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arXiv 2020年

作者： Zhu, Jianchen Zhang, Tong Zhao, Shengjie Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China School of Software Engineering Tongji University Shanghai201804 China School of Electronic and Information Engineering Tongji University Shanghai201804 China

This paper aims at developing a clustering approach with spectral images directly from the compressive measurements of coded aperture snapshot spectral imager (CASSI). Assuming that compressed measurements often lie approximately in low dimensional subspaces corresponding to multiple classes, state of the art methods generally obtains optimal solution for each step separately but cannot guarantee that it will achieve the globally optimal clustering results. In this paper, a low-rank subspace representation (LRSR) algorithm is proposed to perform clustering on the compressed measurements. In addition, a subspace structured norm is added into the objective of low-rank representation problem exploiting the fact that each point in a union of subspaces can be expressed as a sparse linear combination of all other points and that the matrix of the points within each subspace is low rank. Simulation with real dataset illustrates the accuracy of the proposed spectral image clustering approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Spectroscopy

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Social Emergency Event Judgement based on BiLSTM-CRF 19

Social Emergency Event Judgement based on BiLSTM-CRF

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19th Chinese National Conference on Computational Linguistic, CCL 2020

作者： Hu, Huijun Wang, Cong Dai, Jianhua Liu, Maofu School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China

In recent years, classification and rating of social emergency event have attracted more and more attentions in emergency management. However, most of the current studies adopt the rule-based methods to identify the evidences for event judgement, and have troubles in event judgement due to the complexity of social emergency event composition and the flexibility of language description. Inspired by the idea of event extraction, this paper has proposed the event judgement method via BiLSTM (Bidirectional Long-Short Term Memory) and CRF (Conditional Radom Fields) based on event classification and evidence extraction. The social emergency event classification is carried out firstly, and then the event evidences are extracted based on event type. In the end, the rating of social emergency event is judged with the attention mechanism and the combination of event type and evidence. Experimental results show that the proposed method is more robust than rule-based ones, and effective in the social emergency event judgement. © 2020 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License

关键词： Extraction

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Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models

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IEEE Transactions on Vehicular technology 2025年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Mingzhe Quek, Tony Q. S. Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL United States Singapore University of Technology and Design Dept. of Information Systems Technology and Design 487372 Singapore

Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements. © 1967-2012 IEEE.

关键词： NP-hard

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Hierarchical Distributed Frequency Regulation Strategy of Electric Vehicle Cluster Considering Demand Charging Load Optimization

Hierarchical Distributed Frequency Regulation Strategy of El...

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IEEE Student Conference on Electric Machines and systems

作者： Xiaosong Deng Qian Zhang Yan Li Tao Sun Huanzhan Yue State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University Chongqing China Customer Service Centre State Grid Chongqing Electric Power Company Chongqing China

ISBN: (数字)9781728156224

ISBN: (纸本)9781728156231

In view of the problems that the disorderly demand charging load increases the power supply pressure and the control of large-scale cluster is difficult, which will appear during the process of large-scale electric vehicle cluster participating in frequency regulation, this paper proposes a hierarchical distributed frequency regulation strategy of electric vehicle cluster considering the optimization of demand charging load. Firstly, the demand charging plans of electric vehicles are optimized to minimize the system load variance, and the reserve capacity of electric vehicle cluster is predicted in real-time. Then, in order to improve the control performance of large-scale cluster, a hierarchically distributed control framework is established, which enables the traditional unit to assist the electric vehicle through the real-time assignment of system frequency regulation task, and realizes the individual-level orderly scheduling of electric vehicles based on the distributed sorting. The simulation results show that the proposed frequency regulation strategy can not only adjust automatically for ensuring expected frequency regulation effect, but also reduce the power supply pressure on the grid side, as well as achieve efficient and reliable scheduling of electric vehicle cluster.

关键词： Power supplies Process control Electric vehicles Real-time systems Frequency control Optimization Sorting

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Core-Sheath Structured Yarn for Biomechanical Sensing in Health Monitoring

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Biomimetics (Basel, Switzerland) 2025年第5期10卷 304页

作者： Wenjing Fan Cheng Li Bingping Yu Te Liang Junrui Li Dapeng Wei keyu Meng School of Instrumentation and Optoelectronic Engineering Beihang University Beijing 100191 China. College of Metrology Measurement and Instrument China Jiliang University Hangzhou 310018 China. Chongqing Key Laboratory of Generic Technology and System of Service Robots Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing 400714 China. School of Electronic and Information Engineering Changchun University Changchun 130022 China.

The rapidly evolving field of functional yarns has garnered substantial research attention due to their exceptional potential in enabling next-generation electronic textiles for wearable health monitoring, human-machine interfaces, and soft robotics. Despite notable advancements, the development of yarn-based strain sensors that simultaneously achieve high flexibility, stretchability, superior comfort, extended operational stability, and exceptional electrical performance remains a critical challenge, hindered by material limitations and structural design constraints. Here, we present a bioinspired, hierarchically structured core-sheath yarn sensor (CSSYS) engineered through an efficient dip-coating process, which synergistically integrates the two-dimensional conductive MXene nanosheets and one-dimensional silver nanowires (AgNWs). Furthermore, the sensor is encapsulated using a yarn-based protective layer, which not only preserves its inherent flexibility and wearability but also effectively mitigates oxidative degradation of the sensitive materials, thereby significantly enhancing long-term durability. Drawing inspiration from the natural architecture of plant stems-where the inner core provides structural integrity while a flexible outer sheath ensures adaptive protection-the CSSYS exhibits outstanding mechanical and electrical performance, including an ultralow strain detection limit (0.05%), an ultrahigh gauge factor (up to 744.45), rapid response kinetics (80 ms), a broad sensing range (0-230% strain), and exceptional cyclic stability (>20,000 cycles). These remarkable characteristics enable the CSSYS to precisely capture a broad spectrum of physiological signals, ranging from subtle arterial pulsations and respiratory rhythms to large-scale joint movements, demonstrating its immense potential for next-generation wearable health monitoring systems.

关键词： MXene functional yarns health monitoring strain sensor wearable electronics

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Record High Temperatures in the Ocean in 2024

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Advances in Atmospheric Sciences 2025年第6期 1092-1109页

作者： Lijing CHENG John ABRAHAM Kevin E.TRENBERTH James REAGAN Huai-Min ZHANG Andrea STORTO Karina VON SCHUCKMANN Yuying PAN Yujing ZHU Michael E.MANN Jiang ZHU Fan WANG Fujiang YU Ricardo LOCARNINI John FASULLO Boyin HUANG Garrett GRAHAM Xungang YIN Viktor GOURETSKI Fei ZHENG Yuanlong LI Bin ZHANG Liying WAN Xingrong CHEN Dakui WANG Licheng FENG Xiangzhou SONG Yulong LIU Franco RESEGHETTI Simona SIMONCELLI Gengxin CHEN Rongwang ZHANG Alexey MISHONOV Zhetao TAN Wangxu WEI Huifeng YUAN Guancheng LI Qiuping REN Lijuan CAO Yayang LU Juan DU Kewei LYU Albertus SULAIMAN Michael MAYER Huizan WANG Zhanhong MA Senliang BAO Henqian YAN Zenghong LIU Chunxue YANG Xu LIU Zeke HAUSFATHER Tanguy SZEKELY Flora GUES National Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric PhysicsChinese Academy of Sciences University of St. Thomas School of Engineering NSF National Center for Atmospheric Research University of Auckland National Oceanic and Atmospheric Administration National Centers for Environmental Information National Research Council (CNR) Institute of Marine Sciences (ISMAR) Mercator Ocean International Department of Earth and Environmental Science University of Pennsylvania Institute of Oceanology Chinese Academy of Sciences National Marine Environmental Forecasting Center Ministry of Natural Resources of China North Carolina Institute for Climate Studies (NCICS) North Carolina State University Oceanographic Data Center Chinese Academy of Sciences College of Oceanography Hohai University National Marine Data and Information Service Istituto Nazionale di Geofisica eVulcanologia Sede di Bologna South China Sea Institute of Oceanology Chinese Academy of Sciences ESSIC/CISESS-MD University of Maryland Computer Network Information Center Chinese Academy of Sciences Eco-Environmental Monitoring and Research Center Pearl River Valley and South China Sea Ecology and Environment Administration Ministry of Ecology and Environment National Meteorological Information Center China Meteorological Administration International Research Center of Big Data for Sustainable Development Goals Xiamen University Research Center for Climate and Atmosphere National Research and Innovation Agency (BRIN) Research Department European Centre for Medium-Range Weather Forecasts Department of Meteorology and Geophysics University of Vienna College of Meteorology and Oceanography National University of Defense Technology State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of OceanographyMinistry of Natural Resources Stripe Inc. South San Francisco Ocean Scope CELAD

Heating in the ocean has continued in 2024 in response to increased greenhouse gas concentrations in the atmosphere,despite the transition from an El Ni?o to neutral conditions. In 2024, both global sea surface temperature(SST) and upper2000 m ocean heat content(OHC) reached unprecedented highs in the historical record. The 0–2000 m OHC in 2024exceeded that of 2023 by 16 ± 8 ZJ(1 Zetta Joules = 1021 Joules, with a 95% confidence interval)(IAP/CAS data), which is confirmed by two other data products: 18 ± 7 ZJ(CIGAR-RT reanalysis data) and 40 ± 31 ZJ(Copernicus Marine data,updated to November 2024). The Indian Ocean, tropical Atlantic, Mediterranean Sea, North Atlantic, North Pacific, and Southern Ocean also experienced record-high OHC values in 2024. The global SST continued its record-high values from2023 into the first half of 2024, and declined slightly in the second half of 2024, resulting in an annual mean of 0.61°C ±0.02°C(IAP/CAS data) above the 1981–2010 baseline, slightly higher than the 2023 annual-mean value(by 0.07°C ±0.02°C for IAP/CAS, 0.05°C ± 0.02°C for NOAA/NCEI, and 0.06°C ± 0.11°C for Copernicus Marine). The record-high values of 2024 SST and OHC continue to indicate unabated trends of global heating.

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Undoped Strained Ge Quantum Well with Ultrahigh Mobility Grown by Reduce Pressure Chemical Vapor Deposition

arXiv

引用

arXiv 2022年

作者： Kong, Zhenzhen Li, Zonghu Cao, Gang Su, Jiale Zhang, Yiwen Liu, Jinbiao Liu, Jingxiong Ren, Yuhui Wei, Laiming Guo, Guoping Wu, Yuanyuan Radamson, Henry H. Li, Junfeng Wu, Zhenhua Li, Haiou Yang, Jiecheng Zhao, Chao Wang, Guilei Integrated Circuit Advanced Process R&D Center Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China School of Integrated Circuits University of Chinese Academy of Sciences Beijing100049 China CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei230026 China Hefei National Laboratory Hefei230088 China School of Advanced Manufacturing Engineering Hefei University Hefei230601 China Origin Quantum Computing Company Limited Hefei230026 China Research and Development Center of Optoelectronic Hybrid IC Guangdong Greater Bay Area Institute of Integrated Circuit and System Guangzhou510535 China Beijing Superstring Academy of Memory Technology 100176 China

We fabricate an undoped Ge quantum well under a 30 nm relaxed Ge0.8Si0.2 shallow barrier. The bottom barrier contains Ge0.8Si0.2 (650 ℃) followed by Ge0.9Si0.1 (800 ℃) such that variation of Ge content forms a sharp interface that can suppress the threading dislocation density (TDD) penetrating into the undoped Ge quantum well. The Ge0.8Si0.2 barrier introduces enough in-plane parallel strain (Ε strain -0.41%) in the Ge quantum well. The heterostructure field-effect transistors with a shallow buried channel obtain an ultrahigh two-dimensional hole gas (2DHG) mobility over 2×106 cm2/Vs and a very low percolation of 5.625 × 1010cm−2. A tunable fractional quantum Hall effect at high densities and high magnetic fields has also been observed. This approach defines strained germanium as providing the material basis for tuning the spin-orbit coupling strength for fast and coherent quantum *** Codes 81-05, 81V70 © 2022, CC BY-NC-ND.

关键词： Germanium

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Unsupervised feature selection via adaptive embedding and sparse learning for Parkinson’s disease diagnosis 1

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3rd International Workshop on Connectomics in NeuroImaging, CNI 2019 held in conjunction with the 22nd International Conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2019

作者： Huang, Zhongwei Lei, Haijun Chen, Guoliang Li, Shiqi Li, Hancong Elazab, Ahmed Lei, Baiying Key Laboratory of Service Computing and Applications Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen518060 China

ISBN: (数字)9783030323912

ISBN: (纸本)9783030323905

Parkinson’s disease (PD) is known as a progressive neurodegenerative disease in elderly people. Apart from decelerating the disease exacerbation, early and accurate diagnosis also alleviates mental and physical sufferings and provides timely and appropriate medication. In this paper, we propose an unsupervised feature selection method via adaptive manifold embedding and sparse learning exploiting longitudinal multimodal neuroimaging data for classification and regression prediction. Specifically, the proposed method simultaneously carries out feature selection and dynamic local structure learning to obtain the structural information inherent in the neuroimaging data. We conduct extensive experiments on the publicly available Parkinson’s progression markers initiative (PPMI) dataset to validate the proposed method. Our proposed method outperforms other state-of-the-art methods in terms of classification and regression prediction performance. © Springer Nature Switzerland AG 2019.

关键词： Neuroimaging

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Spin-induced orbital frustration in a hexagonal optical lattice

arXiv

引用

arXiv 2021年

作者： Li, Yongqiang Yuan, Jianmin Zhou, Xiaoji Li, Xiaopeng Department of Physics National University of Defense Technology Changsha410073 China Department of Physics Graduate School of China Academy of Engineering Physics Beijing100193 China State Key Laboratory of Advanced Optical Communication System and Network Department of Electronics Peking University Beijing100871 China State Key Laboratory of Surface Physics Institute of Nanoelectronics and Quantum Computing Department of Physics Fudan University Shanghai200438 China Shanghai Qi Zhi Institute AI Tower Xuhui District Shanghai200232 China

Complex lattices provide a versatile ground for fascinating quantum many-body physics. Here, we propose an exotic mechanics for generating orbital frustration in hexagonal lattices. We study two-component (pseudospin-1/2) Bose gases in p-orbital bands of two-dimensional hexagonal lattices, and find that the system exhibits previously untouched orbital frustration as a result of the interplay of spin and orbital degrees of freedom, in contrast to normal Ising-type orbital ordering of spinless porbital band bosons in two-dimensional hexagonal lattices. Based on the classification by symmetry analysis, we find the interplay of orbital frustration and strong interaction leads to exotic Mott and superfluid phases with spin-orbital intertwined orders, in spite of the complete absence of spin-orbital interaction in the Hamiltonian. Our study implies many-body correlations in a multi-orbital setting could induce rich spin-orbital intertwined physics in complex lattice structures. Copyright © 2021, The Authors. All rights reserved.

关键词： Bosons

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Large language model-driven probabilistic trajectory prediction in the Internet of Things using spatio-temporal encoding and normalizing flows

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Digital Communications and Networks 2025年

作者： Xiaoliang Wang Liming Xu Lian Zhou Yuzhen Liu Neal Xiong Kuan-Ching Li School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan 411201 China Sanya Institute of Hunan University of Science and Technology Sanya 572024 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan 411201 China Department of Computer Mathematical and Physical Sciences Sul Ross State University Alpine TX 79830 USA Dept of Computer Science and Information Engineering Providence University Taichung 43301 Taiwan Republic of China

The convergence of the Internet of Things (IoT) and foundation models, particularly Large Language Models (LLMs), heralds a new era of intelligent and adaptive systems. This investigation explores the integration of LLMs and IoT for probabilistic vehicle trajectory prediction, a critical component in autonomous driving. Traditional deterministic models often fail to capture the complexity and uncertainty of real-world driving environments. To address these limitations, we propose an innovative approach that leverages LLM-driven spatio-temporal encoding and normalizing flows. The proposed method extracts latent motion patterns from historical trajectory data using LLMs, providing a nuanced understanding of vehicle dynamics. Advanced spatio-temporal encoders are employed for comprehensive feature extraction, capturing temporal dependencies and spatial relationships. Additionally, scaled dot-product attention is utilized for effective multimodal feature fusion, enhancing the integration of diverse data sources. The normalizing flows framework enhances the model's ability to capture complex, multimodal probability distributions of future trajectories by constructing an invertible transformation network. The network systematically maps simple base distributions to intricate target distributions through reversible transformations, significantly improving prediction accuracy. Evaluation of the nuScenes dataset demonstrates substantial improvements over existing methods. The results underscore the model's capability to generate diverse and realistic trajectory distributions, paving the way for safer and more efficient autonomous driving systems.

关键词： Internet of things Large language models Normalizing flows Spatio-temporal encoding Trajectory prediction

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