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Contrastive MLP Network Based on Adjacent Coordinates for Cross-Domain Zero-Shot Hyperspectral Image Classification

IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Li, Jiaojiao Zhang, Zhiyuan Song, Rui Xu, Haitao Li, Yunsong Du, Qian Xidian University State Key Laboratory of Integrated Service Networks Xi’an710071 China CAS Key Laboratory of Spectral Imaging Technology Xi’an710119 China Chinese Academy of Sciences National Space Science Center China Mississippi State University Department of Electrical and Computer Engineering StarkvilleMS39762 United States

With the breakthrough of transfer learning and meta-learning, cross-domain few-shot hyperspectral image classification (CDFSL HSIC) technology has recently achieved satisfactory performance under limited annotations. Nevertheless, the most practical applications are zero-shot scenarios, which are intractable for CDFSL technology, such as the extraterrestrial detection scene, where unexplored objects are recognized by scientists to be more valuable for research. To conquer the zero-shot problem under domain shift, a two-stage contrastive MLP network (MAC-CDZS) is proposed, which constitutes a pioneering effort in the cross-domain zero-shot (CDZS) HSIC task. Firstly, given the remarkable performance of MLPs within a diminutive model size and their enhanced capacity for extracting spatial-spectral features of HSIs, the MLP framework has been strategically chosen as the foundational backbone of the first stage in the MAC-CDZS for facilitating efficient feature extraction. Secondly, to alleviate the potential category collapse, the second-stage fine-tuning framework is introduced, which extends the first-stage backbone by incorporating the elaborate adjacent coordinate module and contrastive learning paradigm for more harmonious classification performance. Specifically, the adjacent coordinate module is creatively designed to adequately mine the adjacent coordinates among samples for ameliorating category collapse from the perspective of grasping more reliable priors. Furthermore, a contrastive learning paradigm is innovatively constructed, comprising a Spatial Augmentation (SA) module tailored for hyperspectral patches and a construction strategy of sample pair under zero-shot conditions, which aims to boost the representation capability and alleviate the class collapse. The superior performance of the MAC-CDZS is demonstrated by experimental results on four benchmark datasets. © 2025 IEEE. All rights reserved.

关键词： Zero-shot learning

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ORF-Net: Deep Omni-supervised Rib Fracture Detection from Chest CT Scans

arXiv

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arXiv 2022年

作者： Chai, Zhizhong Lin, Huangjing Luo, Luyang Heng, Pheng-Ann Chen, Hao Imsight AI Research Lab Shenzhen China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Hong Kong

Most of the existing object detection works are based on the bounding box annotation: each object has a precise annotated box. However, for rib fractures, the bounding box annotation is very labor-intensive and time-consuming because radiologists need to investigate and annotate the rib fractures on a slice-by-slice basis. Although a few studies have proposed weakly-supervised methods or semi-supervised methods, they could not handle different forms of supervision simultaneously. In this paper, we proposed a novel omni-supervised object detection network, which can exploit multiple different forms of annotated data to further improve the detection performance. Specifically, the proposed network contains an omni-supervised detection head, in which each form of annotation data corresponds to a unique classification branch. Furthermore, we proposed a dynamic label assignment strategy for different annotated forms of data to facilitate better learning for each branch. Moreover, we also design a confidence-aware classification loss to emphasize the samples with high confidence and further improve the model’s performance. Extensive experiments conducted on the testing dataset show our proposed method outperforms other state-of-the-art approaches consistently, demonstrating the efficacy of deep omni-supervised learning on improving rib fracture detection performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Current-induced Néel order switching facilitated by magnetic phase transition

Current-induced Néel order switching facilitated by magneti...

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Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： Hao Wu Hantao Zhang Baomin Wang Yaqin Guo Ran Cheng Kang L. Wang Songshan Lake Materials Laboratory Dongguan China Department of Electrical and Computer Engineering University of California Los Angeles CA USA Department of Electrical and Computer Engineering University of California Riverside CA USA School of Physical Science and Technology Ningbo University Ningbo China CAS Key Laboratory of Magnetic Materials and Devices Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo China

Antiferromagnetic materials (AFMs), which have zero-stray field and the THz-range magnetic resonance frequency, are promising candidates for the next-generation magnetic memory technology. Here, we report an electrical writing of antiferromagnetic orders through a record-low current density on the order of 10 6 A cm -2 facilitated by the unique AFM-ferromagnetic (FM) phase transition in FeRh. The spin-orbit torque can switch the AFM order parameter by 90° with a reduced writing current density by introducing a transient FM state via current-induced Joule heating. This work demonstrates the exciting possibility of writing operations in AFM-based devices with a lower current density, opening a new pathway toward pure AFM memory applications.

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Deterministic spin-orbit torque switching including the interplay between spin polarization and kagome plane in Mn3Sn

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Physical Review B 2024年第13期109卷 134433-134433页

作者： Zhengde Xu Xue Zhang Yixiao Qiao Gengchiau Liang Shuyuan Shi Zhifeng Zhu School of Information Science and Technology ShanghaiTech University Shanghai 201210 China Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai 200050 China University of Chinese Academy of Sciences Beijing 100049 China Department of Electrical and Computer Engineering National University of Singapore 117576 Singapore Industry Academia Innovation School National Yang-Ming Chiao Tung University Hsinchu City 300093 Taiwan Fert Beijing Institute MIIT Key Laboratory of Spintronics School of Integrated Circuit Science and Engineering Beihang University Beijing 100191 China Shanghai Engineering Research Center of Energy Efficient and Custom AI IC Shanghai 201210 China

Previous research has demonstrated the spin-orbit torque (SOT) switching of Mn3Sn in configuration I, where the spin polarization σ resides within the kagome plane. However, this configuration has yielded several unexpected outcomes, giving rise to debates concerning the fundamental physics governing the switching process. Alternatively, in configuration II, σ is perpendicular to the kagome plane, which bears greater resemblance to the ferromagnetic system. In this study, we show successful SOT switching of Mn3Sn in configuration II, demonstrating behaviors more akin to ferromagnets, e.g., the critical switching current density (Jcrit) and external field (Hext) are in the order of 1010A/m2 and tens of Oersted, respectively. The switching result is also independent of the initial state. We further show that the distinctive spin structure of Mn3Sn leads to unique switching characteristics, including Jcrit increasing linearly with Hext and the opposite switching polarity to ferromagnetism. A switching phase diagram is further provided as a guideline for experimental demonstrations, offering a clear physical picture for the observed phenomena.

关键词： Antiferromagnetism Spin dynamics Spin-orbit torque Antiferromagnets Chiral magnets Landau-Lifschitz-Gilbert equation

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An Intelligent IoT enabled Health Care Surveillance using Machine Learning

An Intelligent IoT enabled Health Care Surveillance using Ma...

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2022 IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2022

作者： Ahamed, Saahirabanu Bhatt, Pankaj Sultanuddin, S.J. Walia, Ranjan Haque, M. Akiful Inayathahamed, S.B. Jazan University Department of Computer Science Saudi Arabia KIET Group of Institutions KIET School of Pharmacy Uttar Pradesh India Measi Institute of Information Technology Department of MCA Chennai India Model Institute of Engineering and Technology Department of Electrical Engineering Jammu India Anurag University Dept. of Pharmacy Medchal India Kalasalingam Business School Kalasalingam Academy of Research and Education Deemed to Be University Tamil Nadu 626126 India

ISBN: (纸本)9781665495295

Over the last decade, health care enabled services and accompanying advanced technology have been the subject of much research. Medical equipment, sensors, and healthcare professionals may all be linked through the Internet of Things (IoT) to deliver high-quality medical services everywhere. Patient safety has improved as a consequence, health care costs have decreased, access to health care has grown, and the sector as a whole has become more efficient and productive. According to recent study, Internet of Things (IoT)-based technologies offer enormous promise for enhancing health care. The most effective data analysis methods now use machine learning-Assisted techniques. Due to the conductivity of the top three layers, intelligent applications and services such as sickness detection, behavioural recognition, and intelligent support may be supplied. This research examined medical IoT improvements in terms of enabling technology, health care services, and implementations to address a variety of health care challenges. Additionally, potential issues and concerns about the Health Care IoT system are explored. As a consequence, future researchers interested in investigating and discovering new applications for health care IoT will have a credible resource for learning about the technology's many applications. © 2022 IEEE.

关键词： Health care

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Safeguarding Smart Vehicles: GNN-Powered Real- Time IDS for CAN Networks

Safeguarding Smart Vehicles: GNN-Powered Real- Time IDS for ...

引用

Machine Intelligence and Smart Innovation (ICMISI), International Conference on

作者： Sarah El-Dawy Abdallah Hussien Mostafa Ammar Ahmed El-Dawy Software Development Engineer S2S Vista Virtual Platforms Siemens EDA Cairo Egypt Electrical and Computer Engineering Department University of Waterloo Waterloo Canada Computer Network and Datacenter Department Arab Academy for Science Technology and Submarine Alexandria Egypt Electrical Power Department University of Alexandria Alexandria Egypt

ISBN: (数字)9798350365740

ISBN: (纸本)9798350365757

With the increasing integration of Controller Area Network (CAN) in modern vehicles, securing the communication network from malicious attacks is imperative. A novel Graph Neural Network (GNN)-based Intrusion Detection System (IDS) is proposed for real-time detection of several attacks, such as Denial of Service (DOS), fuzzy, and spoofing attacks. Our proposed model represents the CAN network as a graph, where nodes correspond to message arbitration ID and edges denote the communication sequence. Using Graph Attention Networks (GAT), our model captures the intricate relationships and dependencies between Electronic Circuit Units (ECUs), in real-time, enhancing its ability to discern normal behavior from malicious activity. The key to our system's success is its emphasis on achieving real-time capabilities. The GAT architecture optimizes computational efficiency for swift analysis of CAN traffic without compromising detection accuracy. Our system minimizes model complexity for enhanced deployability in resource-constrained vehicular environments. Comprehensive experiments on a diverse dataset showcase its ability to swiftly and accurately detect a range of attacks in real-time, with an overall accuracy of 99.75% for DOS attacks, 99.50% for fuzzy attacks, and 100% for spoofing attacks. Moreover, attack detection time is calculated to be 6.76 ms for DOS, 6.79 ms for fuzzy, and 6.69 ms for spoofing. These results made the model meet real-time detection constraints.

关键词： Technological innovation Accuracy Computational modeling Image edge detection Intrusion detection Real-time systems Graph neural networks

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Predicted High-Temperature Superconductivity in Rare Earth Hydride ErH2 at Moderate Pressure

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Chinese Physics Letters 2022年第12期39卷 84-88页

作者： Yiding Liu Qiang Fan Jianhui Yang Lili Wang Weibin Zhang Gang Yao College of Mathematics and Physics Leshan Normal UniversityLeshan 614004China Institute of Atomic and Molecular Physics Sichuan UniversityChengdu 610065China School of New Energy Materials and Chemistry Leshan Normal UniversityLeshan 614004China Institute of Computer Application China Academy of Engineering PhysicsMianyang 621900China College of Physics and Electronics Information Yunnan Key Laboratory of Optoelectronic Information TechnologyYunnan Normal UniversityKunming 650500China School of Physical Science and Technology Southwest UniversityChongqing 400715China Tsung-Dao Lee Institute Shanghai Jiao Tong UniversityShanghai 200240China

Hydrides offer an opportunity to study high critical temperature(high-Tc)superconductivity at experimentally achievable ***,the pressure needed remains extremely *** density functional theory calculations,herein we demonstrate that a new rare earth hydride ErH2could be superconducting with Tc~80 K at 14.5 GPa,the lowest reported value for compressed hydrides to ***,due to Kondo destruction,superconductivity was prone to exist at 15 *** also reveal an energy gap at 20 GPa on the background of normal metallic *** 20 GPa,this compressed system could act as a host of superconductor judged from a sharp jump of spontaneous magnetic susceptibility with an evanescent spin density of state at Fermi ***,electron pairing glue for ErH2at these three typical pressures was attributed to the antiferromagnetic spin fluctuation.

关键词： Rare Kondo superconductor

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Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectories

arXiv

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arXiv 2024年

作者： Yang, Ning Chen, Shuo Zhang, Haijun Berry, Randall The Institute of Automation Chinese Academy of Sciences Beijing100190 China The Department of Electrical and Electronic Engineering Imperial College London LondonSW72BX United Kingdom The Department of Computing and Communication Engineering Beijing University of Science and Technology Beijing100083 China The Department of Electrical and Computer Engineering Northwestern University Chicago60208 United States

Mobile Edge Computing (MEC) broadens the scope of computation and storage beyond the central network, incorporating edge nodes close to end devices. This expansion facilitates the implementation of large-scale "connected things" within edge networks. The advent of applications necessitating real-time, high-quality service presents several challenges, such as low latency, high data rate, reliability, efficiency, and security, all of which demand resolution. The incorporation of reinforcement learning (RL) methodologies within MEC networks promotes a deeper understanding of mobile user behaviors and network dynamics, thereby optimizing resource use in computing and communication processes. This paper offers an exhaustive survey of RL applications in MEC networks, initially presenting an overview of RL from its fundamental principles to the latest advanced frameworks. Furthermore, it outlines various RL strategies employed in offloading, caching, and communication within MEC networks. Finally, it explores open issues linked with software and hardware platforms, representation, RL robustness, safe RL, large-scale scheduling, generalization, security, and privacy. The paper proposes specific RL techniques to mitigate these issues and provides insights into their practical applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Modulating spectral reflectance and its impact on thematic mapping: An analysis on glacier facies using very high-resolution satellite imagery 14

Modulating spectral reflectance and its impact on thematic m...

引用

Earth Resources and Environmental Remote Sensing/GIS Applications XIV 2023

作者： Wankhede, Sagar F. Pandit, Prashant H Jawak, Shridhar D Luis, Alvarinho J Balakrishna, Keshava Department of Civil Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Gogol Goa Margao403602 India Department of Information Engineering and Computer Science University of Trento Trento38123 Italy Institute for Earth Observation Eurac Research Viale Druso 1 Bolzano39100 Italy SIOS Knowledge Centre Svalbard Science Centre P.O. Box 156 Longyearbyen SvalbardN-9171 Norway Ministry of Earth Sciences Government of India Headland Sada Goa Vasco-da-Gama403804 India

ISBN: (纸本)9781510666979

Analysis of the spectral properties of glacier surface facies representing glaciological zones is an established method for characterizing facies. This necessitates a reliable estimation of spectral reflectance from image pixels using appropriate atmospheric correction methods. Improving spatial resolution by pansharpening may improve visual detection of facies, but the translation of visual perception to algorithmic classification is unclear. In this study, we analyzed the changes in spectral reflectance by comparing one model-based and two image-based methods of atmospheric corrections using very high-resolution (VHR) WorldView-2/3 satellite data for selected glaciers in Svalbard and the Himalayas. Two pansharpening methods were then applied followed by 12 machine learning and three object-based image analysis (OBIA) rule sets. A combination of wavelength ranges representing traditional and recent spectrums were tested using four different spectral band combinations. In total, we compared 8100 surface facies maps to understand the impact of every image processing routine and mapping method on the final thematic categorization of facies. Broad conclusions suggest that VHR data does not require shortwave infrared ranges;OBIA is more accurate and less sensitive to image processing variations;machine learning is more efficient but readily influenced by image processing. A spatial-spectral information OBIA ruleset yielded the highest overall accuracy (0.86). Segmentation parameters were found to be consistent across study regions and may be transferable outside the current study. This study demonstrates the need for testing image processing induced variations for cryospheric applications and may be assessed on open satellite data for long-Term standardized mapping of glacier surface facies. © 2023 SPIE. All rights reserved.

关键词： Image analysis

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Energy Landscape Shaping for Robust Control of Atoms in Optical Lattices

arXiv

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arXiv 2025年

作者： Weidner, C.A. O'Neil, S.P. Jonckheere, E.A. Langbein, F.C. Schirmer, S.G. Quantum Engineering Technology Laboratories H. H. Wills Physics Laboratory Department of Electrical and Electronic Engineering University of Bristol BristolBS8 1FD United Kingdom Department of Electrical Engineering and Computer Science United States Military Academy West PointNY10996 United States Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90007 United States School of Computer Science and Informatics Cardiff University CardiffCF24 4AG United Kingdom Faculty of Science & Engineering Physics Swansea University SwanseaSA2 8PP United Kingdom

Robust quantum control is crucial for realizing practical quantum technologies. Energy landscape shaping offers an alternative to conventional dynamic control, providing theoretically enhanced robustness and simplifying implementation for certain applications. This work demonstrates the feasibility of robust energy landscape control in a practical implementation with ultracold atoms. We leverage a digital mirror device (DMD) to shape optical potentials, creating complex energy landscapes. To achieve a desired objective, such as efficient quantum state transfer, we formulate a novel hybrid optimization approach that effectively handles both continuous (laser power) and discrete (DMD pixel activation) control parameters. This approach combines constrained quasi-Newton methods with surrogate models for efficient exploration of the vast parameter space. Furthermore, we introduce a framework for analyzing the robustness of the resulting control schemes against experimental uncertainties. By modeling uncertainties as structured perturbations, we systematically assess controller performance and identify robust solutions. We apply these techniques to maximize spin transfer in a chain of trapped atoms, achieving high-fidelity control while maintaining robustness. Our findings provide insights into the experimental viability of controlled spin transfer in cold atom systems. More broadly, the presented optimization and robustness analysis methods apply to a wide range of quantum control problems, offering a toolkit for designing and evaluating robust controllers in complex experimental settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Robust control

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