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Current-Induced Helicity Switching of Frustrated Skyrmions on a Square-Grid Obstacle Pattern

Journal of the Magnetics Society of Japan 2023年第1期47卷 20-27页

作者： Zhang, X. Xia, J. Tretiakov, O.A. Diep, H.T. Zhao, G. Yang, J. Zhou, Y. Ezawa, M. Liu, X. Department of Electrical and Computer Engineering Shinshu University 4-17-1 Wakasato Nagano380-8553 Japan School of Physics The University of New South Wales Sydney2052 Australia Laboratoire de Physique Théorique et Modélisation CY Cergy Paris Université Cergy-Pontoise Cedex95302 France College of Physics and Electronic Engineering Sichuan Normal University Chengdu610068 China State Key Laboratory for Mesoscopic Physics School of Physics Peking University Beijing100871 China School of Science and Engineering The Chinese University of Hong Kong Guangdong Shenzhen518172 China Department of Applied Physics The University of Tokyo 7-3-1 Hongo Tokyo113-8656 Japan

Topological spin textures on artificial pinning landscape may show unique static and dynamic properties. Here, we computationally show that the helicity of frustrated skyrmions on an artificial square-grid obstacle pattern can be switched by a spin current pulse. The obstacle pattern is formed by defect lines with enhanced perpendicular magnetic anisotropy, which could protect the skyrmion from being annihilated at the sample edge. It is found that the skyrmion driven by a moderate current shows a circular motion guided by the boundary of the obstacle pattern, while it shows an almost straight motion toward the sample edge in the absence of the obstacle pattern. By applying a short pulse current to drive the skyrmion in a sample with the obstacle pattern, we find that the helicity of the skyrmion could be switched between Bloch-type configurations favored by the dipole-dipole interaction. Besides, we demonstrate the pos-sibility of switching the helicity of an array of skyrmions on the square-grid obstacle pattern using the same method. Our results could be useful for the helicity control of topological spin textures, and may provide guidelines for building future helicity-based spintronic functions. © 2023 by the Magnetics Society of Japan.

关键词： Textures

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AFML: An Asynchronous Federated Meta-Learning Mechanism for Charging Station Occupancy Prediction with Biased and Isolated Data

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IEEE Transactions on Big Data 2024年

作者： Chen, Qiyang Li, Han You, Linlin Qu, Haohao Abdelmoniem, Ahmed M. Yuen, Chau Sun Yat-Sen University School of Intelligent Systems Engineering Shenzhen510006 China Hong Kong Polytechnic University Department of Computing Hong Kong100872 Hong Kong Queen Mary University of London Department of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Assiut University Department of Computer Science Assiut71515 Egypt Nanyang Technological University School of Electrical and Electronics Engineering Singapore639798 Singapore

Electric vehicles (EVs) are driving green and low-carbon transport in modern cities. It makes charging station occupancy prediction (CSOP) critual for intelligent transportation systems (ITS) to achieve a balance between the supply and demand in resolving the dynamics between EVs and changing stations. Even though several big data-based solutions have been discussed, they are still struggling to collaboratively utilize heterogeneous data and distributed computing resources located at both physically and logicially isolated charging stations to better support context-driven CSOP. To addres this challenge, we propose an Asynchronous Federated Meta-learning Mechanism (AFML) for CSOP, which can train a meta-model with strong adaptation ability in an asynchronous and collaborative manner. In general, it incorporates an adaptive reptile algorithm (AR) and an weighted aggregation strategy (WA) to jointly ensure the training efficiency and model adaptivity. Evaluations on real-world CSOP datasets demonstrate that compared to the second best method, AFML can significantly improve forecasting accuracy by 14%, accelerate model convergence by 9% and enhance model generalizability by 10%, illustrating its merits in support CSOP to embrace a smart and sustainable city. © 2015 IEEE.

关键词： Federated learning

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Multi-behavior Learning for Socially Compatible Autonomous Driving

Multi-behavior Learning for Socially Compatible Autonomous D...

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International Conference on Intelligent Transportation

作者： Sanjula Jayawardana Vindula Jayawardana Kaneeka Vidanage Cathy Wu University of Westminster UK Informatics Institute of Technology Colombo Sri Lanka Department of Electrical Engineering and Computer Science MIT Laboratory for Information & Decision Systems Cambridge MA USA Department of Civil and Environmental Engineering MIT Laboratory for Information Decision Systems Institute of Data Systems and Society Cambridge MA USA

Autonomous vehicles (AVs) have the potential to revolutionize transportation, but their effective integration into the real world requires addressing the challenge of interacting with human drivers. Real-world driving involves negotiating and cooperating with fellow drivers through social cues, necessitating AVs to also demonstrate such social compatibilities. However, despite the popularity, current learning-based control methods for AV policy synthesis often overlook this crucial aspect. In this work, we look at the problem of enabling socially compatible driving when AV control policies are learned. We leverage human driving data to learn a social preference model of human driving and then integrate it with reinforcement learning-based AV policy synthesis using Social Value Orientation theory. In particular, we propose to use multi-task reinforcement learning to learn diverse social compatibility levels in driving (ex: altruistic, prosocial, individualistic, and competitive), focusing on the requirement of having diverse behaviors in real-world driving. Using highway driving scenarios, we demonstrate through experiments that socially compatible AV driving not only enables naturalistic driving behaviors but also reduces collision rates from the baseline. Our findings reveal that without social compatibility, AV policies tend to adopt dangerously competitive driving behaviors, while the incorporation of social compatibility fosters smoother vehicle maneuvers.

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Learning a Better Control Barrier Function

Learning a Better Control Barrier Function

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IEEE Conference on Decision and Control

作者： Bolun Dai Prashanth Krishnamurthy Farshad Khorrami Electrical & Computer Engineering Department Control/Robotics Research Laboratory Tandon School of Engineering New York University Brooklyn NY

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Control barrier functions (CBFs) are widely used in safety-critical controllers. However, constructing a valid CBF is challenging, especially under nonlinear or non-convex constraints and for high relative degree systems. Meanwhile, finding a conservative CBF that only recovers a portion of the true safe set is usually possible. In this work, starting from a "conservative" handcrafted CBF (HCBF), we develop a method to find a CBF that recovers a reasonably larger portion of the safe set. Since the learned CBF controller is not guaranteed to be safe during training iterations, we use a model predictive controller (MPC) to ensure safety during training. Using the collected trajectory data containing safe and unsafe interactions, we train a neural network to estimate the difference between the HCBF and a CBF that recovers a closer solution to the true safe set. With our proposed approach, we can generate safe controllers that are less conservative and computationally more efficient. We validate our approach on two systems: a second-order integrator and a ball-on-beam.

关键词： Training Heuristic algorithms Neural networks Predictive models Data collection Data models Safety

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Exploring and Lifting the Robustness of LLM-powered Automated Program Repair with Metamorphic Testing

arXiv

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arXiv 2024年

作者： Xue, Pengyu Wu, Linhao Yang, Zhen Li, Xinyi Yu, Zhongxing Jin, Zhi Li, Ge Xiao, Yan Wu, Jingwen The School of Computer Science and Technology Shandong University Qingdao China The Key Laboratory of High Confidence Software Technologies Peking University Ministry of Education China The School of Computer Science Peking University Beijing China The School of Electrical and Electronic Engineering Nanyang Technological University Singapore The School of Cyber Science and Technology Sun Yat-sen University Shenzhen China

In recent years, Large language model-powered Automated Program Repair (LAPR) techniques have achieved state-of-the-art bug-fixing performance and have been pervasively applied and studied in both industry and academia. Nonetheless, LLMs were proved to be highly sensitive to input prompts, with slight differences in the expressions of semantically equivalent programs potentially causing repair failures. Therefore, it is crucial to conduct robustness testing on LAPR techniques before their practical deployment. However, related research is scarce. To this end, we propose MT-LAPR, a Metamorphic Testing framework exclusively for LAPR techniques, which summarizes nine widely-recognized Metamorphic Relations (MRs) by developers across three perturbation levels: token, statement, and block. Afterward, our proposed MRs are applied to buggy codes to generate test cases, which are semantically equivalent yet to affect the inference of LAPR. Experiments are carried out on two extensively examined bug-fixing datasets, i.e., Defect4J and QuixBugs, and four bug-fixing abled LLMs released recently, demonstrating that 34.4%∼48.5% of the test cases expose the instability of LAPR techniques on average, showing the effectiveness of MT-LAPR and uncovering a positive correlation between code readability and the robustness of LAPR techniques. Inspired by the above findings, this paper uses the test cases generated by MT-LAPR as samples to train a CodeT5-based code editing model aiming at improving code readability and then embeds it into the LAPR workflow as a data preprocessing step. Extensive experiments demonstrate that this approach significantly enhances the robustness of LAPR by 49.32% at most. Copyright © 2024, The Authors. All rights reserved.

关键词： Software testing

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Reliable Adaptive Recoding for Batched Network Coding with Burst-Noise Channels

Reliable Adaptive Recoding for Batched Network Coding with B...

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Asilomar Conference on Signals, systems & computers

作者： Jie Wang Talha Bozkus Yao Xie Urbashi Mitra H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta USA Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles USA

Network coding provides an efficient approach for multi-hop communication. The message from the source node is encoded into batches of coded packets, and intermediate nodes perform recoding steps to transmit the message to the destination node. Adaptive recoding optimizes the number of recoded packets per batch to enhance network throughput, accounting for fluctuations in packet loss. In this paper, we propose an adaptive recoding scheme in burst-noise channels with unknown channel parameters. We first provide uncertainty quantification for channel parameters using historical data and build a confidence set to cover true channel parameters with high probability. Next, we obtain the optimal recoding policy by solving a robust Markov decision process (MDP) problem, where uncertain parameters belong to the confidence set. The objective of the robust MDP is to optimize the worst-case reward function by considering all possible problem parameters from the confidence set. Experimental results demonstrate that our proposed recoding strategy significantly enhances network communication throughput with burst-noise channels.

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computer-Vision-Based Non-Contact Paste Concentration Measurement*

Computer-Vision-Based Non-Contact Paste Concentration Measur...

引用

Automation in Manufacturing, Transportation and Logistics (ICaMaL), International Conference on

作者： Tailin Liang Zhaolin Yuan State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China Department of Industrial and Systems Engineering Research Institute of Advanced Manufacturing The Hong Kong Polytechnic University Hong Kong China

ISBN: (数字)9798350378658

ISBN: (纸本)9798350378665

For monitoring the paste concentration, existing techniques, such as ultrasonic concentration meters and neutron meters, suffer from radiation hazards and low precision in high concentrations. This paper proposes a novel non-contact concentration measurement method based on deep learning. With the dataset collected by preparing the paste with different concentrations and taking photographs from the paste surface, a convolutional neural network is trained to extract the features from images and predict the concentration. The experiments indicate that the measurement accuracy is close to 88.79 % for the manual stirring paste dataset and 91.42 % for the automatic stirring paste dataset, which are sufficient in industrial applications. As a substitution of traditional concentration measurement instruments, the proposed vision-based concentration measurement method is non-contact and its accuracy is sufficient in industrial applications.

关键词： Meters Deep learning Accuracy Ultrasonic variables measurement Transportation Manuals Feature extraction Surface texture Convolutional neural networks Surface treatment

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Output Feedback Position Tracking Control of Marine Vessels Subject to Periodic Disturbances

Output Feedback Position Tracking Control of Marine Vessels ...

引用

American Control Conference (ACC)

作者： Deniz Kurtoglu Enver Tatlicioglu Erkan Zergeroglu Department of Mechanical Engineering Laboratory for Autonomy Control Information and Systems (LACIS) University of South Florida Tampa FL United States of America Department of Electrical Electronics Engineering Ege University Izmir Turkey Department of Computer Engineering Gebze Technical University Gebze Kocaeli Turkey

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This work deals with the design and implementation of a novel output feedback position tracking controller for marine vessels subject to uncertainties in their dynamical parameters and periodic external disturbance. Specifically, an adaptive controller that does not make use of velocity measurements has been presented which can compensate for uncertainties in the system's dynamical parameters and periodic external disturbance. A filtered based velocity surrogate formulation in conjunction with a periodic noise estimator and a desired model compensation based adaptive parameter estimator have been utilized to tackle the problem. Boundedness of the closed loop system and convergence of the position tracking error to the origin are proven via Lyapunov-type arguments. Comparative numerical simulations are presented to illustrate the effectiveness of the proposed controller.

关键词： Uncertain systems Uncertainty Noise Estimation Numerical simulation Filtering theory Numerical models

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Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses

arXiv

引用

arXiv 2023年

作者： Xu, Minrui Niyato, Dusit Chen, Junlong Zhang, Hongliang Kang, Jiawen Xiong, Zehui Mao, Shiwen Han, Zhu The School of Computer Science and Engineering Nanyang Technological University Singapore308232 Singapore The School of Electronics Peking University Beijing100871 China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States The Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

In the vehicular mixed reality (MR) Metaverse, the distance between physical and virtual entities can be overcome by fusing the physical and virtual environments with multidimensional communications in autonomous driving systems. Assisted by digital twin (DT) technologies, connected autonomous vehicles (AVs), roadside units (RSU), and virtual simulators can maintain the vehicular MR Metaverse via digital simulations for sharing data and making driving decisions collaboratively. However, large-scale traffic and driving simulation via realistic data collection and fusion from the physical world for online prediction and offline training in autonomous driving systems are difficult and costly. In this paper, we propose an autonomous driving architecture, where generative AI is leveraged to synthesize unlimited conditioned traffic and driving data in simulations for improving driving safety and traffic efficiency. First, we propose a multi-task DT offloading model for the reliable execution of heterogeneous DT tasks with different requirements at RSUs. Then, based on the preferences of AV’s DTs and collected realistic data, virtual simulators can synthesize unlimited conditioned driving and traffic datasets to further improve robustness. Finally, we propose a multi-task enhanced auction-based mechanism to provide fine-grained incentives for RSUs in providing resources for autonomous driving. The property analysis and experimental results demonstrate that the proposed mechanism and architecture are strategy-proof and effective, respectively. © 2023, CC BY.

关键词： Autonomous vehicles

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Towards Efficient Deep Spiking Neural Networks Construction with Spiking Activity based Pruning

arXiv

引用

arXiv 2024年

作者： Li, Yaxin Xu, Qi Shen, Jiangrong Xu, Hongming Chen, Long Pan, Gang School of Computer Science and Technology Dalian University of Technology Dalian China Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen China College of Computer Science and Technology Zhejiang University Hangzhou China Centre for Systems Engineering and Innovation Imperial College London London United Kingdom

The emergence of deep and large-scale spiking neural networks (SNNs) exhibiting high performance across diverse complex datasets has led to a need for compressing network models due to the presence of a significant number of redundant structural units, aiming to more effectively leverage their low-power consumption and biological interpretability advantages. Currently, most model compression techniques for SNNs are based on unstructured pruning of individual connections, which requires specific hardware support. Hence, we propose a structured pruning approach based on the activity levels of convolutional kernels named Spiking Channel Activity-based (SCA) network pruning framework. Inspired by synaptic plasticity mechanisms, our method dynamically adjusts the network’s structure by pruning and regenerating convolutional kernels during training, enhancing the model’s adaptation to the current target task. While maintaining model performance, this approach refines the network architecture, ultimately reducing computational load and accelerating the inference process. This indicates that structured dynamic sparse learning methods can better facilitate the application of deep SNNs in low-power and high-efficiency scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Network architecture

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