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IEEE Conference on Wireless Communications and Networking

作者： Hongxing Li Haotian Li Qianhan Gao Guochu Shou Yaqiong Liu Zhigang Guo Yihong Hu State Key Laboratory of Networking and Switching Technology School of Computer Science (National Pilot Software Engineering School) Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The demand for precise timing continues to grow with the rapid development of smart cities, where intelligent transportation emerges as a critical application heavily dependent on accurate time distribution. Deterministic communication is anticipated to be a defining feature of nextgeneration mobile networks (6G), enabling end-to-end timecritical applications, and unlocking new possibilities for achieving end-to-end precise time synchronization as well. To cope with the challenge of delivering precise time synchronization across broader areas at a lower cost, this paper proposes a method to achieve end-to-end precise time synchronization by leveraging the deterministic characteristics of communication networks. Specially, the proposed approach adopts the Software-Defined Networking (SDN) paradigm to implement deterministic networking for the seamless integration of Time-Sensitive Networking (TSN) and cellular networks. Furthermore, to mitigate the impact of wireless channel uncertainties on synchronization accuracy, we propose a network delay measurement mechanism based on the principle of time redundancy, designed to reduce end-to-end network delay variation. Experimental results validate the effectiveness of the proposed method, demonstrating its capability to constrain the end-to-end relative time error of wired and wireless converged networks to the microsecond scale, without necessitating modifications to the existing network infrastructure.

关键词： 6G mobile communication Wireless communication Cellular networks Uncertainty Accuracy Measurement uncertainty Transportation Delays Synchronization Software defined networking

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Pilot Protection Based on Amplitude Comparison for Renewable Power Teed Lines

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CSEE Journal of Power and Energy systems 2022年第6期8卷 1519-1529页

作者： Ke Jia Zhe Yang Yu Fang Zhengxuan Zhu Liming Zheng Tianshu Bi Ali Hooshyar State Key Laboratory of Alternative Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China Department of Electrical and Computer Engineering University of TorontoTorontoON M5S 3G4Canada

Fault behaviors of inverter-interfaced renewable energy generators(IIREGs)are quite diverse from those of synchronous generators(SGs).They show the variable system impedance,the limited current and low inertia,so the proportional brake differential protection installed on the transmission line has a high risk of failure for phase-phase *** cope with this adaptive problem,a new pilot protection is proposed considering the huge differences in both current ***,an improved criterion is presented for a scenario with a teed *** existing protection principles focus excessively on the fault current characteristics of IIREGs,so they cannot operate correctly once IIREGs do not output their fault *** happens when IIREGs produce no power or the circuit breakers exactly reclose on permanent ***,the proposed method has good performance for the above *** proposed method only requires current amplitude information,and in this case the strict synchronizing measurement is not *** studies are performed under different fault conditions,and they confirm the proposed method has good performance under different fault ***,it is also verified using field-testing data.

关键词： Current amplitude inverter-interfaced renewable energy generators pilot protection teed line

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Energy-Efficient and Reliable Federated Learning in Heterogeneous Mobile-Edge Computing 19

Energy-Efficient and Reliable Federated Learning in Heteroge...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Xu, Yan Li, Liying Cong, Peijin Zhou, Junlong Nanjing University of Science and Technology School of Computer Science and Engineering China Institute of Computing Technology Chinese Academy of Sciences State Key Laboratory of Computer Architecture China

ISBN: (纸本)9798350346558

As a decentralized machine learning paradigm, Federated Learning (FL) is an emerging technique to protect user data privacy in Mobile Edge Computing (MEC). FL adopts the idea of distributed privacy computing, enabling the terminal devices to train machine learning models required by servers locally and upload model parameters to servers for aggregation. However, in the process of coupling FL with MEC systems, device heterogeneity, malicious use of poisoned data by users and limited system resources will seriously affect FL training accuracy, system reliability and energy efficiency. In this paper, a highly reliable and energy-efficient FL scheme is designed to solve these issues. Specifically, we first propose a calculation method for estimating the reliability of heterogeneous devices. Then, we design a highly reliable device selection scheme based on devices' reliability, computing power, and participation times. Finally, we utilize the slack time in FL training to dynamically adjust the voltage and frequency of devices for reducing the energy consumption of training. Experimental results show that our proposed scheme improves the accuracy by up to 36.45% on average and saves energy by up to 48.63% when compared to the baseline and benchmarking methods. © 2022 IEEE.

关键词： Reliability

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From an odd arity signature to a Holant dichotomy

arXiv

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arXiv 2025年

作者： Meng, Boning Wang, Juqiu Xia, Mingji Zheng, Jiayi Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software University of Chinese Academy of Sciences Beijing100080 China

Holant is an essential framework in the field of counting complexity. For over fifteen years, researchers have been clarifying the complexity classification for complex-valued Holant on the Boolean domain, a challenge that remains unresolved. In this article, we prove a complexity dichotomy for complex-valued Holant on Boolean domain when a non-trivial signature of odd arity exists. This dichotomy is based on the dichotomy for #EO, and consequently is an FPNP vs. #P dichotomy as well, stating that each problem is either in FPNP or #P-hard. Furthermore, we establish a generalized version of the decomposition lemma for complex-valued Holant on Boolean domain. It asserts that each signature can be derived from its tensor product with other signatures, or conversely, the problem itself is in FPNP. We believe that this result is a powerful method for building reductions in complex-valued Holant, as it is also employed as a pivotal technique in the proof of the aforementioned dichotomy in this article. Copyright © 2025, The Authors. All rights reserved.

关键词： Complex networks

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Acorns: A Framework for Accelerating Deep Neural Networks with Input Sparsity 19

Acorns: A Framework for Accelerating Deep Neural Networks wi...

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Proceedings of the International Conference on Parallel architectures and Compilation Techniques

作者： Xiao Dong Lei Liu Peng Zhao Guangli Li Jiansong Li Xueying Wang Xiaobing Feng State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China and University of Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728136134

Deep neural networks have been employed in a broad range of applications, including face detection, natural language processing, and autonomous driving. Yet, the neural networks with the capability to tackle real-world problems are intrinsically expensive in computation, hindering the usage of these models. Sparsity in the input data of neural networks provides an optimizing opportunity. However, harnessing the potential performance improvement on modern CPU faces challenges raised by sparse computations of the neural network, such as cache-unfriendly memory accesses and efficient sparse kernel *** this paper, we propose Acorns, a framework to accelerate deep neural networks with input sparsity. In Acorns, sparse input data is organized into our designed sparse data layout, which allows memory-friendly access for kernels in neural networks and opens the door for many performance-critical optimizations. Upon that, Acorns generates efficient sparse kernels for operators in neural networks from kernel templates, which combine directions that express specific optimizing transformations to be performed, and straightforward code that describes the *** evaluations demonstrate Acorns can outperform state-of-the-art baselines by significant speedups. On the real-world detection task in autonomous driving, Acorns demonstrates 1.8-22.6× performance improvement over baselines. Specifically, the generated programs achieve 1.8-2.4× speedups over Intel MKL-DNN, 3.0-8.8× speedups over TensorFlow, and 11.1-13.2× speedups over Intel MKL-Sparse.

关键词： Compiler

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DasAtom: A Divide-and-Shuttle Atom Approach to Quantum Circuit Transformation

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IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2025年

作者： Huang, Yunqi Gao, Dingchao Ying, Shenggang Li, Sanjiang Faculty of Engineering and Information Technology SydneyNSW2007 Australia Institute of Software Chinese Academy of Sciences Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science China

Neutral atom (NA) quantum systems are emerging as a leading platform for quantum computation, offering superior or competitive qubit count and gate fidelity compared to superconducting circuits and ion traps. However, the unique features of NA devices, such as long-range interactions, long qubit coherence time, and the ability to physically move qubits, present distinct challenges for quantum circuit compilation. In this paper, we introduce DasAtom, a novel divide-and-shuttle atom approach designed to optimise quantum circuit transformation for NA devices by leveraging these capabilities. DasAtom partitions circuits into subcircuits, each associated with a qubit mapping that allows all gates within the subcircuit to be directly executed. The algorithm then shuttles atoms to transition seamlessly from one mapping to the next, enhancing both execution efficiency and overall fidelity. For a 30-qubit Quantum Fourier Transform (QFT), DasAtom achieves a 415.8x improvement in fidelity over the move-based algorithm Enola and a 10.6x improvement over the SWAP-based algorithm Tetris. Notably, this improvement is expected to increase exponentially with the number of qubits, positioning DasAtom as a highly promising solution for scaling quantum computation on NA platforms. © 1982-2012 IEEE.

关键词： Qubits

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Feature Decoupled of Deep Mutual Information Maximization 2

Feature Decoupled of Deep Mutual Information Maximization

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2nd International Conference on Automation, Robotics and computer Engineering, ICARCE 2023

作者： He, Xing Peng, Changgen Wang, Lin Tan, Weijie Wang, Zifan State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China Guizhou Big Data Academy Guizhou University Guiyang China Guizhou Minzu University Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guiyang China Institute of Guizhou Aerospace Measuring and Testing Technology Guiyang China

ISBN: (纸本)9798350308341

In deep learning, supervised learning techniques usually require a large amount of expensive labeled data to train the network, and the feature representations extracted by the model usually mix multiple attributes, resulting in feature representations that are difficult to decouple and are non-interpretable, which restricts the application and development of deep learning techniques, and for this reason, it is particularly important to study decoupled feature representation methods for unsupervised learning. Although the Learning deep representations by mutual information estimation and maximization (DIM) method achieves excellent results in unsupervised learning, the feature representations learned by the DIM method still suffer from the problem of difficult decoupling. decoupling problem. To address this problem, we minimize the mutual information between the intermediate layer feature representations learned by the hidden layer of the encoder during the encoder training process, so that the features learned by each filter are as uncorrelated as possible, thus realizing feature decoupling, and our method is called FP-DIM. Finally, the effectiveness of the FP-DIM method is verified on the CIFAR-10, STL-10, and Fashion-MNIST datasets. The experiments show that our proposed FP-DIM method is more significant for the learned decouplable middle layer feature representation. Finally, we also propose a reflection of future research for the FP-DIM method, aiming to provide a research idea and direction for solving unsupervised interpretable machine learning and to lay a solid theoretical and application foundation for machine learning fields such as image classification and migration learning. © 2023 IEEE.

关键词： Unsupervised learning

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Advswap: Covert Adversarial Perturbation with High Frequency Info-Swapping for Autonomous Driving Perception 27

Advswap: Covert Adversarial Perturbation with High Frequency...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Huang, Yuanhao Zhang, Qinfan Xing, Jiandong Cheng, Mengyue Yu, Haiyang Ren, Yilong Xiong, Xiao School of Transportation Science and Engineering Beihang University Beijing100191 China State Key Lab of Intelligent Transportation System Beijing100191 China Zhongguancun Laboratory Beijing100094 China University of Alberta Department of Electrical and Computer Engineering Edmonton Canada

ISBN: (纸本)9798331505929

Perception module of Autonomous vehicles (AVs) are increasingly susceptible to be attacked, which exploit vulnerabilities in neural networks through adversarial inputs, thereby compromising the AI safety. Some researches focus on creating covert adversarial samples, but existing global noise techniques are detectable and difficult to deceive the human visual system. This paper introduces a novel adversarial attack method, AdvSwap, which creatively utilizes wavelet-based highfrequency information swapping to generate covert adversarial samples and fool the camera. AdvSwap employs invertible neural network for selective high-frequency information swapping, preserving both forward propagation and data integrity. The scheme effectively removes the original label data and incorporates the guidance image data, producing concealed and robust adversarial samples. Experimental evaluations and comparisons on the GTSRB and nuScenes datasets demonstrate that AdvSwap can make concealed attacks on common traffic targets. The generates adversarial samples are also difficult to perceive by humans and algorithms. Meanwhile, the method has strong attacking robustness and attacking transferability. © 2024 IEEE.

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User Selection and Resource Allocation for Satellite-Based Multi-task Federated Learning system 4th

User Selection and Resource Allocation for Satellite-Based M...

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4th International Conference on Artificial Intelligence in China, AIC 2022

作者： Zhang, Mingyu Wu, Xiaoyong Zhang, Zhilong Liu, Danpu Yin, Fangfang Beijing Laboratory of Advanced Information Network Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China Shanghai Radio Equipment Research Institute Shanghai201109 China State Key Laboratory of Media Convergence and Communication School of Information and Communication Communication University of China Beijing100024 China

ISBN: (纸本)9789819912551

In view of the development of low-orbit satellite constellation and the increasing demand for computing ability and privacy protection of space-based applications, a satellite-based multi-task federated learning system is designed. Specifically, with consideration of both the fluctuations of satellite-to-ground communication links and local training impacts on global models, a user selection strategy is established. On this basis, the functional relationship between task convergence speed and wireless communication factors is analyzed, and the KM optimal matching algorithm is adopted to dynamically allocate communication resources. Simulation results show that our proposed scheme outperforms baseline methods in both total weighted error rate and accuracy, and achieves the fastest convergence rate at the same time. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Satellites

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Towards Expanding Precise Timing for Collaborative Its with Deterministic Communications in 6G

Towards Expanding Precise Timing for Collaborative Its with ...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Li, Hongxing Li, Haotian Gao, Qianhan Shou, Guochu Liu, Yaqiong Guo, Zhigang Hu, Yihong State Key Laboratory of Networking and Switching Technology China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing China

ISBN: (纸本)9798350368369

关键词： Mobile telecommunication systems

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