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Modulation of Asymmetric Magnetic Domain-Wall Motion via Noncolinear Interlayer Exchange Coupling

IEEE Transactions on Magnetics 2025年第5期61卷

作者： Zhou, Xiangqing Chen, Shiwei Wu, Chuangwen Li, Peizhi Meng, Dequan Tomasello, Riccardo Finocchio, Giovanni Liang, Shiheng Hubei University Key Laboratory for Intelligent Sensing System and Security of Ministry of Education School of Physics Wuhan430062 China Politecnico di Bari Department of Electrical and Information Engineering Bari70125 Italy University of Messina Department of Mathematical and Computer Sciences Physical Sciences and Earth Sciences Messina98166 Italy

The search of scalable approach to design field-free deterministic switching is currently a key challenge. Here, we investigate current and magnetic driven magnetization switching in a T-type magnetic heterojunction with a structure composed by a hybrid synthetic antiferromagnet (SAF) Co/Ta/CoTb/Pt, where the bottom Co layer has in-plane magnetic anisotropy (IMA) and the top CoTb layer has perpendicular magnetic anisotropy (PMA). The interlayer exchange coupling (IEC) interaction allows a tilted easy axis of the perpendicular CoTb layer. The main result achieved is the field-free magnetization switching driven by spin-orbit torque (SOT) with a switching direction (clockwise or counterclockwise), which can be controlled by the in-plane direction of the Co magnetization. Meanwhile, we demonstrate that the IEC also induces the asymmetric bubble expansion in the CoTb layer in field-driven experiments and favors the propagation of the domain walls (DWs) with internal magnetization antiparallel to the in-plane IEC field. Our results demonstrate versatile control of the DW motion by noncollinear IEC, which paves a potential way for designing energy-efficient spintronic memory and logic devices, as well as provides a promising and high-efficiency approach for detecting the IEC type by magneto-optical Kerr effect (MOKE) in T-type magnetic heterojunction. © 1965-2012 IEEE.

关键词： Heterojunctions

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In-situ Gas Sensor of Dissolved Acetylene in Transformer Oil Based on Hollow-core Photonic Bandgap Fiber

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IEEE Transactions on Dielectrics and Electrical Insulation 2025年

作者： Shao, Jianwei Wang, Junhua Jin, Zhanxiang Wang, Ziqing Wang, Chao Pan, Xingya Cai, Changsong Wuhan University Hubei Key Laboratory of Power Equipment and System Security for Integrated Energy School of Electrical Engineering and Automation Wuhan430072 China Shenzhen University of Technology School of Future Technology Shenzhen518118 China Guizhou Electric Power Research Institute of Guizhou Power Grid Co. Ltd Guiyang550002 China

Real-time monitoring of the concentration of characteristic gases can therefore detect abnormal operating conditions of a transformer. This paper proposes an in-situ acetylene detection system based on hollow-core photonic bandgap fibers (HC-PBF) and membrane extraction. Simultaneous oil-gas separation and gas detection are achieved using a double-sided rectangular PTFE gas cell and 5-cm-long hollow core fiber Fabry-Perot interferometers (FPI). The FPI fiber probe exhibits a contrast of 7.06dB. And the saturation equilibrium time for oil-gas separation is 0.8h at 80°C. Furthermore, the in-situ detection system exhibits a linear response to acetylene, with a response coefficient of approximately 0.06916μV/ppm and a high degree of linearity (goodness of fit=0.9951). Moreover, 60s is the optimal averaging time, at which time the noise standard deviation is measured at 0.069V and the detection limit is tested to be 0.973ppm. The sensor, with its compact size, high sensitivity, and lack of power supply, enables in-situ detection in oil and presents a novel approach for online monitoring of dissolved gases in transformer oil. © 1994-2012 IEEE.

关键词： Fabry-Perot interferometers

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Identification of time-varying parameters of a monthly Budyko function in US MOPEX catchments and its implications

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Journal of Hydrology: Regional Studies 2025年 59卷

作者： Liu, Weibo Liu, Pan Cheng, Lei Zhang, Xiaojing Zhou, Liting State Key Laboratory of Water Resources Engineering and Management Wuhan University Wuhan 430072 China Hubei Provincial Key Lab of Water System Science for Sponge City Construction Wuhan University Wuhan 430072 China Research Institute for Water Security (RIWS) Wuhan University Wuhan 430072 China China Yangtze Power Co. Ltd Yichang 443133 China Hubei Key Laboratory of Intelligent Yangtze and Hydroelectric Science Yichang 443133 China

Study region: Model Parameter Estimation Experiment (MOPEX) catchments across the continental United States. Study focus: The Budyko framework provides a simple yet useful method for estimating evapotranspiration (ET), traditionally used at the mean annual scale due to the assumption of constant catchment water storage. Under changing conditions, time-invariant model parameters may not fully capture variations in the underlying surface. Several studies have extended the Budyko framework to the monthly scale;however, few studies have identified monthly time-varying Budyko model parameters and explored the factors driving their variability. This study develops a method for identifying time-varying parameters κ and y₀ of a two-parameter Budyko model using the Ensemble Kalman Filter. Additionally, Extreme Gradient Boosting is used to explore the factors influencing time-varying parameters. New hydrological insights for the study region: Monthly time-varying parameters are effectively identified through simultaneous assimilation of ET and runoff observations, achieving a Kling-Gupta Efficiency of 0.94 in ET simulation. (2) 11 and 2 catchments display change points and trends in parameter κ, respectively, while 64 and 48 catchments exhibit change points and trends in y₀, respectively. (3) Parameter κ is mainly influenced by climatic factors in low-latitude, storage-related factors in high-latitude, and normalized difference vegetation index in central U.S. catchments, respectively. y₀ is mainly influenced by total soil moisture, along with vegetation and agricultural land cover changes in the central region. © 2025 The Authors

关键词： Ensemble Kalman filter Evapotranspiration estimation Monthly Budyko function MOPEX catchments Time-varying parameters

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MMAGL: Multiobjective Multiview Attributed Graph Learning for Joint Clustering of Hyperspectral and LiDAR Data

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Zhang, Zijia Cai, Yaoming Gong, Wenyin Liu, Xiaobo Zeng, Cheng Yu, Gan Hubei University School of Artificial Intelligence Wuhan430078 China Hubei University Key Laboratory of Intelligent Sensing System and Security Ministry of Education Wuhan430078 China Zhongnan University of Economics and Law School of Information Engineering Wuhan430073 China China University of Geosciences School of Computer Science Wuhan430074 China China University of Geosciences School of Automation Wuhan430074 China Fuyang Normal University College of Information Engineering Fuyang236037 China

The joint clustering of multimodal remote sensing (RS) data represents a multiobjective optimization challenge involving conflicting modality-specific objectives and diverse regularization objectives. Current approaches to multiview subspace clustering (MVSC) often oversimplify this task by transforming it into a weighted single-objective optimization problem, neglecting the intricate interactions between objectives and leading to suboptimal subspace representations. The presence of quadratic decision variables in MVSC renders direct application on large-scale RS data impracticable using multiobjective evolutionary algorithms (MOEAs). To overcome this challenge, we propose a novel MVSC method termed multiobjective multiview attributed graph learning (MMAGL). Instead of optimizing every self-representation coefficient individually, our method transforms MVSC into a link prediction task over a sparse attributed graph that fuses different modalities. We incorporate superpixel-based sample reduction and proximity-based population coding, leveraging spatial and structural priors, respectively. This results in a significantly compressed decision space, enabling optimization with MOEAs. To fully exploit node attributes and the graph structure, we redefine self-representation using contrastive learning and introduce an efficient graph filtering (GF) through a generalized spectral graph convolution, enhancing clustering discriminability. The proposed MMAGL constitutes a hybrid and versatile framework, adaptable to any MOEA. Extensive experimental evaluations demonstrate that our MMAGL method surpasses the current state-of-the-art on multimodal RS benchmarks (e.g., with nearly 2% gain on Trento and 3% on Houston) on overall accuracy. © 2025 IEEE.

关键词： Proximity sensors

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Correlation effects on topological structure in complex network evolution

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Physica A: Statistical Mechanics and its Applications 2025年 671卷

作者： Xiaojie Chen Weile Qiao Guijun Pan School of physics Hubei University 430062 Wuhan Hubei China Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education 430062 Wuhan Hubei China

Network growth model is an important and popular research topic aimed at uncovering the mechanisms of network formation and evolution. Most growth models focus on two key mechanisms: growth and preferential attachment without directly accounting for the correlation effects, existing in many realistic network systems. Here, we propose a preferential correlation growth model by introducing a proximity parameter that controls the proximity between nodes connected to newly added nodes. The smaller the proximity parameter, the shorter the distance between the selected nodes, and the stronger the correlation between them. When the proximity parameter approaches infinity, our model reduces to the BA model. By comparing simulations with different proximity parameter, we find that a high degree of correlation significantly enhances the robustness, both local and global efficiency of the network. Besides, our model demonstrates high clustering coefficient, short average path length, and richer cyclic structures, which are closer to real-world networks. Furthermore, our network model effectively addresses the low clustering coefficient issue of the BA model and provides a better understanding of the cyclic structures in real-world networks. Specially, we find that on the mesoscopic time scale, the correlation effects can significantly improve thermodynamic efficiency from a statistical perspective. Our work shows that correlation should be another key mechanism of network formation in addition to the preferential attachment, deserved to be considered for better understanding of the structure and function of the network.

关键词： Growth temperature

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Bandwidth-Efficient Two-Server ORAMs with O(1) Client Storage

arXiv

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arXiv 2025年

作者： Wang, Wei Zhang, Xianglong Xu, Peng Chen, Rongmao Yang, Laurence Tianruo School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Jinyinhu Laboratory Wuhan430040 China School of Computer National University of Defense Technology Changsha410073 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China Department of Computer Science St. Francis Xavier University AntigonishNSB2G 2W5 Canada

Oblivious RAM (ORAM) allows a client to securely retrieve elements from outsourced servers without leakage about the accessed elements or their virtual addresses. Two-server ORAM, designed for secure two-party RAM computation, stores data across two non-colluding servers. However, many two-server ORAM schemes suffer from excessive local storage or high bandwidth costs. To serve lightweight clients, it is crucial for ORAM to achieve concretely efficient bandwidth while maintaining O(1) local storage. Hence, this paper presents two new client-friendly two-server ORAM schemes that achieve practical logarithmic bandwidth under O(1) local storage, while incurring linear symmetric key computations. The core design features a hierarchical structure and a pairwise-area setting for the elements and their tags. Accordingly, we specify efficient read-only and write-only private information retrieval (PIR) algorithms in our schemes to ensure obliviousness in accessing two areas respectively, so as to avoid the necessity of costly shuffle techniques in previous works. We empirically evaluate our schemes against LO13 (TCC’13), AFN17 (PKC’17), and KM19 (PKC’19) in terms of both bandwidth and time cost. The results demonstrate that our schemes reduce bandwidth by approximately 2 ∼ 4× compared to LO13, and by 16 ∼ 64× compared to AFN17 and KM19. For a database of size 214 blocks, our schemes are over 64× faster than KM19, while achieving similar performance to LO13 and AFN17 in the WAN setting, with a latency of around 1 second. Copyright © 2025, The Authors. All rights reserved.

关键词： Virtual addresses

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Dual topology and versatile Rashba-split surface state configurations in 2M−WS2 and 2M−WSe2

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Physical Review B 2025年第11期111卷 115151-115151页

作者： Lixuan Xu Yuqiang Fang Fuqiang Huang Shihao Zhang Shiheng Liang Yulin Chen Lexian Yang Zhongkai Liu Nan Xu Department of Physics Hubei University Wuhan 430062 China Key Laboratory of Intelligent Sensing System and Security (Hubei University) Ministry of Education Wuhan 430062 China School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China School of Physics and Electronics Hunan University Changsha 410082 China School of Physical Science and Technology ShanghaiTech University Shanghai 201210 China ShanghaiTech Laboratory for Topological Physics Shanghai 201210 China Department of Physics University of Oxford Oxford OX1 3PU United Kingdom State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing 100084 China Institute for Advanced Studies (IAS) Wuhan University Wuhan 430072 China Wuhan Institute of Quantum Technology Wuhan 430206 China

The 2M-phase transition metal dichalcogenides have recently attracted intensive research interest due to their rich topological and superconducting phase diagrams. Apart from the topological surface states of 2M−WS2 near Γ that originated from the strong topological order, using angle-resolved photoemission spectroscopy, we discover additional Rashba-split states on the surfaces of both 2M−WS2 and 2M−WSe2, which extend in large momentum-energy regions. First-principles calculations well reproduce these states and attribute them to the weak topological orders. The calculations further indicate that the surface state connecting configurations are tunable under moderate pressure, suggesting that 2M−WS2 and WSe2 are promising platforms to study topological phase transition and explore topological superconductivity.

关键词： Photoacoustic spectroscopy

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Poisoning as a Post-Protection: Mitigating Membership Privacy Leakage From Gradient and Prediction of Federated Models

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Liu, Gaoyang Xu, Tianlong Yang, Yang Abdelmoniem, Ahmed M. Wang, Chen Liu, Jiangchuan Huazhong University of Science and Technology Hubei Key Laboratory of Internet of Intelligence School of Electronic Information and Communications Wuhan 430074 China Simon Fraser University School of Computing Science Burnaby V5A 1S6 BC Canada Ministry of Education Key Laboratory of Intelligent Sensing System and Security China Hubei University School of Artificial Intelligence Wuhan 430062 China Queen Mary University of London School of Electronic Engineering and Computer Science United Kingdom

Federated learning (FL) is a distributed learning paradigm that enables multiple clients to train a unified model without sharing their private data. However, recent works demonstrate that FL models are vulnerable to membership inference attacks (MIAs), which can infer whether a data sample was used to train a given FL model. Existing countermeasures either require far-reaching modifications of FL training process or enforce extra processing in prediction phase, yielding them unlikely to be applied well in practice. In this paper, we design a post-protection mechanism, dubbed P²-Protection, which degrades the inference performance of MIAs by simultaneously poisoning the prediction and gradient of the target FL model to reduce the privacy leakage of training data while keeping the model prediction accuracy. P²-Protection only involves one additional training round to embed the poisoned prediction and gradient into the target FL model, without requiring model retraining or training process modification. We evaluate P²-Protection and compare it with two state-of-the-art defenses against three MIAs on five realistic datasets. Experimental results show that P²-Protection outperforms the existing defenses by offering limited implement overhead and improved utility-privacy trade-off. © 2004-2012 IEEE.

关键词： Federated learning membership inference attack poisoned prediction and gradient

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Tuning Second Chern Number in a Four-Dimensional Topological Insulator by High-Frequency Time-Periodic Driving

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Chinese Physics Letters 2024年第4期41卷 77-89页

作者：刘峥嵘陈锐周斌 Department of Physics Hubei UniversityWuhan 430062China Key Laboratory of Intelligent Sensing System and Security of Ministry of Education Hubei UniversityWuhan 430062China

Floquet engineering has attracted considerable attention as a promising approach for tuning topological phase *** investigate the effects of high-frequency time-periodic driving in a four-dimensional(4D)topological insulator,focusing on topological phase transitions at the off-resonant quasienergy *** 4D topological insulator hosts gapless three-dimensional boundary states,characterized by the second Chern number C_(2).We demonstrate that the second Chern number of 4D topological insulators can be modulated by tuning the amplitude of time-periodic *** includes transitions from a topological phase with C_(2)=±3 to another topological phase with C_(2)=±1,or to a topological phase with an even second Chern number C_(2)=±2,which is absent in the 4D static ***,the approximation theory in the high-frequency limit further confirms the numerical conclusions.

关键词： topological transitions Periodic

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Higher-order topological Anderson insulator on the Sierpiński lattice

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Chinese Physics B 2024年第1期33卷 218-222页

作者：陈焕刘峥嵘陈锐周斌 Department of Physics Hubei UniversityWuhan 430062China Key Laboratory of Intelligent Sensing System and Security of Ministry of Education Hubei UniversityWuhan 430062China

Disorder effects on topological materials in integer dimensions have been extensively explored in recent years. However, its influence on topological systems in fractional dimensions remains unclear. Here, we investigate the disorder effects on a fractal system constructed on the Sierpiński lattice in fractional dimensions. The system supports the second-order topological insulator phase characterized by a quantized quadrupole moment and the normal insulator phase. We find that the second-order topological insulator phase on the Sierpiński lattice is robust against weak disorder but suppressed by strong disorder. Most interestingly, we find that disorder can transform the normal insulator phase to the second-order topological insulator phase with an emergent quantized quadrupole moment. Finally, the disorder-induced phase is further confirmed by calculating the energy spectrum and the corresponding probability distributions.

关键词： fractal system topological insulator

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