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Role of substrate clamping on anisotropy and domain structure in the canted antiferromagnet α−Fe2O3

Physical Review B 2022年第22期106卷 224419-224419页

作者： Angela Wittmann Olena Gomonay Kai Litzius Allison Kaczmarek Alexander E. Kossak Daniel Wolf Axel Lubk Tyler N. Johnson Elizaveta A. Tremsina Alexandra Churikova Felix Büttner Sebastian Wintz Mohamad-Assaad Mawass Markus Weigand Florian Kronast Larry Scipioni Adam Shepard Ty Newhouse-Illige James A. Greer Gisela Schütz Norman O. Birge Geoffrey S. D. Beach Department of Materials Science and Engineering Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Institute of Physics Johannes Gutenberg-University Mainz 55128 Mainz Germany Max Planck Institute for Intelligent Systems 70569 Stuttgart Germany Leibniz Institute for Solid State and Materials Research IFW Dresden 01069 Dresden Germany Department of Chemical Engineering and Materials Science Michigan State University East Lansing Michigan 48824 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Helmholtz-Zentrum Berlin für Materialien und Energie GmbH 14109 Berlin Germany PVD Products Wilmington Massachusetts 01887 USA Department of Physics and Astronomy Michigan State University East Lansing Michigan 48824 USA

Antiferromagnets have recently been propelled to the forefront of spintronics by their high potential for revolutionizing memory technologies. For this, understanding the formation and driving mechanisms of the domain structure is paramount. In this work, we investigate the domain structure in a thin-film canted antiferromagnet α−Fe2O3. We find that the internal destressing fields driving the formation of domains do not follow the crystal symmetry of α−Fe2O3, but fluctuate due to substrate clamping. This leads to an overall isotropic distribution of the Néel order with locally varying effective anisotropy in antiferromagnetic thin films. Furthermore, we show that the weak ferromagnetic nature of α−Fe2O3 leads to a qualitatively different dependence on the magnetic field compared to collinear antiferromagnets such as NiO. The insights gained from our work serve as a foundation for further studies of electrical and optical manipulation of the domain structure of antiferromagnetic thin films.

关键词： Antiferromagnetism Magnetic domains Magnetic texture Spin Hall magnetoresistance

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Progressive frequency-decoupled network with neighbor-aware self-evolving convolution kernels for pansharpening

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Pattern Recognition 2026年 169卷

作者： Rui Miao Fengguang Peng Dong Dong Hang Shi Zhejiang Lab Hangzhou China State Key Laboratory of Ocean Sensing Hangzhou China School of Computer Science and Engineering Beihang University China Department of Computer Science and Technology Tsinghua University China

Pansharpening uses the spatial details of the panchromatic image (PAN) to sharpen the multispectral image (MS), so as to obtain a higher-resolution multispectral image for subsequent vision tasks. Existing methods focus more on high-level semantic feature extraction, the diverse features cannot be effectively distinguished and fully utilized. In this paper, we propose a novel strategy to generate neighbor-aware self-evolving convolution kernels (NSConv) and introduce a frequency-decoupled block (FDBlock) for deep fusion. The proposed NSConv can dynamically optimize in multiple dimensions and enhance the ability to extract locally correlated features. Furthermore, the FDBlock realizes the interactive fusion of dual-domain features based on NSConv to learn the complementary representation, making the overall network have a more diverse representation and strong neighborhood feature extraction ability. We conduct extensive qualitative and quantitative experiments to validate the effectiveness of the proposed network and demonstrate its significant performance against other state-of-the-art methods.

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A Distributed Privacy Preservation Approach for Big Data in Public Health Emergencies Using Smart Contract and SGX

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computers, Materials & Continua 2020年第10期65卷 723-741页

作者： Jun Li Jieren Cheng Naixue Xiong Lougao Zhan Yuan Zhang Hainan Blockchain Technology Engineering Research Center Hainan UniversityHaikou570228China School of Compute Science and Cyberspace Security Hainan UniversityHaikou570228China Department of Mathematics and Computer Science Northeastern State UniversityTahlequah74464USA Key Laboratory of Internet Information Retrieval of Hainan Province Hainan UniversityHaikou570228China

Security and privacy issues have become a rapidly growing problem with the fast development of big data in public ***,big data faces many ongoing serious challenges in the process of collection,storage,and *** them,data security and privacy problems have attracted extensive *** an effort to overcome this challenge,this article aims to present a distributed privacy preservation approach based on smart contracts and Intel Software Guard Extensions(SGX).First of all,we define SGX as a trusted edge computing node,design data access module,data protection module,and data integrity check module,to achieve hardware-enhanced data privacy ***,we design a smart contract framework to realize distributed data access control management in a big data *** crucial role of the smart contract was revealed by designing multiple access control contracts,register contracts,and history *** control contracts provide access control methods for different users and enable static access verification and dynamic access verification by checking the user’s properties and history *** contract contains user property information,edge computing node information,the access control and history smart contract information,and provides functions such as registration,update,and *** contract records the historical behavior information of malicious users,receives the report information of malicious requestors from the access control contract,implements a misbehavior check method to determines whether the requestor has misbehavior,and returns the corresponding ***,we design decentralized system architecture,prove the security properties,and analysis to verify the feasibility of the *** demonstrate that our method can effectively improve the timeliness of data,reduce network latency,and ensure the security,reliability,and traceability of data.

关键词： SGX big data privacy protection smart contract access control

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Pushing AI to wireless network edge: An overview on integrated sensing, communication, and computation towards 6G

arXiv

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arXiv 2022年

作者： Zhu, Guangxu Lyu, Zhonghao Jiao, Xiang Liu, Peixi Chen, Mingzhe Xu, Jie Cui, Shuguang Zhang, Ping Shenzhen Research Institute of Big Data Shenzhen518172 China Shenzhen518172 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables33146 United States State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Peng Cheng Laboratory Shenzhen518066 China

Pushing artificial intelligence (AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things (AIoT) in the sixth-generation (6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the huge amount of data scattered at wireless network edge. In general, realizing edge intelligence corresponds to the process of sensing, communication, and computation, which are coupled ingredients for data generation, exchanging, and processing, respectively. However, conventional wireless networks design the sensing, communication, and computation separately in a task-agnostic manner, which encounters difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications such as auto-driving. This thus prompts a new design paradigm of seamless integrated sensing, communication, and computation (ISCC) in a task-oriented manner, which comprehensively accounts for the use of the data in the downstream AI applications. In view of its growing interest, this article provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art development, and shedding light on the road ahead. Copyright © 2022, The Authors. All rights reserved.

关键词： Artificial intelligence

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Modeling Method for DFIG-Based Wind Farm in High-Efficiency Real-Time Electromagnetic Transient (EMT) Simulations

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IEEE Transactions on Power Electronics 2025年

作者： Liu, Yifan Xu, Jianzhong Zhu, Yiyang Tian, Zhaoxuan Zhao, Chengyong Li, Gen State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources China Energy Technology and Computer Science Section Department of Engineering Technology and Didactics Ballerup2750 Denmark

With the increasing integration of renewable energy into power systems, electromagnetic transient (EMT) simulation has become indispensable for accurate system analysis. However, the complexity of wind turbine (WT) modeling, characterized by a large number of electrical nodes, poses significant challenges and necessitates substantial real-time simulation hardware. Existing methods for reducing circuit complexity improve simulation efficiency but are each associated with inherent limitations. Aggregation methods sacrifice considerable internal station information, while existing decoupling techniques are constrained by specific requirements. This paper proposes a real-time simulation model for a Doubly Fed Induction Generator (DFIG)-based wind farm (WF) using latency decoupling and a multi-level nested fast and simultaneous solution (M-NFSS) approach, effectively reducing node count while preserving internal details. A WF test model is implemented in RTDS for validation. Results demonstrate highly accurate impedance characteristics and time-domain waveforms of the proposed model, utilizing only 33.3% of the hardware resources compared to the traditional detailed model. © 1986-2012 IEEE.

关键词： Electromagnetic transients

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Pruning random resistive memory for optimizing analogue AI

arXiv

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arXiv 2023年

作者： Li, Yi Wang, Songqi Zhao, Yaping Wang, Shaocong Zhang, Woyu He, Yangu Lin, Ning Cui, Binbin Chen, Xi Zhang, Shiming Jiang, Hao Lin, Peng Zhang, Xumeng Qi, Xiaojuan Wang, Zhongrui Xu, Xiaoxin Shang, Dashan Liu, Qi Cheng, Kwang-Ting Liu, Ming Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Laboratory of Microelectronic Devices & Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China ACCESS – AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong State Key Lab of Fabrication Technologies for Integrated Circuits Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China Frontier Institute of Chip and System Fudan University Shanghai200433 China College of Computer Science and Technology Zhejiang University Zhejiang310027 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong University of Chinese Academy of Sciences Beijing100049 China Institute of Mind The University of Hong Kong Hong Kong

The rapid advancement of artificial intelligence (AI) has been marked by the large language models exhibiting human-like intelligence. However, these models also present unprecedented challenges to energy consumption and environmental sustainability, which are exacerbated by the increasing number of users and the development of even larger models. One promising solution is to revisit analogue computing, a technique that predates digital computing and exploits emerging analogue electronic devices, such as resistive memory, which features in-memory computing, high scalability, and nonvolatility that addresses the von Neumann bottleneck, slowdown of Moore’s law, and volatile DRAM of conventional digital hardware. However, analogue computing still faces the same challenges as before: programming nonidealities and expensive programming due to the underlying devices physics. Therefore, leveraging the efficiency advantage while mitigating the programming disadvantage of analogue computing with resistive memory is a major open problem in AI hardware and electronics communities. Here, we report a universal solution, software-hardware co-design using structural plasticity-inspired edge pruning to optimize the topology of a randomly weighted analogue resistive memory neural network. Software-wise, the topology of a randomly weighted neural network is optimized by pruning connections rather than precisely tuning resistive memory weights. Hardware-wise, we reveal the physical origin of the programming stochasticity using transmission electron microscopy, which is leveraged for large-scale and low-cost implementation of an overparameterized random neural network containing high-performance sub-networks. We implemented the co-design on a 40nm 256K resistive memory macro, observing 17.3% and 19.9% accuracy improvements in image and audio classification on FashionMNIST and Spoken digits datasets, as well as 9.8% (2%) improvement in PR (ROC) in image segmentation on DRIVE datasets, r

关键词： Energy utilization

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Sparse data-driven knowledge discovery for interpretable prediction of permeability in tight sandstones

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Engineering Geology 2025年 353卷

作者： Xu, Lulu Du, Zhengyang Cai, Meifeng Yin, Shangxian Dong, Shuning Thanh, Hung Vo Carroll, Kenneth C. Soltanian, Mohamad Reza Dai, Zhenxue College of Construction Engineering Jilin University State Key Laboratory of Deep Earth Exploration and Imaging Changchun China Institute of Intelligent Simulation and Early Warning for Subsurface Environment Jilin University Changchun130026 China School of Civil and Resource Engineering University of Science and Technology Beijing Beijing100083 China College of Safety Engineering North China Institute of Science and Technology Langfang065201 China Xi'an Research Institute Co. Ltd. China Coal Technology and Engineering Group Corp Xi'an710077 China Waseda Research Institute for Science and Engineering Waseda University 3-4-1 Okubo Tokyo Shinjuku-ku169-8555 Viet Nam Department of Plant and Environmental Sciences New Mexico State University Las CrucesNM88003 United States Department of Geosciences University of Cincinnati CincinnatiOH45220 United States Department of Environmental Engineering University of Cincinnati CincinnatiOH45220 United States School of Environmental and Municipal Engineering Qingdao University of Technology Qingdao266033 China

Permeability (k) is crucial for subsurface fluid flow, but predicting k-values in tight sandstones remains challenging due to their complex pore structure and heterogeneity. Although machine learning (ML) has shown promise, it faces significant challenges, including limited high-quality data, high computational costs, and unclear prediction mechanisms. This study proposes a sparse data-driven knowledge discovery framework aimed at enhancing the accuracy and interpretability of k-value predictions in tight sandstone formations. We integrate ML models with data augmentation (ML-DA), using Extreme Gradient Boosting (XGBoost-DA) and Least Squares Support Vector Regression (LSSVR-DA), optimized through genetic algorithms (GA), particle swarm optimization (PSO), and Bayesian optimization (BO). SHapley Additive Explanations (SHAP) are employed to elucidate the interactions between key factors influencing predictions. Monte Carlo simulations demonstrate the robust performance of our ML-DA models, even under data constraints. SHAP analysis identifies key predictors, including porosity, displacement pressure, median pore throat radius, median pressure, and carbonate content. Partial dependence plots (PDPs) reveal a significant interaction between porosity and carbonate content, as well as a decrease in model stability at low carbonate content. This study presents an interpretable ML framework with data augmentation, enabling improved predictions from sparse data while exploring the interactions between key factors. The framework can be adapted to other domains facing similar challenges, enhancing the accuracy and transparency of model predictions. © 2025 Elsevier B.V.

关键词： Data augmentation Explainable AI Hybrid intelligence Optimization algorithms Permeability prediction Tight sandstones

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Tackling Over-Smoothing for General Graph Convolutional Networks

arXiv

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arXiv 2020年

作者： Huang, Wenbing Rong, Yu Xu, Tingyang Sun, Fuchun Huang, Junzhou State Key Lab on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University China Tencent AI Lab Shenzhen China

Increasing the depth of GCN, which is expected to permit more expressivity, is shown to incur performance detriment especially on node classification. The main cause of this lies in over-smoothing. The over-smoothing issue drives the output of GCN towards a space that contains limited distinguished information among nodes, leading to poor expressivity. Several works on refining the architecture of deep GCN have been proposed, but it is still unknown in theory whether or not these refinements are able to relieve over-smoothing. In this paper, we first theoretically analyze how general GCNs act with the increase in depth, including generic GCN, GCN with bias, ResGCN, and APPNP. We find that all these models are characterized by a universal process: all nodes converging to a cuboid. Upon this theorem, we propose DropEdge to alleviate over-smoothing by randomly removing a certain number of edges at each training epoch. Theoretically, DropEdge either reduces the convergence speed of over-smoothing or relieves the information loss caused by dimension collapse. Experimental evaluations on simulated dataset have visualized the difference in over-smoothing between different GCNs. Moreover, extensive experiments on several real benchmarks support that DropEdge consistently improves the performance on a variety of both shallow and deep GCNs. Copyright © 2020, The Authors. All rights reserved.

关键词： Benchmarking

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Over-the-Air Computation for 6G: Foundations, Technologies, and Applications

arXiv

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arXiv 2022年

作者： Wang, Zhibin Zhao, Yapeng Zhou, Yong Shi, Yuanming Jiang, Chunxiao Letaief, Khaled B. The School of Information Science and Technology ShanghaiTech University Shanghai201210 China The State Key Laboratory of Internet of Things for Smart City University of Macau Macao999078 China The Tsinghua Space Center The Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

The rapid advancement of artificial intelligence technologies has given rise to diversified intelligent services, which place unprecedented demands on massive connectivity and gigantic data aggregation. However, the scarce radio resources and stringent latency requirement make it challenging to meet these demands. To tackle these challenges, over-the-air computation (AirComp) emerges as a potential technology. Specifically, AirComp seamlessly integrates the communication and computation procedures through the superposition property of multiple-access channels, which yields a revolutionary multiple-access paradigm shift from "compute-after-communicate" to "compute-when-communicate". By this means, AirComp enables spectral-efficient and low-latency wireless data aggregation by allowing multiple devices to occupy the same channel for transmission. In this paper, we aim to present the recent advancement of AirComp in terms of foundations, technologies, and applications. The mathematical form and communication design are introduced as the foundations of AirComp, and the critical issues of AirComp over different network architectures are then discussed along with the review of existing literature. The technologies employed for the analysis and optimization on AirComp are reviewed from the information theory and signal processing perspectives. Moreover, we present the existing studies that tackle the practical implementation issues in AirComp systems, and elaborate the applications of AirComp in Internet of Things and edge intelligent networks. Finally, potential research directions are highlighted to motivate the future development of AirComp. Copyright © 2022, The Authors. All rights reserved.

关键词： Network architecture

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A real world attack model combining LED modulation and attention-superpixel guidance

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Expert systems with Applications 2025年 293卷

作者： You Jiang Yuqiao Luo Junbin Fang Canjian Jiang Yixuan Shen Yinglong Liao Yujing Sun Siu-Ming Yiu Chuanyi Liu Zoe Lin Jiang Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications Guangdong Provincial Engineering Technology Research Center on Visible Light Communication Guangzhou Municipal Key Laboratory of Engineering Technology on Visible Light Communication and the Department of Optoelectronic Engineering Jinan University Guangzhou 510632 China School of Intelligent Systems Science and Engineering Jinan University Zhuhai 519070 China Department of Computer Science The University of Hong Kong Hong Kong 999077 China School of Computer Science and Technology Harbin Institute of Technology(Shenzhen) Shenzhen 518055 China

Physical adversarial attacks present critical security threats to AI vision systems in real-world scenarios due to their high executability. Optical physical adversarial attacks leverage imaging mechanisms to introduce non-invasive adversarial perturbations but often suffer from weak attack transferability. To address this problem, we propose an attention superpixel guided physical adversarial attack based on LED illumination modulation, which offers the advantages of high transferability and generalization ability. Firstly combines superpixel segmentation and class activation mapping to generate a more accurate attention map. Then, Fuzzy C-means clustering is utilized to adaptively extract the decision regions and to enhance attack transferability. Finally, an LED flashlight implants optical adversarial perturbations, imperceptible to the human eye, using high-speed illumination modulation. We conducted attack experiments on nine models across three tasks: image classification, traffic sign recognition, and face recognition. In digital domain simulations, the average attack success rates for image classification, traffic sign recognition, and face recognition are 94.1 %, 93.4 %, and 100 %, respectively. The transferability is 66.1 %, which is double that of the state-of-the-art optical physical adversarial attack. In physical domain experiments, the average attack success rates in outdoor daylight and nighttime conditions are 90 % and 100 %, respectively.

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