute (HFI) Case Western Reserve University (CWRU) Dept. of Biomedical Engineering CWRU Dept. of Electrical Computer and Systems Engineering CWRU Dept. of Mechanical and Aerospace Engineering University of California Los Angeles (UCLA) Dept. of Physical Medicine & Rehabilitation The MetroHealth System Dept. of VA Northeast Ohio Healthcare System Advanced Platform Technology Center

ISBN: (数字)9798331528249

ISBN: (纸本)9798331528256

Haptic feedback in telepresence applications is vital for remote task performance and object manipulation. To maximize telepresence capabilities, haptic systems need to be integrated with immersive telepresence interfaces such as virtual reality, which offer benefits such as improved spatial awareness and operator mobility. Compatibility with the mobility of these systems and their built-in optical hand tracking require low-profile wearable interfaces. Further, for full functional benefits, haptic systems must be able to elicit spatially congruent sensation without impeding operator movement and physical object interactions. In this paper, we describe the design of a lightweight, compact, and adaptable surface electrical nerve stimulation-based haptic interface. We demonstrate its capacity to consistently generate spatially congruent sensation on the all five fingertips without occlusion, as well as its compatibility with headset-based optical hand tracking.

关键词： Hands Telepresence Tracking Stimulated emission Optical feedback Virtual reality Electrical stimulation Haptic interfaces Optical sensors

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Privacy Preservation for Cloud-Edge-Collaborative Energy Management System Using Post-Quantum Homomorphic Encryption

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IEEE Transactions on Smart Grid 2025年

作者： Jiang, Cheng Li, Xue Du, Dajun Qian, Hong Wen, Mi Wu, Lei Findeisen, Rolf Shanghai University of Electric Power Shanghai Key Laboratory of Power Station Automation Technology College of Automation Engineering Shanghai200090 China Shanghai University Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronical Engineering and Automation Shanghai200072 China Shanghai University of Electric Power College of Computer Science and Technology Shanghai201306 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States TU Darmstadt Control and Cyber-Physical Systems Laboratory Darmstadt64227 Germany

Cloud-based energy management systems (EMS) in smart grids face privacy challenges, as existing methods based on traditional homomorphic encryption support limited operations and are vulnerable to quantum attacks. We propose a privacy preservation method for smart grid energy management system, leveraging edge-assisted computing and ring learning with errors (RLWE) homomorphic encryption. Our approach enables both addition and multiplication operations on encrypted smart grid data while defending against quantum threats. We demonstrate the methods security under the RLWE hardness assumption and validate its effectiveness through an experimental platform simulating smart grid environments. This work addresses computational limitations in current privacy-preserving techniques, enhancing data security for evolving smart grid technologies © 2010-2012 IEEE.

关键词： Power management (telecommunication)

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Optimizing Reservoir Computing Based on an Alternating Input-Driven Spin-Torque Oscillator

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Physical Review Applied 2023年第2期20卷 024069-024069页

作者： Xuezhao Wu Zihan Tong Qiming Shao Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong SAR China State Key Laboratory of Advanced Displays and Optoelectronics Technologies The Hong Kong University of Science and Technology Clear Water Bay Hong Kong SAR China ACCESS – AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong SAR China Department of Physics and IAS Center for Quantum Technologies The Hong Kong University of Science and Technology Hong Kong SAR China Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology The Hong Kong University of Science and Technology Hong Kong SAR China

Neurons in the brain show nonlinear oscillatorlike behaviors, inspiring the development of neuromorphic computing with spin-torque oscillators for their low power consumption and high integration density. The time-multiplexing technique enables utilizing only one device to achieve neuromorphic computing, further reducing power consumption and simplifying device fabrication. However, optimizing such systems remains a challenge and needs further development. A controversial hypothesis claims that dynamic systems possess the optimal computational capacity to address the information at the dynamical phase transition. Here, we apply this hypothesis to improve a reservoir computing system based on an alternating input-driven spin-torque oscillator. The input-driven property of reservoir computing allows us to exploit the dynamics of a spin-torque oscillator by manipulating the input stream. By tuning the ac amplitude in inputs, the system can exhibit diversified dynamic regimes indicated by the largest Lyapunov exponent and echo-state property spectra. Our findings demonstrate that the proper configuration of ac inputs for reservoir computing based on a single spin-torque oscillator can adjust its information-processing capacity profile and enhance its computational performance for specific tasks.

关键词： Magnetization dynamics Network phase transitions Neuromorphic computing Spintronics Coupled oscillators Devices for digital logic, storage & processing Spin torque nano-oscillators Chaos & nonlinear dynamics

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The Future of Smart Grids: Revolutionizing Energy Distribution with advanced IoT, and Renewable Integration

The Future of Smart Grids: Revolutionizing Energy Distributi...

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Global Conference for Advancement in technology (GCAT)

作者： S. Avinash J.C. Vinitha S. Sree Dharinya V. Samuthira Pandi Lakshmi Priya J Jaishika S Department of MBA St. Joseph's College of Engineering Chennai Department of Electrical and Electronics Engineering Rajalakshmi Engineering College Chennai Tamil Nadu India School of Computer Science Engineering and Information Systems VIT University Vellore Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of ECE Saveetha Engineering College Chennai Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

Smart grids, which integrate modern Internet of Things (IoT) technology and renewable energy sources to boost efficiency, sustainability, and dependability, are poised to bring about a dramatic shift in the future of energy distribution. Intelligent grids are expected to bring about this shift. Focusing on the role of the Internet of Things (IoT) in enabling real-time monitoring and control, as well as the incorporation of renewable energy to support a low-carbon future, this research investigates the potential for smart grids to change the energy sector. This study gives insights into how smart grids can be efficiently deployed to satisfy the growing energy needs of society by studying the technological breakthroughs, obstacles, and benefits of smart grids. Specifically, the study's focus is on smart grids. The research highlights the essential role that smart grids play in aiding the shift towards a more sustainable and resilient energy infrastructure by conducting an in-depth analysis of case studies, pilot projects, and industry trends all around the world. Providing recommendations for policymakers, industry stakeholders, and utilities to exploit the full potential of smart grids in determining the future of energy distribution, the findings contribute to the continuing discourse on the strategic development and implementation of smart grid technologies. Both of these topics are currently being discussed.

关键词： Industries Renewable energy sources Protocols Real-time systems Smart grids Internet of Things Stakeholders Standards Resilience Investment

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Incremental Few-Shot Semantic Segmentation via Embedding Adaptive-Update and Hyper-class Representation

arXiv

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arXiv 2022年

作者： Shi, Guangchen Wu, Yirui Liu, Jun Wan, Shaohua Wang, Wenhai Lu, Tong Collage of Computer and Information Hohai University Nanjing China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore Singapore Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China Shanghai AI Laboratory Shanghai China National Key Lab for Novel Software Technology Nanjing University Nanjing China

Incremental few-shot semantic segmentation (IFSS) targets at incrementally expanding model’s capacity to segment new class of images supervised by only a few samples. However, features learned on old classes could significantly drift, causing catastrophic forgetting. Moreover, few samples for pixel-level segmentation on new classes lead to notorious overfitting issues in each learning session. In this paper, we explicitly represent class-based knowledge for semantic segmentation as a category embedding and a hyper-class embedding, where the former describes exclusive semantical properties, and the latter expresses hyper-class knowledge as class-shared semantic properties. Aiming to solve IFSS problems, we present EHNet, i.e., Embedding adaptive-update and Hyper-class representation Network from two aspects. First, we propose an embedding adaptive-update strategy to avoid feature drift, which maintains old knowledge by hyper-class representation, and adaptively update category embeddings with a class-attention scheme to involve new classes learned in individual sessions. Second, to resist overfitting issues caused by few training samples, a hyper-class embedding is learned by clustering all category embeddings for initialization and aligned with category embedding of the new class for enhancement, where learned knowledge assists to learn new knowledge, thus alleviating performance dependence on training data scale. Significantly, these two designs provide representation capability for classes with sufficient semantics and limited biases, enabling to perform segmentation tasks requiring high semantic dependence. Experiments on PASCAL-5i and COCO datasets show that EHNet achieves new state-of-the-art performance with remarkable advantages. Copyright © 2022, The Authors. All rights reserved.

关键词： Embeddings

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Biomedical data and AI

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Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Reducing Idleness in Financial Cloud Services via Multi-objective Evolutionary Reinforcement Learning based Load Balancer

arXiv

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arXiv 2023年

作者： Yang, Peng Zhang, Laoming Liu, Haifeng Li, Guiying Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology Shenzhen518055 China Guangdong OPPO Mobile Telecommunications Corp. Ltd Shenzhen518052 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China

In recent years, various companies have started to shift their data services from traditional data centers to the cloud. One of the major motivations is to save on operational costs with the aid of cloud elasticity. This paper discusses an emerging need from financial services to reduce the incidence of idle servers retaining very few user connections, without disconnecting them from the server side. This paper considers this need as a bi-objective online load balancing problem. A neural network based scalable policy is designed to route user requests to varied numbers of servers for the required elasticity. An evolutionary multi-objective training framework is proposed to optimize the weights of the policy. Not only is the new objective of idleness is reduced by over 130% more than traditional industrial solutions, but the original load balancing objective itself is also slightly improved. Extensive simulations with both synthetic and real-world data help reveal the detailed applicability of the proposed method to the emergent problem of reducing idleness in financial services. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Distribution Shift Matters for Knowledge Distillation with Webly Collected Images

arXiv

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arXiv 2023年

作者： Tang, Jialiang Chen, Shuo Niu, Gang Sugiyama, Masashi Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China Center for Advanced Intelligence Project RIKEN Japan The Graduate School of Frontier Sciences The University of Tokyo Japan

Knowledge distillation aims to learn a lightweight student network from a pre-trained teacher network. In practice, existing knowledge distillation methods are usually infeasible when the original training data is unavailable due to some privacy issues and data management considerations. Therefore, data-free knowledge distillation approaches proposed to collect training instances from the Internet. However, most of them have ignored the common distribution shift between the instances from original training data and webly collected data, affecting the reliability of the trained student network. To solve this problem, we propose a novel method dubbed "Knowledge Distillation between Different Distributions" (KD3), which consists of three components. Specifically, we first dynamically select useful training instances from the webly collected data according to the combined predictions of teacher network and student network. Subsequently, we align both the weighted features and classifier parameters of the two networks for knowledge memorization. Meanwhile, we also build a new contrastive learning block called MixDistribution to generate perturbed data with a new distribution for instance alignment, so that the student network can further learn a distribution-invariant representation. Intensive experiments on various benchmark datasets demonstrate that our proposed KD3 can outperform the state-of-the-art data-free knowledge distillation approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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