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arXiv 2023年

作者： Jiang, Xiaorui Gao, Yu Xu, Hengwei Zhang, Qi Liao, Yong Zhou, Peng Yuan CCCD Key Lab of Ministry of Culture and Tourism University of Science and Technology of China Hefei China Department of Electrical and Computer Engineering Aarhus University Aarhus Denmark

Federated Learning (FL) allows several clients to cooperatively train machine learning models without disclosing the raw data. In practical applications, asynchronous FL (AFL) can address the straggler effect compared to synchronous FL. However, Non-IID data and stale models pose significant challenges to AFL, as they can diminish the practicality of the global model and even lead to training failures. In this work, we propose a novel AFL framework called Federated Historical Learning (FedHist), which effectively addresses the challenges posed by both Non-IID data and gradient staleness based on the concept of knowledge rumination. FedHist enhances the stability of local gradients by performing weighted fusion with historical global gradients cached on the server. Relying on hindsight, it assigns aggregation weights to each participant in a multi-dimensional manner during each communication round. To further enhance the efficiency and stability of the training process, we introduce an intelligent 2-norm amplification scheme, which dynamically regulates the learning progress based on the 2-norms of the submitted gradients. Extensive experiments indicate FedHist outperforms state-of-the-art methods in terms of convergence performance and test accuracy. Copyright © 2023, The Authors. All rights reserved.

关键词： Federated learning

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Mid-infrared Photons Released by NAD^(+)Reduction in the Tricarboxylic Acid Cycle of Myelinated Neuron

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Neuroscience Bulletin 2023年第7期39卷 1146-1150页

作者： Daoling Peng Wenpeng Qi Feng Zhang Bo Song Key Laboratory of Theoretical Chemistry of Environment Ministry of EducationSchool of EnvironmentSouth China Normal UniversityGuangzhou 510006China Shanghai Institute of Applied Physics Chinese Academy of SciencesShanghai 201800China Key Laboratory of Optical Technology and Instruments for Medicine Ministry of EducationShanghai Key Lab of Modern Optical SystemsSchool of Optical-Electrical Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China

Dear Editor,Life can utilize energy with high efficiency,especially with the help of aerobic respiration,where the tricarboxylic acid(TCA)cycle acts as an important metabolic engine for mitochondria to produce the cell energy currency of adenosine triphosphate(ATP),and nicotinamide adenine dinucleotide(NAD)plays a particular role of energy management molecule in the TCA cycle[1].

关键词： carboxylic utilize cycle

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Evolution-Assisted Deep Reinforcement Learning for Fast Charging Station Coordinated Operation 13

Evolution-Assisted Deep Reinforcement Learning for Fast Char...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Yang, Xiaoying Gu, Yujing Jia, Fuhua Li, Yiran Wang, Hongru Du, Nanjiang Cui, Tianxiang Ye, Yujian Bai, Ruibin State Grid Nantong Power Supply Company Nantong China University of Nottingham Ningbo China Department of Mechanical Materials and Manufacturing Engineering Ningbo China School of Electrical Engineering Southeast University Nanjing China School of Computer Science University of Nottingham Ningbo China Ningbo China University of Nottingham Ningbo China The Digital Port Technologies Lab Ningbo China Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute Ningbo China

ISBN: (纸本)9798350308365

The shift towards transportation electrification, marked by the rising use of electric vehicles (EVs) and the development of fast charging stations (FCS), plays a crucial role in transport decarbonization initiatives. To optimize the rollout of FCS and set appropriate charging service fees (CSF)-a process referred to as the coupled FCS multi-stage bi-level operation problem (FCS-MBOP)-is essential for improving both investment and operational efficiency within the integrated power distribution and transportation network (CPTN). For operators, it's not only necessary to adapt to short-term fluctuations within the environment but also to swiftly respond to changes in the FCS layout resulting from various long-term investment decisions. To address this complexity, we introduce a dual-timescale evolutionary assist deep reinforcement learning framework, which includes two specialized agents with distinct functions: an investment agent (planner) and an operational agent (operator). The planner focuses on annual investments, evolving long-term strategies that weigh social benefits against investment costs through the use of a genetic algorithm (GA). In contrast, the operator acts on an hourly basis, fine-tuning CSF to alleviate traffic congestion and minimize the social costs, while taking into account the planner's feasible investment decisions. Leveraging the integrated capabilities of a graph neural network (GNN), long-short-term memory (LSTM), and attention mechanisms, our framework's agents are adept at extracting both temporal and spatial features and facilitating the transfer of experiences across different investment stages. Empirical evidence underscores the effectiveness of our approach, showcasing its ability to surpass conventional methodologies in delivering high-quality solutions. © 2024 IEEE.

关键词： Deep reinforcement learning

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Fully implantable and battery-free wireless optoelectronic system for modulable cancer therapy and real-time monitoring

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npj Flexible Electronics 2023年第1期7卷 119-130页

作者： Kiho Kim In Sik Min Tae Hee Kim Do Hyeon Kim Seungwon Hwang Kyowon Kang Kyubeen Kim Sangun Park Jongmin Lee Young Uk Cho Jung Woo Lee Woon-Hong Yeo Young Min Song Youngmee Jung Ki Jun Yu Functional Bio-integrated Electronics and Energy Management Lab School of Electrical and Electronic EngineeringYonsei University50 Yonsei-roSeodaemun-guSeoul 03722Republic of Korea Center for Biomaterials Biomedical Research InstituteKorea Institute of Science and Technology(KIST)Seoul 02792Republic of Korea Department of Fusion Research and Collaboration Biomedical Research InstituteSeoul National University HospitalSeoul 03080Republic of Korea School of Electrical Engineering and Computer Science(EECS) Gwangju Institute of Science and Technology(GIST)Gwangju 61005Republic of Korea PA1 Foundry BusinessSamsung Electronics1 Samsungjeonja-roHwaseong-siGyeonggi-doRepublic of Korea R&D Center DNA Lab PharmaresearchSeongnam 13486Republic of Korea KU-KIST Graduate School of Converging Science and Technology Korea UniversitySeoul 136-705Republic of Korea School of Materials Science and Engineering Energy Materials for Soft Electronics LaboratoryPusan National UniversityBusan 46241Republic of Korea IEN Center for Human-Centric Interfaces and Engineering and George W.Woodruff School of Mechanical Engineering Georgia Institute of TechnologyAtlantaGA 30332USA Wallace H.Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University School of MedicineAtlantaGA 30332USA Institute for Robotics and Intelligent Machines Parker H.Petit Institute for Bioengineering and BiosciencesInstitute for MaterialsNeural Engineering CenterGeorgia Institute of TechnologyAtlantaGA 30332USA School of Electrical and Electronic Engineering YU-Korea Institute of Science and Technology(KIST)InstituteYonsei University50Yonsei-roSeodaemun-guSeoul 03722Republic of Korea

Photodynamic therapy(PDT)is attracting attention as a next-generation cancer treatment that can selectively destroy malignant tissues,exhibit fewer side effects,and lack pain during *** PDT systems have recently been developed to resolve the issues of bulky and expensive conventional PDT systems and to implement continuous and repetitive *** implantable PDT systems,however,are not able to perform multiple functions simultaneously,such as modulating light intensity,measuring,and transmitting tumor-related data,resulting in the complexity of cancer ***,we introduce a flexible and fully implantable wireless optoelectronic system capable of continuous and effective cancer treatment by fusing PDT and hyperthermia and enabling tumor size monitoring in *** system exploits micro inorganic light-emitting diodes(μ-LED)that emit light with a wavelength of 624 nm,designed not to affect surrounding normal tissues by utilizing a fully programmable light intensity ofμ-LED and precisely monitoring the tumor size by Si phototransistor during a long-term implantation(2–3 weeks).The superiority of simultaneous cancer treatment and tumor size monitoring capabilities of our system operated by wireless power and data transmissions with a cell phone was confirmed through in vitro experiments,ray-tracing simulation results,and a tumor xenograft mouse model in *** all-in-one single system for cancer treatment offers opportunities to not only enable effective treatment of tumors located deep in the tissue but also enable precise and continuous monitoring of tumor size in real-time.

关键词： optoelectronic enable battery

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Highsimb: A Concrete Blockchain High Simulation with Contract Vulnerability Detection for Ethereum and Hyperledger Fabric 4th

Highsimb: A Concrete Blockchain High Simulation with Contra...

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Huang, Pengfei Jie, Wanqing Voundi Koe, Arthur Sandor Hou, Ruitao Yan, Hongyang Nouioua, Mourad Thien, Phan Duc Mbous Ikong, Jacques Lancine, Camara Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou510006 China Pazhou Lab Guangzhou510330 China Faculty of Mathematics and Computer Science University of Mohamed Bachir El Ibrahimi Bordj Bou Arreridj34030 Algeria Faculty of Information Technology Nam Dinh University of Technology Education Nam Dinh420000 Viet Nam Department of Electrical and Telecommunications Engineering National Advanced School of Engineering - University of Yaounde I Yaounde337 Cameroon Department of Computer Science University of Social Sciences and Management Bamako2575 Mali

ISBN: (纸本)9783031200984

Blockchain testing plays a critical role in the maturation of blockchain technology by ensuring the quality of implemented functional and non-functional requirements. In the new global economy, rapid time to market has become a central issue: developers fail to scrutinize their blockchain designs prior to deployment and customers undergo negative experiences that hurt the widespread adoption of the blockchain technology. Previous published studies aimed for effective blockchain simulators. However, existing solutions exhibit several drawbacks: they rely on guesswork, conceal low-level implementation details, lack expected realistic outcomes and automated testing, as well as lag in smart contract vulnerability analysis. In this paper, we introduce highsimb: the first concrete blockchain high simulation platform for Ethereum and Hyperledger Fabric that supports smart contract vulnerability detection. Unlike a testnet, the blockchain tester can customize any low-level detail to achieve realistic expected results under automated testing. Theoretical analysis demonstrates our concrete simulator is highly observable, supports realistic feedback, is scalable, detects smart contract vulnerabilities, has strong white-box testing capabilities and automates experiments. Our framework complements existing blockchain simulators and introduces a novel development paradigm for blockchain testing. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Enhancing Sovereign Allocation of Resources in cloud Milieus Using a Causal Dilated Geometric Algebra Approach for Dynamic Scalability 2

Enhancing Sovereign Allocation of Resources in Cloud Milieus...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Katru, Chandrasekhar Rao Srinivasan, Sathish Kumar, Mohan Yadwad, Sunita Satish, G. Maranan, Ramya Lead Software Engineer Indian LandSC29707 United States Machine Learning & AI Development Oracle Cloud Infrastructure Division San FranciscoCA United States Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Karnataka Bengaluru India Parul institute of Engineering and Technology Parul University Computer science and Engineering Faculty of Engineering and Technology Gujarat India Sree Dattha Institute of Engineering and Science Department of Electrical and Electronics Engineering Telangana Sheriguda India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798331505745

cloud environments utilize availability and usage of resources as crucial components in order to produce scalability, reliability and low cost. Such resource capabilities require autonomous systems that can handle these resources dynamically in spite of fluctuating workloads and service delivery requirements. Static models of resource management suffer from scalability and inability to react on the fly and accurately predict the demand thus causing Service Level Agreement (SLA) breaches and affecting the Quality of Service (QoS) as well as energy consumption profile. Advanced and elaborative cloud workloads require an intelligent system that can analyse the temporal and spatial patterns without impacting the overall system's performance significantly. Inspired by these challenges, this research proposes a Causal Dilated Geometric Algebra Convolutional Transformer Network (CATNet) along with the Prairie-Dog Optimizer (P-DogO), referred to as CAT-DogNet to learn and predict the dynamic scalability. CATNet integrates Causal Dilated Convolutional Networks in time domain for temporal structure capturing and Geometric Algebra Transformer for spatial resource coding. P-DogO adjusts weight parameters and losses for resource allocation, enhancing them depending on a work environment. A set of comprehensive assessments demonstrate a 0.85% SLA violation rate, average response time of less than 10ms, and overall 12.3% less energy consumption than baseline methods. The system had favourable predictability rates over 99.4% for the resource demand estimate. CAT-DogN et presents a sound and agnostic architecture for autonomous resource allocation in cloud environment, which indeed extend the state of art by considering flexibility, power consumption and QoS compliance. © 2025 IEEE.

关键词： Infrastructure as a service (IaaS)

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How Far Have Edge clouds Gone? A Spatial-Temporal Analysis of Edge Network Latency In the Wild

How Far Have Edge Clouds Gone? A Spatial-Temporal Analysis o...

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International Workshop on Quality of Service

作者： Heng Zhang Shaoyuan Huang Mengwei Xu Deke Guo Xiaofei Wang Victor C.M. Leung Wenyu Wang College of Intelligence and Computing Tianjin University Tianjin China Beijing University of Posts and Telecommunications Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada Paiou Cloud Computing (Shanghai) Co. Ltd Shanghai China

The emergence of next-generation latency-critical applications places strict requirements on network latency and stability. Edge cloud, an instantiated paradigm for edge computing, is gaining more and more attention due to its benefits of low latency. In this work, we make an in-depth investigation into the network QoS, especially end-to-end latency, at both spatial and temporal dimensions on a nationwide edge computing platform. Through the measurements, we collect a multi-variable large-scale real-world dataset on latency. We then quantify how the spatial-temporal factors affect the end-to-end latency, and verified the predictability of end-to-end latency. The results reveal the limitation of centralized clouds and illustrate how could edge clouds provide low and stable latency. Our results also point out that existing edge clouds merely increase the density of servers and ignore spatial-temporal factors, so they still suffer from high latency and fluctuations. Based on the observations, we propose a robust prototype edge cloud model based on lessons we learn from the measurement and evaluate its performance in the production environment. The further evaluation result shows that edge clouds achieve 84.1% latency reduction with 0.5ms latency fluctuation and 73.3% QoS improvement compared with the centralized clouds.

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Spin-UP: Spin Light for Natural Light Uncalibrated Photometric Stereo

arXiv

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arXiv 2024年

作者： Li, Zongrui Lu, Zhan Yan, Haojie Shi, Boxin Pan, Gang Zheng, Qian Jiang, Xudong Lab Interdisciplinary Graduate Programme Nanyang Technological University Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore College of Computer Science and Technology Zhejiang University China The State Key Lab of Brain-Machine Intelligence Zhejiang University China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China National Engineering Research Center of Visual Technology School of Computer Science Peking University China

Natural Light Uncalibrated Photometric Stereo (NaUPS) relieves the strict environment and light assumptions in classical Uncalibrated Photometric Stereo (UPS) methods. However, due to the intrinsic ill-posedness and high-dimensional ambiguities, addressing NaUPS is still an open question. Existing works impose strong assumptions on the environment lights and objects' material, restricting the effectiveness in more general scenarios. Alternatively, some methods leverage supervised learning with intricate models while lacking interpretability, resulting in a biased estimation. In this work, we propose Spin Light Uncalibrated Photometric Stereo (Spin-UP), an unsupervised method to tackle NaUPS in various environment lights and objects. The proposed method uses a novel setup that captures the object's images on a rotatable platform, which mitigates NaUPS's ill-posedness by reducing unknowns and provides reliable priors to alleviate NaUPS's ambiguities. Leveraging neural inverse rendering and the proposed training strategies, Spin-UP recovers surface normals, environment light, and isotropic reflectance under complex natural light with low computational cost. Experiments have shown that Spin-UP outperforms other supervised/unsupervised NaUPS methods and achieves state-of-the-art performance on synthetic and real-world datasets. Codes and data are available at https://***/LMozart/CVPR2024-SpinUP. Copyright © 2024, The Authors. All rights reserved.

关键词： Photometry

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Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation

arXiv

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arXiv 2025年

作者： Yu, Yi Xia, Song Lin, Xun Kong, Chenqi Yang, Wenhan Lu, Shijian Tan, Yap-Peng Kot, Alex C. Lab Interdisciplinary Graduate Programme Nanyang Technological University Singapore PengCheng Laboratory Shenzhen China School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Computer Science and Engineering Beihang University Beijing China College of Computing and Data Science Nanyang Technological University Singapore

Adversarial examples, characterized by imperceptible perturbations, pose significant threats to deep neural networks by misleading their predictions. A critical aspect of these examples is their transferability, allowing them to deceive unseen models in black-box scenarios. Despite the widespread exploration of defense methods, including those on transferability, they show limitations: inefficient deployment, ineffective defense, and degraded performance on clean images. In this work, we introduce a novel training paradigm aimed at enhancing robustness against transferable adversarial examples (TAEs) in a more efficient and effective way. We propose a model that exhibits random guessing behavior when presented with clean data x as input, and generates accurate predictions when with triggered data x + τ. Importantly, the trigger τ remains constant for all data instances. We refer to these models as models with trigger activation. We are surprised to find that these models exhibit certain robustness against TAEs. Through the consideration of first-order gradients, we provide a theoretical analysis of this robustness. Moreover, through the joint optimization of the learnable trigger and the model, we achieve improved robustness to transferable attacks. Extensive experiments conducted across diverse datasets, evaluating a variety of attacking methods, underscore the effectiveness and superiority of our approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep neural networks

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Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report 17th

Efficient Single-Image Depth Estimation on Mobile Devices, ...

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17th European Conference on computer Vision, ECCV 2022

作者： Ignatov, Andrey Malivenko, Grigory Timofte, Radu Treszczotko, Lukasz Chang, Xin Ksiazek, Piotr Lopuszynski, Michal Pioro, Maciej Rudnicki, Rafal Smyl, Maciej Ma, Yujie Li, Zhenyu Chen, Zehui Xu, Jialei Liu, Xianming Jiang, Junjun Shi, XueChao Xu, Difan Li, Yanan Wang, Xiaotao Lei, Lei Zhang, Ziyu Wang, Yicheng Huang, Zilong Luo, Guozhong Yu, Gang Fu, Bin Li, Jiaqi Wang, Yiran Huang, Zihao Cao, Zhiguo Conde, Marcos V. Sapozhnikov, Denis Lee, Byeong Hyun Park, Dongwon Hong, Seongmin Lee, Joonhee Lee, Seunggyu Chun, Se Young Computer Vision Lab ETH Zürich Zürich Switzerland AI Witchlabs Zollikerberg Switzerland University of Wuerzburg Wuerzburg Germany TCL Research Europe Warsaw Poland Harbin Institute of Technology Harbin China Xiaomi Inc. Beijing China Tencent GY-Lab Shenzhen China National Key Laboratory of Science and Technology on Multi-Spectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of

ISBN: (纸本)9783031250651

Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks. Thus, it is very crucial to have efficient and accurate depth estimation models that can run fast on low-power mobile chipsets. In this Mobile AI challenge, the target was to develop deep learning-based single image depth estimation solutions that can show a real-time performance on IoT platforms and smartphones. For this, the participants used a large-scale RGB-to-depth dataset that was collected with the ZED stereo camera capable to generated depth maps for objects located at up to 50 m. The runtime of all models was evaluated on the Raspberry Pi 4 platform, where the developed solutions were able to generate VGA resolution depth maps at up to 27 FPS while achieving high fidelity results. All models developed in the challenge are also compatible with any Android or Linux-based mobile devices, their detailed description is provided in this paper. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image segmentation

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