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arXiv 2024年

作者： Islam, Tanzima Z. Marathe, Aniruddha Schutte, Holland Zaeed, Mohammad The Department of Computer Science Texas State University San MarcosTX78666 United States The Lawrence Livermore National Laboratory LivermoreCA7000 United States The Western Washington University BellinghamWA United States The Department of Computer Science Texas State University United States

With heterogeneous systems, the number of GPUs per chip increases to providecomputational capabilities for solving science at a nanoscopic scale. However,low utilization for single GPUs defies the need to invest more money forexpensive ccelerators. While related work develops optimizations for improvingapplication performance, none studies how these optimizations impact hardwareresource usage or the average GPU utilization. This paper takes a data-drivenanalysis approach in addressing this gap by (1) characterizing how hardwareresource usage affects device utilization, execution time, or both, (2)presenting a multi-objective metric to identify important application-deviceinteractions that can be optimized to improve device utilization andapplication performance jointly, (3) studying hardware resource usage behaviorsof several optimizations for a benchmark application, and finally (4)identifying optimization opportunities for several scientific proxyapplications based on their hardware resource usage behaviors. Furthermore, wedemonstrate the applicability of our methodology by applying the identifiedoptimizations to a proxy application, which improves the execution time, deviceutilization and power consumption by up to 29.6%, 5.3% and 26.5% respectively. © 2024, CC BY.

关键词： Program processors

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Forecasting-Aided Graphical Learning for Robust State Estimation of Distribution System 1

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8th Purple Mountain Forum on Smart Grid Protection and Control, PMF 2023

作者： Di, Cao Zhao, Junbo Hu, Jiaxiang Huang, Yuehui Huang, Qi Chen, Zhe Hu, Weihao University of Electronic Science and Technology of China Chengdu China Department of Electrical and Computer Engineering University of Connecticut StorrsCT06269 United States State Key Laboratory of Operation and Control of Renewable Energy and Storage Systems China Electric Power Research Institute Beijing China Department of Energy Technology Aalborg University Pontoppidanstraede 111 Aalborg Denmark

ISBN: (数字)9789819992515

ISBN: (纸本)9789819992508

The forecasting of of pseudo-measurements play an important role in distribution system state estimation (DSSE). This paper proposes robust DSSE method based on forecasting-aided graphical learning method. The nodal power consumption models are first built to produce pseudo-measurements based on deep neural network. Then, the pseudo-measurements and real-time measurements are represented as a graph according to the topology of the distribution network, which are further processed by a graph attention network to capture the mapping relationship between the graphical measurements and the state variables based on the error modeling of pseudo-measurements. The robustness against anomalous measurements is achieved through the embedding of structural information of DN. The modeling of pseudo-measurements further enhance its robustness by guiding the formulation of edge weights of the graph neural network. Comparative tests are carried out on a IEEE 119-node system to demonstrate the effectiveness and robustness of the proposed method. © 2024, State Grid Electric Power.

关键词： Electric power distribution

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Flow-aware synthesis: A generic motion model for video frame interpolation

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Computational Visual Media 2021年第3期7卷 393-405页

作者： Jinbo Xing Wenbo Hu Yuechen Zhang Tien-Tsin Wong Department of Computer Science and Engineering the Chinese University of Hong KongHong Kong SARChina Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesChina

A popular and challenging task in video research,frame interpolation aims to increase the frame rate of *** existing methods employ a fixed motion model,e.g.,linear,quadratic,or cubic,to estimate the intermediate warping ***,such fixed motion models cannot well represent the complicated non-linear motions in the real world or rendered ***,we present an adaptive flow prediction module to better approximate the complex motions in ***,interpolating just one intermediate frame between consecutive input frames may be insufficient for complicated non-linear *** enable multi-frame interpolation,we introduce the time as a control variable when interpolating frames between original ones in our generic adaptive flow prediction *** and quantitative experimental results show that our method can produce high-quality results and outperforms the existing stateof-the-art methods on popular public datasets.

关键词： flow-aware generic motion model video frame interpolation

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Variable Speed Limit Control for Highway Scenarios a Multi-agent Reinforcement Learning Based Approach 2nd

Variable Speed Limit Control for Highway Scenarios a Multi...

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2nd International Conference on Cognitive Mobility, COGMOB 2023

作者： Kővári, Bálint Knáb, István Gellért Bécsi, Tamás Department of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics Budapest Hungary Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

ISBN: (纸本)9783031876196

Modern road networks are critical in developing transportation infrastructures from the aspect of sustainability, thanks to the rapid increase in road users. The demand for mobility makes the existing infrastructure more crowded, boosting greenhouse gas emissions and delays in everyday commuting. Expanding the road network is only possible in some cases and is also not feasible, but Intelligent Transportation Systems (ITS) can enhance the efficiency of the existing transportation network. From a management point of view, a proven algorithm is Variable Speed Limit Control, which regulates the state of certain sectors by spatially distributing traffic in the form of dynamically varying speed limits. Combining this with a state-of-the-art predictive solution can make a big difference to performance. For the design of speed limits, this paper proposes an approach where deep reinforcement learning with the smallest industrial share not only resolves moving jams that arise during congested traffic situations, but also prevents them, thereby avoiding cumulative error from transients, all by abandoning physical equations and identified models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Highway traffic control

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Efficient learning of robust quadruped bounding usingpretrained neural networks

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IET Cyber-Systems and Robotics 2022年第4期4卷 331-338页

作者： Zhicheng Wang Anqiao Li Yixiao Zheng Anhuan Xie Zhibin Li Jun Wu Qiuguo Zhu Institute of Cyber-Systems and Control Zhejiang UniversityZhejiangChina Intelligent Robot Research Center Zhejiang LabZhejiangChina Department of Computer Science University College LondonLondonUK State Key Laboratory of Industrial Control Technology Zhejiang UniversityZhejiangChina

Bounding is one of the important gaits in quadrupedal locomotion for negotiating *** authors proposed an effective approach that can learn robust bounding gaits more efficiently despite its large variation in dynamic body *** authors first pretrained the neural network(NN)based on data from a robot operated by conventional model-based controllers,and then further optimised the pretrained NN via deep reinforcement learning(DRL).In particular,the authors designed a reward function considering contact points and phases to enforce the gait symmetry and periodicity,which improved the bounding *** NN-based feedback controller was learned in the simulation and directly deployed on the real quadruped robot Jueying Mini successfully.A variety of environments are presented both indoors and outdoors with the authors’*** authors’approach shows efficient computing and good locomotion results by the Jueying Mini quadrupedal robot bounding over uneven *** cover image is based on the Research Article Efficient learning of robust quadruped bounding using pretrained neural networks by Zhicheng Wang et al.,https://***/10.1049/csy2.12062.

关键词： legged locomotion reinforcement learning robot learning

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Predicting Student Success in Mathematics Using Machine Learning Techniques: A Comparative Study of Algorithms

Predicting Student Success in Mathematics Using Machine Lear...

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Innovative and Intelligent Information Technologies (IC3IT), International Conference on

作者： Zouaoui Samia Zouaoui Nour El Houda Mestour Hareth Computer Science Department University of Batna 2 Batna Algeria LINFI Laboratory of Biskra University Mathematics Department Laboratory of Applied Mathematics University of Biskra Algeria Mathematics Department University of Batna 2 Batna Algeria

ISBN: (数字)9798331541064

ISBN: (纸本)9798331541071

Achieving low results in mathematics is a real and urgent concern, as mathematics is the foundation of science and technology, and any weakness in it reflects negatively on the progress of society in various ways. This study evaluates how different mathematics study methods affect students educational success and achievement. We employed three different machine learning models to analyze data related to mathematics study techniques and student outcomes. These methods included: support vector machine (SVM), A K-nearest neighbors (KNN), and Linear Regression (LR). The results showed that KNN provided the best accuracy rate with 97.6% and an F1-score of 96.7%. These results highlight the significant contribution of forming study groups and trying to cooperate with each other, this factor is considered important and influential in the success of students in mathematics.

关键词： Support vector machines Logistic regression Machine learning algorithms Education Machine learning Nearest neighbor methods Predictive models Prediction algorithms Mathematical models Object recognition

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GNSS Interference Classification Using Federated Reservoir Computing 24

GNSS Interference Classification Using Federated Reservoir C...

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24th IEEE International Conference on Communication Technology, ICCT 2024

作者： Ye, Ziqiang Gao, Yulan Liu, Xinyue Xiao, Yue Xiao, Ming Zammit, Saviour University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Kth Royal Institute of Technology Division of Information Science and Engineering Stockholm100 44 Sweden Southwest Jiaotong University SWJTU-Leeds Joint School Chengdu611756 China University of Malta Department of Communications and Computer Engineering MsidaMSD 2080 Malta

ISBN: (纸本)9798350363760

The expanding use of Unmanned Aerial Vehicles (UAVs) in vital areas like traffic management, surveillance, and environmental monitoring highlights the need for robust communication and navigation systems. Particularly vulnerable are Global Navigation Satellite Systems (GNSS), which face a spectrum of interference and jamming threats that can significantly undermine their performance. While traditional deep learning approaches are adept at mitigating these issues, they often fall short for UAV applications due to significant computational demands and the complexities of managing large, centralized datasets. In response, this paper introduces Federated Reservoir Computing (FedRC) as a potent and efficient solution tailored to enhance interference classification in GNSS systems used by UAVs. Our experimental results demonstrate that FedRC not only achieves faster convergence but also sustains lower loss levels than traditional models, highlighting its exceptional adaptability and operational efficiency. © 2024 IEEE.

关键词： Federated learning

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Localized Zeroth-Order Prompt Optimization

arXiv

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arXiv 2024年

作者： Hu, Wenyang Shu, Yao Yu, Zongmin Wu, Zhaoxuan Lin, Xiaoqiang Dai, Zhongxiang Ng, See-Kiong Low, Bryan Kian Hsiang Department of Computer Science National University of Singapore Singapore Institute of Data Science National University of Singapore Singapore China Laboratory for Information and Decision Systems MIT United States

The efficacy of large language models (LLMs) in understanding and generating natural language has aroused a wide interest in developing prompt-based methods to harness the power of black-box LLMs. Existing methodologies usually prioritize a global optimization for finding the global optimum, which however will perform poorly in certain tasks. This thus motivates us to re-think the necessity of finding a global optimum in prompt optimization. To answer this, we conduct a thorough empirical study on prompt optimization and draw two major insights. Contrasting with the rarity of global optimum, local optima are usually prevalent and well-performed, which can be more worthwhile for efficient prompt optimization (Insight I). The choice of the input domain, covering both the generation and the representation of prompts, affects the identification of well-performing local optima (Insight II). Inspired by these insights, we propose a novel algorithm, namely localized zeroth-order prompt optimization (ZOPO), which incorporates a Neural Tangent Kernel-based derived Gaussian process into standard zeroth-order optimization for an efficient search of well-performing local optima in prompt optimization. Remarkably, ZOPO outperforms existing baselines in terms of both the optimization performance and the query efficiency, which we demonstrate through extensive experiments. © 2024, CC BY.

关键词： Global optimization

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Highly ordered inkjet-printed quantum-dot thin films enable efficient and stable QLEDs with EQE exceeding 23%

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escience 2024年第3期4卷 152-162页

作者： Changting Wei Bo Xu Meng Zhang Zhenhuang Su Jiawei Gu Wenrui Guo Xingyu Gao Wenming Su Zheng Cui Seokwoo Jeon Zhiyong Fan Haibo Zeng MIIT Key Laboratory of Advanced Display Materials and Devices Institute of Optoelectronics&NanomaterialsSchool of Materials Science and EngineeringNanjing University of Science and TechnologyNanjing 210094China Ningbo Nato New Material Technology Co. LtdNingbo 315600China Shanghai Synchrotron Radiation Facility(SSRF) Zhangjiang LaboratoryShanghai Advanced Research InstituteChinese Academy of SciencesShanghai 201204China School of Mechanical Engineering Jiangsu UniversityZhenjiang 212013China Printable Electronics Research Center Nano Devices and Materials DivisionSuzhou Institute of Nano-Tech and Nano-BionicsChinese Academy of SciencesSuzhou 215123China Department of Materials Science and Engineering Korea UniversitySeoul 02841South Korea Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyHong Kong 999077China

Inkjet-printed quantum dot light-emitting diodes(QLEDs)are emerging as a promising technology for next-generation ***,the progress in fabricating QLEDs using inkjet printing technique has been slower compared to spin-coated devices,particularly in terms of efficiency and *** key to achieving high performance QLEDs lies in creating a highly ordered and uniform inkjet-printed quantum dot(QD)thin *** this study,we present a highly effective strategy to significantly improve the quality of inkjet-printed CdZnSe/CdZnS/ZnS QD thin films through a pressure-assisted thermal annealing(PTA)*** from this PTA process,a high quality QD thin film with ordered packing,low surface roughness,high photoluminescence and excellent electrical property is *** mechanism behind the PTA process and its profound impact on device performance have been thoroughly investigated and ***,a record high external quantum efficiency(EQE)of 23.08%with an impressive operational lifetime(T50)of up to 343,342h@100cdm−2,and a record EQE of 22.43%with T50 exceeding to 1,500,463h@100cdm−2 are achieved in inkjet-printed red and green CdZnSe-based QLEDs,*** work highlights the PTA process as an important approach to realize highly efficient and stable inkjet-printed QLEDs,thus advancing QLED technology to practical applications.

关键词： Inkjet printing Quantum dot light-emitting diode High efficiency Thermal annealing Highly ordered

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Optimal Linear Deception Attacks on Remote State Estimation with Constrained Alarm Rates: A Low-Dimensional Case 63

Optimal Linear Deception Attacks on Remote State Estimation ...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Shang, Jun Zhang, Hanwen Zhou, Jing Chen, Tongwen Tongji University Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Department of Control Science and Engineering Shanghai200092 China University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering Beijing100083 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

ISBN: (纸本)9798350316339

This study addresses linear attacks on remote state estimation within the context of a constrained alarm rate. Smart sensors, which are equipped with local Kalman filters, transmit innovations instead of raw measurements through a wireless communication network. This transmission is vulnerable to malicious data interception and manipulation by attackers. The aim of this research is to identify the optimal attack strategy that degrades the system performance while adhering to stealthiness constraints. A notable innovation of this paper is the direct association of the attack's stealthiness with the alarm rate, diverging from traditional approaches that rely on the covariance of the innovation or the Kullback-Leibler divergence, which are conventional metrics that have been extensively explored in previous studies. Our findings reveal that the optimal attack strategy exhibits some structural characteristics in systems of low dimensions. The performance of the proposed attack strategy is demonstrated through numerical examples. © 2024 IEEE.

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