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advanced-computing-research-FACILITY, MATHEMATICS-AND-COMPUTER-SCIENCE-DIVISION, ARGONNE-NATIONAL-laboratory

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INTERNATIONAL JOURNAL OF SUPERCOMPUTER APPLICATIONS AND HIGH PERFORMANCE computing 1989年第4期3卷 6-8页

作者： DONGARRA, J ADVANCED COMPUTING RESEARCH FACILITY ARGONNE NATIONAL LABORATORY ARGONNE ILLINOIS 60439

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Polymorphic signal responses of phase-change quantum dot string upon stochastic resonance

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Nano research 2025年第2期18卷 717-724页

作者： Qin Wan Fei Zeng Ziao Lu Junwei Yu Feng Pan Key Laboratory of Advanced Materials(MOE) School of Materials Science and EngineeringTsinghua UniversityBeijing 100084China Center for Brain Inspired Computing Research(CBICR) Tsinghua UniversityBeijing 100084China

We successfully constructed phase-change quantum dots string(PCQDS)systems and studied their signal *** PCQDs actually is a cascaded structure consisted of several stochastic resonance(SR)two-state systems,in which inherent non-linearity,i.e.,phase-change of quantum dots(QDs),plays elementary and important roles to modulate signal *** established an SR model to simulate signal responses depending on stimulation *** know that some QDs will oscillate with input forcing frequency,while certain QDs will oscillate in their own frequency triggered by phase *** two effectscooperate togeneratepolymorphic response patterns,including action potential patterns exhibited by envelope of spike peak *** interesting and important simulation is that we replicate the memory effect in Nb-doped AINO,i.e.,a QDs dispersed *** result indicates that memory can occur in a system only constructed by volatile elementary units,implying memory existing in ***-term plasticity and spike-rate dependent plasticity can also be realized by using frequency and phase *** study provides a new scope to study signal handling and memory effect in quantum system.

关键词： quantum-dot string phase-change inherent nonlinearity signal modulation niobium oxide stochastic resonance

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A smart grid computational offloading policy generation method for end-edge-cloud environments

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Journal of Reliable Intelligent Environments 2025年第1期11卷 1-13页

作者： Liu, Weiwei Wang, Yue Xu, Chenxi Zheng, Min State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing102209 China

With the continuous progress of the Internet of Things (IoT) technology, smart grid has become the key to improve the efficiency of the power system. At present, how to reasonably distribute the massive power computing tasks under the premise of guaranteeing the quality of service is an urgent problem of the current smart grid. Therefore, this paper focuses on the smart grid computation task offloading decision problem based on the collaboration of edge computing and cloud computing. An end-edge-cloud task scheduling model with task execution delay, energy consumption and load balance as optimization objectives is proposed. In addition, the Dyna-Actor Critic (Dyna-AC) algorithm is proposed using Deep Reinforcement Learning (DRL), which integrates the advantages of model-based and model-free learning and optimizes the performance of this algorithm through heuristic sample selection and asynchronous parameter update strategies. Simulation results show that the algorithm is very effective in reducing power business execution delay, terminal energy consumption and improving system load. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Computation offloading

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RF-Sauron:Enabling Contact-Free Interaction on Eyeglass Using Conformal RFID Tag

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IEEE Internet of Things Journal 2025年第10期12卷 14320-14332页

作者： Yang, Baizhou Chen, Ling Peng, Xiaopeng Chen, Jiashen Tang, Yani Wang, Wei Fang, Dingyi Feng, Chao Northwest University Shaanxi Key Laboratory of Passive Internet of Things and Neural Computing School of Information Science and Technology Xi'an710127 China Northwest University Xi'an Key Laboratory of Advanced Computing and Software Security School of Information Science and Technology Xi'an710127 China Rochester Institute of Technology RochesterNY14623 United States Northwest University Xi'an Key Laboratory of Advanced Computing and System Security School of Information Science and Technology Xi'an710127 China Northwest University Shaanxi International Joint Research Centre for the Battery-Free Internet of Things School of Information Science and Technology Xi'an710127 China

Smart eyeglasses are emerging as a new medium for human-computer interaction. Existing solutions typically rely on cameras or touchpads, raising privacy invasion concerns or requiring users to physically interact with the glass frames. Here, we present, a novel mid-air gesture interaction system for eyeglasses, based on radio frequency identification (RFID). The design of our system involves embedding a conformal RFID tag into the eyeglass frame, where the received signals change with user gestures. We optimize the radiation gain of the tag to avoid view blockage while preserving a long working range. To discriminate different gestures, we propose an adaptively weighted multi-channel fusion network to extract their respective distinctive features. To allow efficient adaptation of the pre-trained network to new users, we also introduce a novel diagonal dot-product attention in our contrastive learning framework to uncover the feature similarities of different users. The proposed system was evaluated through extensive experiments, demonstrating an average recognition accuracy of 98.86% across twenty users and a cross-user accuracy of 98.75%. © 2014 IEEE.

关键词： Contrastive Learning

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Fairness-Aware Budgeted Edge Server Placement for Connected Autonomous Vehicles

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IEEE Transactions on Mobile computing 2025年第6期24卷 4762-4776页

作者： Wu, Jintao Xu, Xiaolong Cui, Guangming Zhang, Yiwen Qi, Lianyong Dou, Wanchun Cai, Zhipeng Nanjing University of Information Science & Technology Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services School of Software Nanjing210044 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Nanjing University of Information Science & Technology Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services School of Software China Nanjing University State Key Laboratory for Novel Software Technology China Anhui University School of Computer Science and Technology Anhui230031 China College of Computer Science and Technology Qingdao266580 China Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China Georgia State University Department of Computer Science United States

Mobile edge computing (MEC) considerably enhances the capabilities and performance of connected autonomous vehicles (CAVs) by deploying edge servers (ESs) on roadside units (RSUs) near CAVs, thereby ensuring low-latency services. Given the constrained and costly nature of ES resources (computing, storage, and bandwidth), equitable ES utilization is critical for CAV operations. However, fairness considerations are often overlooked in current budgeted edge server placement (ESP) strategies, potentially worsening resource imbalances and compromising user experience. This paper investigates the fairness-aware budgeted edge server placement (FESP) problem within RSUs, proving its NP-hardness. To address FESP, we first propose FESP-O, an integer programming-based optimal approach for small-scale problems, followed by FESP-APX, an approximation approach for large-scale scenarios that provides near-optimal solutions. We analyze the time complexity and approximation ratio of our proposed algorithms and validate their efficacy through experiments on real-world datasets. Extensive experimental results demonstrate significant performance improvements over baseline and state-of-the-art methods, indicating practical suitability and efficiency. © 2002-2012 IEEE.

关键词： Integer linear programming

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Towards practical data alignment in production federated learning

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Frontiers of Computer Science 2025年第1期19卷 167-169页

作者： Yexuan SHI Wei YU Yuanyuan ZHANG Chunbo XUE Yuxiang ZENG Zimu ZHOU Manxue GUO Lun XIN Wenjing NIE State Key Laboratory of Complex&Critical Software Environment and Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang UniversityBeijing 100191China Zhongguancun Pan Connected Mobile Communication Technology Innovation and Application Research Institute Beijing 100088China China Mobile Research Institute Beijing 100053China School of Data Science City University of Hong KongHong Kong 999077China

1 Introduction Federated learning has emerged as a promising par-adigm for collaborative model training that facilitates cooperation among multiple parties while ensuring data privacy[1].Successful alignment of data across parties is crucial for effective federated learning[2].This alignment involves harmonizing heterogeneous data from different parties to identify shared data for joint model *** set intersection(PSI)is a technique that allows the alignment of common entities between parties without revealing additional ***,efficiently performing data alignment with PSI in federated learning[3],especially when dealing with highly unbalanced data,remains challenging due to the low efficiency.

关键词： alignment dealing parties

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A Dual-Capture and Dual-Output 3D DNA Walker System Integrated with Ligases Enables Ultrasensitive Detection of Single-Nucleotide Polymorphisms

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Analytical Chemistry 2025年第17期97卷 9316-9325页

作者： Zhang, Yunshan Tan, Qianglong Yang, Fang Huang, Tuo Yu, Siyu Ye, Jing Zeng, Jianxian Feng, Xianzhong Zhang, Diming Key Laboratory of Soybean Molecular Design Breeding National Key Laboratory of Black Soils Conservation and Utilization Northeast Institute of Geography and Agroecology Chinese Academy of Sciences Changchun130102 China Research Center for Novel Computing Sensing and Intelligent Processing Zhejiang Laboratory Hangzhou311121 China Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Zhejiang Hangzhou310024 China School of Chemistry and Chemical Engineering Hunan University of Science and Technology Hunan Xiangtan411201 China

DNA walkers, as structurally and functionally programmable signal amplification tools, exhibit great potential for application in the field of biosensing. Traditional DNA walkers often rely on enzymes for operation, posing compatibility challenges, while the handful of existing enzyme-free DNA walkers demonstrate limited performance. To address this, we innovatively developed an efficient enzyme-free 3D DNA walker with dual capture and dual output capabilities. Coupled with ligase chain reaction (LCR), this system facilitates highly sensitive and specific detection of single nucleotide polymorphisms (SNPs). Specifically, LCR precisely identifies single-base mutations, effectively transmitting biological information. The 3D DNA walker system is based on entropy-driven circuit cycling reaction technology. In this system, LCR products serve as the driving strands for the DNA walker, independently binding to track strands and walking legs immobilized on gold nanoparticles, forming a unique dual signal capture mechanism. Each track strand carries two signal chains, significantly enhancing signal amplification efficiency. Benefiting from this novel enzyme-free 3D DNA walker strategy, our biosensing system exhibits exceptional sensitivity to mutant targets (MT), detecting MT at concentrations as low as 30.3 aM and distinguishing heterozygous samples with a 0.01% mutation frequency. Furthermore, this system has been successfully applied to genotyping and mutation abundance assessment of genomes from fresh soybean leaves, demonstrating its vast potential for practical applications. In summary, this research pioneers a novel enzyme-free 3D DNA walker with dual capture and dual output capabilities, and develops an ultrasensitive genotyping tool. This provides strong technical support for the advancement of genetic research. © 2025 American Chemical Society.

关键词： Nucleotides

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Retrieval-Augmented Generation Based Assistant: A Smart Home Case Study 23rd

Retrieval-Augmented Generation Based Assistant: A Smart Home...

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23rd EPIA Conference on Artificial Intelligence, EPIA 2024

作者： Ribeiro, Bruno Gomes, Luis Vale, Zita GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory Polytechnic of Porto Rua Dr. António Bernardino de Almeida 431 Porto4249-015 Portugal

ISBN: (纸本)9783031734991

Smart homes are starting to become more present in our daily lives. Smart homes are useful for enabling energy management services and reducing monetary costs of the consumers. Despite the increasing research in these aspects of the smart home, few actually have in mind the interactivity of the system they propose. Therefore, this paper, an extension of a previous work, proposes a new human-machine interface based on multi-agent systems and natural language models with retrieval-augmented generation techniques for smart homes to facilitate the consumers adaptation to this type of systems. The assistant enables the user to control the house and provides him with the information he needs, even when a problem within the system occurs. Through the experiment the assistant showed to be flexible, able to provide answers to questions outside of the smart home system domain and it increases the scalability of the system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Smart homes

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Benchmarking Large Language Models for Multi-agent Systems: A Comparative Analysis of AutoGen, CrewAI, and TaskWeaver 22nd

Benchmarking Large Language Models for Multi-agent Systems: ...

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22nd International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2024

作者： Barbarroxa, Rafael Gomes, Luis Vale, Zita GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI – Intelligent Systems Associate Laboratory Polytechnic of Porto R. Dr. António Bernardino de Almeida 431 Porto4249-015 Portugal

ISBN: (纸本)9783031704147

This paper presents the benchmarking of three multi-agent systems powered by large language models. The paper presents a comparative analysis of AutoGen, CrewAI, and TaskWeaver. Nowadays, large language models have emerged as powerful tools able to assist users in various areas. The integration of large language models into multi-agent systems increases their potential for collaborative problem-solving. This study focuses on a case study involving a machine learning code generation task which is used to evaluate the framework’s performance. To assess the performance of the solutions, it is requested to create energy forecasting models using the same dataset as the base. After producing the code, a new dataset is used to test the model performance using the root mean square error. The three solutions were able to provide results using multiple large language models. The best result was achieved by TaskWeaver using GPT-3.5, with an error of 25.04. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Multi agent systems

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CroPrompt: Cross-task Interactive Prompting for Zero-shot Spoken Language Understanding

CroPrompt: Cross-task Interactive Prompting for Zero-shot Sp...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Qin, Libo Wei, Fuxuan Chen, Qiguang Zhou, Jingxuan Huang, Shijue Si, Jiasheng Lu, Wenpeng Che, Wanxiang School of Computer Science and Engineering Central South University China Key Laboratory of Data Intelligence and Advanced Computing in Provincial Universities Soochow University China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology China Harbin Institute of Technology Shenzhen China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences China

ISBN: (纸本)9798350368741

Slot filling and intent detection are two highly correlated tasks in spoken language understanding (SLU). Recent SLU research attempts to explore zero-shot prompting techniques in large language models to alleviate the data scarcity problem. Nevertheless, the existing prompting work ignores the cross-task interaction information for SLU, which leads to sub-optimal performance. To solve this problem, we present the pioneering work of Cross-task Interactive Prompting (CroPrompt) for SLU, which enables the model to interactively leverage the information exchange across the correlated tasks in SLU. Additionally, we further introduce a multi-task self-consistency mechanism to mitigate the error propagation caused by the intent information injection. We conduct extensive experiments on the standard SLU benchmark and the results reveal that CroPrompt consistently outperforms the existing prompting approaches. In addition, the multi-task self-consistency mechanism can effectively ease the error propagation issue, thereby enhancing the performance. We hope this work can inspire more research on cross-task prompting for SLU. © 2025 IEEE.

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