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arXiv 2023年

作者： Nobili, Alberto Maria Qin, Yuzhen Avizzano, Carlo Alberto Bassett, Danielle S. Pasqualetti, Fabio The Department of Mechanical Engineering University of California Riverside United States The Perceptual Robotics Laboratory The IIM Institute Department of Excellence in Robotics and A.I. Scuola Superiore Sant’Anna Pisa Italy The Department of Bioengineering The Department of Electrical & Systems Engineering The Department of Physics & Astronomy The Department of Psychiatry The Department of Neurology University of Pennsylvania The Santa Fe Institute United States Collaborative Research Network Chevy ChaseMD United States

Many natural and man-made network systems need to maintain certain patterns, such as working at equilibria or limit cycles, to function properly. Thus, the ability to stabilize such patterns is crucial. Most of the existing studies on stabilization assume that network systems’ states can be measured online so that feedback control strategies can be used. However, in many real-world scenarios, systems’ states, e.g., neuronal activity in the brain, are often difficult to measure. In this paper, we take this situation into account and study the stabilization problem of linear network systems with an open-loop control strategy—vibrational control. We derive a graph-theoretic sufficient condition for structural vibrational stabilizability, under which network systems can always be stabilized. We further provide an approach to select the locations in the network for control placement and design corresponding vibrational inputs to stabilize systems that satisfy this condition. Finally, we provide some numerical results that demonstrate the validity of our theoretical findings. Copyright © 2023, The Authors. All rights reserved.

关键词： Linear networks

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Topological Landau–Zener nanophotonic circuits

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Advanced Photonics 2023年第3期5卷 127-134页

作者： Bing-Cong Xu Bi-Ye Xie Li-Hua Xu Ming Deng Weijin Chen Heng Wei Fengliang Dong Jian Wang Cheng-Wei Qiu Shuang Zhang Lin Chen Huazhong University of Science and Technology School of Optical and Electronic InformationWuhan National Laboratory for OptoelectronicsWuhanChina The Chinese University of Hong Kong School of Science and EngineeringShenzhenChina CAS Center for Excellence in Nanoscience National Center for Nanoscience and Technology of ChinaNanofabrication LaboratoryCAS Key Laboratory for Nanosystems and Hierarchical FabricationCAS Key Laboratory for Nanophotonic Materials and DevicesBeijingChina National University of Singapore Department of Electrical and Computer EngineeringSingapore University of Chinese Academy of Sciences Center of Materials Science and Optoelectronics EngineeringBeijingChina The University of Hong Kong Department of PhysicsHong KongChina Peking University School of PhysicsState Key Laboratory for Mesoscopic PhysicsBeijingChina

Topological edge states(TESs),arising from topologically nontrivial phases,provide a powerful toolkit for the architecture design of photonic integrated circuits,since they are highly robust and strongly localized at the boundaries of topological *** is highly desirable to be able to control TES transport in photonic *** the coupling between the TESs in a finite-size optical lattice is capable of exchanging light energy between the boundaries of a topological lattice,hence facilitating the flexible control of TES ***,existing strategies have paid little attention to enhancing the coupling effects between the TESs through the finite-size ***,we establish a bridge linking the interaction between the TESs in a finite-size optical lattice using the Landau–Zener model so as to provide an alternative way to modulate/control the transport of topological *** experimentally demonstrate an edge-to-edge topological transport with high efficiency at telecommunication wavelengths in silicon waveguide *** results may power up various potential applications for integrated topological photonics.

关键词： topological edge states finite-size effect Landau-Zener model edge-to-edge topological transport nanophotonic integrated circuits

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PCART: Automated Repair of Python API Parameter Compatibility Issues

arXiv

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arXiv 2024年

作者： Zhang, Shuai Xiao, Guanping Wang, Jun Lei, Huashan He, Gangqiang Liu, Yepang Zheng, Zheng The College of Computer Science and Technology The Key Laboratory for Safety-critical Software Development and Verification Nanjing University of Aeronautics and Astronautics Nanjing China The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China The School of Automation Science and Electrical Engineering Beihang University Beijing China

In modern software development, Python third-party libraries play a critical role, especially in fields like deep learning and scientific computing. However, API parameters in these libraries often change during evolution, leading to compatibility issues for client applications reliant on specific versions. Python’s flexible parameter-passing mechanism further complicates this, as different passing methods can result in different API compatibility. Currently, no tool can automatically detect and repair Python API parameter compatibility issues. To fill this gap, we introduce PCART, the first solution to fully automate the process of API extraction, code instrumentation, API mapping establishment, compatibility assessment, repair, and validation. PCART handles various types of Python API parameter compatibility issues, including parameter addition, removal, renaming, reordering, and the conversion of positional to keyword parameters. To evaluate PCART, we construct PCBENCH, a large-scale benchmark comprising 47,478 test cases mutated from 844 parameter-changed APIs across 33 popular Python libraries. Evaluation results demonstrate that PCART is both effective and efficient, significantly outperforming existing tools (MLCatchUp and Relancer) and the large language model ChatGPT (GPT-4o), achieving an F1-score of 96.49% in detecting API parameter compatibility issues and a repair precision of 92.26%. Further evaluation on 30 real-world Python projects from GitHub confirms PCART’s practicality. We believe PCART can significantly reduce the time programmers spend maintaining Python API updates and advance the automation of Python API compatibility issue repair. Copyright © 2024, The Authors. All rights reserved.

关键词： Python

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Demonstrating the Potential of Adaptive LMS Filtering on FPGA-Based Qubit Control Platforms for Improved Qubit Readout in 2D and 3D Quantum Processing Units

arXiv

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arXiv 2024年

作者： Johnson, Hans Bornman, Nicholas Kim, Taeyoon Van Zanten, David Zorzetti, Silvia Saniie, Jafar Research Laboratory Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States Center Fermi National Accelerator Laboratory BataviaIL60510 United States Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States

Advancements in quantum computing underscore the critical need for sophisticated qubit readout techniques to accurately discern quantum states. This abstract presents our research intended for optimizing readout pulse fidelity for 2D and 3D Quantum Processing Units (QPUs), the latter coupled with Superconducting Radio Frequency (SRF) cavities. Focusing specifically on the application of the Least Mean Squares (LMS) adaptive filtering algorithm, we explore its integration into the FPGA-based control systems to enhance the accuracy and efficiency of qubit state detection by improving Signal-to-Noise Ratio (SNR). Implementing the LMS algorithm on the Zynq UltraScale+ RFSoC Gen 3 devices (RFSoC 4x2 FPGA and ZCU216 FPGA) using the Quantum Instrumentation Control Kit (QICK) open-source platform, we aim to dynamically test and adjust the filtering parameters in real-time to characterize and adapt to the noise profile presented in quantum computing readout signals. Our preliminary results demonstrate the LMS filter's capability to maintain high readout accuracy while efficiently managing FPGA resources. These findings are expected to contribute to developing more reliable and scalable quantum computing architectures, highlighting the pivotal role of adaptive signal processing in quantum technology advancements. © 2024, CC BY.

关键词： Adaptive filtering

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An Unequal Error Protection-based Coded Transmission for Federated Learning

An Unequal Error Protection-based Coded Transmission for Fed...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Qingya Lu Rongchi Xu Chang Liu Shuangyang Li Baoming Bai State Key Laboratory of ISN Xidian University Xi’an China Department of Electrical and Electronic Engineering Hong Kong Polytechnic University Hong Kong SAR China Faculty of Electrical Engineering and Computer Science Technical University of Berlin Berlin Germany

Effective data transmission plays a crucial role in federated learning (FL), which enables collaborative model training without centralizing data. This paper proposes a new coded transmission to enhance the communication quality for FL. The proposed coded transmission incorporates weight quantization, multilevel coding, set partitioning, and multi-stage decoding which are optimized to improve the FL performance. Furthermore, the unequal error protection (UEP) strategy is adopted in the proposed coded transmission, which allows the code rates to be optimized according to the significance of the quantized data. Simulation results demonstrate that the proposed UEP-based coded transmission outperforms conventional bit-interleaved coded modulation (BICM) scheme in terms of NMSE performance for FL, which, in return, improves the FL performance.

关键词：

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Consensus Control of Leader-Following Multi-Agent Systems in Directed Topology With Heterogeneous Disturbances

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 423-431页

作者： Qinglai Wei Xin Wang Xiangnan Zhong Naiqi Wu the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049and also with Qingdao Academy of Intelligent IndustriesQingdao 266109China the Department of Computer and Electrical Engineering and Computer Science Florida Atlantic UniversityBoca Raton FL33431 USA the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and TechnologyMacao 999078China

This paper investigates the consensus problem for linear multi-agent systems with the heterogeneous disturbances generated by the Brown *** main contribution is that a control scheme is designed to achieve the dynamic consensus for the multi-agent systems in directed topology interfered by stochastic *** traditional ways,the coupling weights depending on the communication structure are static.A new distributed controller is designed based on Riccati inequalities,while updating the coupling weights associated with the gain matrix by state errors between adjacent *** introducing time-varying coupling weights into this novel control law,the state errors between leader and followers asymptotically converge to the minimum value utilizing the local *** the Lyapunov directed method and It?formula,the stability of the closed-loop system with the proposed control law is *** simulation results conducted by the new and traditional schemes are presented to demonstrate the effectiveness and advantage of the developed control method.

关键词： Consensus control directed topology external disturbance multi-agent(MA)systems

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Design of a computational intelligence system for detection of multiple sclerosis with visual evoked potentials

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Neuroscience Informatics 2025年第1期5卷 100177-100177页

作者： Mohsenpourian, Moussa Suratgar, Amir Abolfazl Ali Talebi, Heidar Arzani, Mahsa Moghadasi, Abdorreza Naser Malakouti, Seyed Matin Bagher Menhaj, Mohammad Distributed and Intelligent Optimization Research Laboratory Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Real-time Robotics Laboratory Department of Electrical Engineering Amirkabir University of Technology Hafez Ave. Tehran Iran Department of Neurology Sian Hospital Tehran University of Medical Sciences Tehran Iran MS Research Center Sina Hospital Tehran University of Medical Sciences Hasan Abad Sq. Tehran Iran

In this study, a new approach for modification of membership functions of a fuzzy inference system (FIS) is demonstrated, in order to serve as a pattern recognition tool for classification of patients diagnosed with multiple sclerosis (MS) from healthy controls (HC) using their visually evoked potential (VEP) recordings. The new approach utilizes Krill Herd (KH) optimization algorithm to modify parameters associated with membership functions of both inputs and outputs of an initial Sugeno-type FIS, while making sure that the error corresponding to training of the network is minimized. This novel pattern recognition system is applied for classification of VEP signals in 11 MS patients and 11 HC's. A feature extraction routine was performed on the VEP signals, and later substantial features were selected in an optimized feature subset selection scheme employing Ant Colony Optimization (ACO) and Simulated Annealing (SA) algorithms. This alone provided further information regarding clinical value of many previously unused VEP features as an aide for making the diagnosis. The newly designed computational intelligence system is shown to outperform popular classifiers (e.g., multilayer perceptron, support-vector machine, etc.) and was able to distinguish MS patients from HC's with an overall accuracy of 90%. © 2024 The Author(s)

关键词： Binary classification Fuzzy inference system Krill herd optimization Multiple sclerosis Visual evoked potentials

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Improved Radiation Estimation at High Energy Facility for Public Health

Improved Radiation Estimation at High Energy Facility for Pu...

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IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Jerry Cao-Xue Hongfang Zhang Adam Stavola Bence Budavari Chiman Kwan Hongyi Wu Jiang Li William G. Enloe High School Raleigh NC Department of Electrical and Computer Engineering Old Dominion University Norfolk VA Thomas Jefferson National Accelerator Facility Newport News VA Applied Research LLC Rockville MD Force Projection Sector Johs Hopkins University Applied Physics Laboratory Laurel MD Department of Electrical & Computer Engineering University of Arizona Tuscon AZ

ISBN: (数字)9798331540906

ISBN: (纸本)9798331540913

Long-term exposure to ionizing radiation can lead to the development of cancer and other potentially fatal diseases. Estimating radiation levels at high-energy particle accelerators is crucial for ensuring the safety of personnel, the public, and the environment. This paper explores the application of transformer neural networks, focusing on five different attention mechanisms—Full Attention, Prob Attention, AutoCorrelation, Fourier Attention, and Wavelet Attention—for estimating radiation levels at the Thomas Jefferson National Accelerator Facility (JLab). The goal is to improve the accuracy of radiation estimation by training models using data from key radiation sensors. Four hundred trials were conducted for each attention mechanism, and performance was evaluated using metrics including R 2 . The results demonstrated that Prob Attention and AutoCorrelation outperformed the other mechanisms, offering substantial improvements over previous models. These findings highlight the importance of selecting and fine-tuning attention mechanisms in transformers for complex time-series analysis tasks in radiation monitoring.

关键词： Wavelet transforms Training Radiation monitoring Attention mechanisms Time series analysis Estimation Transformers Sensors Safety Autocorrelation

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Energy-Efficient Probabilistic Semantic Communication over Space-Air-Ground Integrated Networks

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IEEE Transactions on Wireless Communications 2025年

作者： Zhao, Zhouxiang Yang, Zhaohui Chen, Mingzhe Zhu, Chen Xu, Wei Zhang, Zhaoyang Huang, Kaibin Zhejiang University College of Information Science and Electronic Engineering China Zhejiang Provincial Key Laboratory of Info. Proc. Commun Hangzhou310027 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL33146 United States Zhejiang University Polytechnic Institute Hangzhou310015 China Hangzhou Dianzi University School of Communication Engineering Hangzhou310018 China Southeast University National Mobile Communications Research Laboratory Nanjing211189 China The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong

Space-air-ground integrated networks (SAGINs) are emerging as a pivotal element in the evolution of future wireless networks. Despite their potential, the joint design of communication and computation within SAGINs remains a formidable challenge. In this paper, the problem of energy efficiency in SAGIN-enabled probabilistic semantic communication (PSCom) system is investigated. In the considered model, a satellite needs to transmit data to multiple ground terminals (GTs) via an unmanned aerial vehicle (UAV) acting as a relay. During transmission, the satellite and the UAV can use PSCom technique to compress the transmitting data, while the GTs can automatically recover the missing information. The PSCom is underpinned by shared probabilistic graphs that serve as a common knowledge base among the transceivers, allowing for resource-saving communication at the expense of increased computation resource. Through analysis, the computation overhead function in PSCom is a piecewise function with respect to the semantic compression ratio. Therefore, it is important to make a balance between communication and computation to achieve optimal energy efficiency. The joint communication and computation problem is formulated as an optimization problem aiming to minimize the total communication and computation energy consumption of the network under latency, power, computation capacity, bandwidth, semantic compression ratio, and UAV location constraints. To solve this non-convex non-smooth problem, we propose an iterative algorithm where the closed-form solutions for computation capacity allocation and UAV altitude are obtained at each iteration. Numerical results show the effectiveness of the proposed algorithm. © 2002-2012 IEEE.

关键词： Drones

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Personalized QoE Enhancement for Adaptive Video Streaming: A Digital Twin-Assisted Scheme

arXiv

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arXiv 2022年

作者： Huang, Xinyu Zhou, Conghao Wu, Wen Li, Mushu Wu, Huaqing Shen, Xuemin Department of Electrical & Computer Engineering University of Waterloo Canada Frontier Research Center Peng Cheng Laboratory China

In this paper, we present a digital twin (DT)-assisted adaptive video streaming scheme to enhance personalized quality-of-experience (PQoE). Since PQoE models are user-specific and time-varying, existing schemes based on universal and time-invariant PQoE models may suffer from performance degradation. To address this issue, we first propose a DT-assisted PQoE model construction method to obtain accurate user-specific PQoE models. Specifically, user DTs (UDTs) are respectively constructed for individual users, which can acquire and utilize users' data to accurately tune PQoE model parameters in real time. Next, given the obtained PQoE models, we formulate a resource management problem to maximize the overall long-term PQoE by taking the dynamics of users' locations, video content requests, and buffer statuses into account. To solve this problem, a deep reinforcement learning algorithm is developed to jointly determine segment version selection, and communication and computing resource allocation. Simulation results on the real-world dataset demonstrate that the proposed scheme can effectively enhance PQoE compared with benchmark schemes. © 2022, CC BY.

关键词： Quality of service

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