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arXiv 2023年

作者： Hong, Danfeng Zhang, Bing Li, Xuyang Li, Yuxuan Li, Chenyu Yao, Jing Yokoya, Naoto Li, Hao Ghamisi, Pedram Jia, Xiuping Plaza, Antonio Gamba, Paolo Benediktsson, Jon Atli Chanussot, Jocelyn Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China College of Resources and Environment University of Chinese Academy of Sciences Beijing100049 China School of Mathematics Southeast University Nanjing210096 China Graduate School of Frontier Sciences the University of Tokyo Chiba277–8561 Japan Geospatial Data Management Technical University of Munich Munich85521 Germany Helmholtz-Zentrum Dresden-Rossendorf Helmholtz Institute Freiberg for Resource Technology Freiberg09599 Germany Institute of Advanced Research in Artificial Intelligence Vienna1030 Austria School of Engineering and Information Technology University of New South Wales CanberraACT2612 Australia Hyperspectral Computing Laboratory Department of Technology of Computers and Communications Escuela Politécnica University of Extremadura Cáceres10003 Spain Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia27100 Italy Faculty of Electrical and Computer Engineering University of Iceland Reykjavik102 Iceland Univ. Grenoble Alpes Inria CNRS Grenoble INP LJK Grenoble38000 France

The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner. While most foundation models are tailored to effectively process RGB images for various visual tasks, there is a noticeable gap in research focused on spectral data, which offers valuable information for scene understanding, especially in remote sensing (RS) applications. To fill this gap, we created for the first time a universal RS foundation model, named SpectralGPT, which is purpose-built to handle spectral RS images using a novel 3D generative pretrained transformer (GPT). Compared to existing foundation models, SpectralGPT 1) accommodates input images with varying sizes, resolutions, time series, and regions in a progressive training fashion, enabling full utilization of extensive RS big data;2) leverages 3D token generation for spatial-spectral coupling;3) captures spectrally sequential patterns via multi-target reconstruction;4) trains on one million spectral RS images, yielding models with over 600 million parameters. Our evaluation highlights significant performance improvements with pretrained SpectralGPT models, signifying substantial potential in advancing spectral RS big data applications within the field of geoscience across four downstream tasks: single/multi-label scene classification, semantic segmentation, and change detection. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Sequential resource access: Theory and algorithm

arXiv

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arXiv 2020年

作者： Chen, Lin Giovanidis, Anastasios Wang, Wei Shan, Lin School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Sorbonne University CNRS-LIP6 Paris France College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Dept. of Electrical and Computer Engineering Stony Brook University Stony BrookNY United States Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

We formulate and analyze a generic sequential resource access problem arising in a variety of engineering fields, where a user disposes a number of heterogeneous computing, communication, or storage resources, each characterized by the probability of successfully executing the user’s task and the related access delay and cost, and seeks an optimal access strategy to maximize her utility within a given time horizon, defined as the expected reward minus the access cost. We develop an algorithmic framework on the (near-)optimal sequential resource access strategy. We first prove that the problem of finding an optimal strategy is NP-hard in general. Given the hardness result, we present a greedy strategy implementable in linear time, and establish the closed-form sufficient condition for its optimality. We then develop a series of polynomial-time approximation algorithms achieving (∊, δ)-optimality, with the key component being a pruning process eliminating dominated strategies and, thus maintaining polynomial time and space overhead. © 2020, CC BY-SA.

关键词： Polynomial approximation

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Anticipative Tracking with the Short-Term Synaptic Plasticity of Spintronic Devices

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Physical Review Applied 2020年第4期14卷 044060-044060页

作者： Qi Zheng Yuanyuan Mi Xiaorui Zhu Zhe Yuan Ke Xia Center for Advanced Quantum Studies and Department of Physics Beijing Normal University Beijing 100875 China Center for Quantum Computing Peng Cheng Laboratory Shenzhen 518005 China Center for Neurointelligence School of Medicine Chongqing University Chongqing 400044 China Center for Artificial Intelligence Peng Cheng Laboratory Shenzhen 518005 China Beijing Computational Science Research Center Beijing 100193 China Shenzhen Institute for Quantum Science and Engineering and Department of Physics Southern University of Science and Technology Shenzhen 518055 China

Real-time tracking of high-speed objects in cognitive tasks is challenging in the present artificial intelligence techniques because the data processing and computation are time consuming, resulting in impeditive time delays. A brain-inspired continuous attractor neural network (CANN) can be used to track fast moving targets, where the time delays are intrinsically compensated if the dynamical synapses in the network have short-term plasticity. Here, we show that synapses with short-term depression can be realized by a magnetic tunnel junction, which perfectly reproduces the dynamics of the synaptic weight in a widely applied mathematical model. Then, these dynamical synapses are incorporated into one-dimensional and two-dimensional CANNs, which are demonstrated to have the ability to predict a moving object via micromagnetic simulations. This portable spintronics-based hardware for neuromorphic computing needs no training and is therefore very promising for the tracking technology of moving targets.

关键词： Spintronics Synapses Vision Artificial neural networks Magnetic tunnel junctions Micromagnetic modeling

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Syntax-enhanced Pre-trained model

arXiv

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arXiv 2020年

作者： Xu, Zenan Guo, Daya Tang, Duyu Su, Qinliang Shou, Linjun Gong, Ming Zhong, Wanjun Quan, Xiaojun Jiang, Daxin Duan, Nan School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Microsoft Research Asia Beijing China Microsoft Search Technology Center Asia Beijing China Guangdong Key Laboratory Big Data Analysis and Processing Guangzhou China Key Lab. of Machine Intelligence and Advanced Computing Ministry of Education China

We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa. Existing methods utilize syntax of text either in the pre-training stage or in the fine-tuning stage, so that they suffer from discrepancy between the two stages. Such a problem would lead to the necessity of having human-annotated syntactic information, which limits the application of existing methods to broader scenarios. To address this, we present a model that utilizes the syntax of text in both pre-training and fine-tuning stages. Our model is based on Transformer with a syntax-aware attention layer that considers the dependency tree of the text. We further introduce a new pre-training task of predicting the syntactic distance among tokens in the dependency tree. We evaluate the model on three downstream tasks, including relation classification, entity typing, and question answering. Results show that our model achieves state-of-the-art performance on six public benchmark datasets. We have two major findings. First, we demonstrate that infusing automatically produced syntax of text improves pre-trained models. Second, global syntactic distances among tokens bring larger performance gains compared to local head relations between contiguous tokens. Copyright © 2020, The Authors. All rights reserved.

关键词： Syntactics

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A Node Rating Based Sharding Scheme for Blockchain

A Node Rating Based Sharding Scheme for Blockchain

引用

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Jianrong Wang Yangyifan Zhou Xuewei Li Tianyi Xu Tie Qiu College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Advanced Networking Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Tianjin International Engineering Institute Tianjin University Tianjin China

ISBN: (纸本)9781728125848

The incumbent sharding schemes usually assign the nodes to different committees randomly to meet the demands of security and efficiency at the same time. For example, Elastico protocol obtains a random value by letting the node perform proof of work, and then uses this value for sharding. However, the strategy of random sharding ignores the objective differences between nodes, causing performance gaps between different committees in blockchain. This creates a bottleneck in the transaction throughput of the blockchain. In the paper, we propose a node rating based sharding scheme for blockchain system called NRSS. The key idea of NRSS is to evaluate nodes in the network by both the speeds and results of transactions verification before, and then assign them into different committees by balancing the score to reduce the performance gap between committees and increase the speed of transaction process. We implement NRSS in a local blockchain system, and the experiment results show that NRSS can increase the sharding effect of a blockchain, with an average throughput increase of 32.2% in the simulation environment where the node performance difference is up to 75%, depending on the number of nodes in the committee that are preset in the blockchain.

关键词： Blockchain Sharding technology Distributed system Performance rating Performance appraisal Distributed Systems Committees Ganglia Simulated Environment Line of nodes

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Nonreciprocal transport in a bilayer of MnBi2Te4 and Pt

arXiv

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arXiv 2022年

作者： Ye, Chen Xie, Xiangnan Lv, Wenxing Huang, Ke Yang, Allen Jian Jiang, Sicong Liu, Xue Zhu, Dapeng Qiu, Xuepeng Tong, Mingyu Zhou, Tong Hsu, Chuang-Han Chang, Guoqing Lin, Hsin Li, Peisen Yang, Kesong Wang, Zhenyu Jiang, Tian Wang, Xiao Renshaw Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 21 Nanyang Link 637371 Singapore State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Physics Laboratory Industrial Training Center Shenzhen Polytechnic Guangdong Shenzhen518055 China Department of NanoEngineering Program of Chemical Engineering University of California La Jolla San DiegoCA92093-0448 United States Program of Materials Science and Engineering University of California La Jolla San DiegoCA92093-0418 United States Institutes of Physical Science and Information Technology Anhui University Hefei230601 China Fert Beijing Institute MIIT Key Laboratory of Spintronics School of Integrated Circuit Science and Engineering Beihang University Beijing100191 China Beihang-Goertek Joint Microelectronics Institute Qingdao Research Institute Beihang University Qingdao266000 China Shanghai Key Laboratory of Special Artificial Microstructure Materials and Technology School of Physics Science and Engineering Tongji University Shanghai200092 China College of Advanced Interdisciplinary Studies National University of Defense Technology Changsha410073 China Insitute of Physics Academia Sinica Taipei115229 Taiwan College of Intelligence Science and Technology National University of Defense Technology Changsha410073 China National Innovation Institute of Defense Technology Academy of Military Sciences PLA China Beijing100010 China Beijing Academy of Quantum Information Sciences Beijing100084 China Beijing Institute for Advanced Study National University of Defense Technology Changsha410073 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Ave 639798 Singapore

MnBi2Te4 (MBT) is the first intrinsic magnetic topological insulator with the interaction of spin-momentum locked surface electrons and intrinsic magnetism, and it exhibits novel magnetic and topological phenomena. Recent studies suggested that the interaction of electrons and magnetism can be affected by the Mn-doped Bi2Te3 phase at the surface due to inevitable structural defects. Here we report an observation of nonreciprocal transport, i.e. current-direction-dependent resistance, in a bilayer composed of antiferromagnetic MBT and nonmagnetic Pt. The emergence of the nonreciprocal response below the Néel temperature confirms a correlation between nonreciprocity and intrinsic magnetism in the surface state of MBT. The angular dependence of the nonreciprocal transport indicates that nonreciprocal response originates from the asymmetry scattering of electrons at the surface of MBT mediated by magnon. Our work provides an insight into nonreciprocity arising from the correlation between magnetism and Dirac surface electrons in intrinsic magnetic topological insulators. © 2022, CC BY.

关键词： Magnetism

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RIS-assisted SATINs with RSMA and DRL: A Trade-off between Spectral, Secrecy, and Energy Efficiency

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IEEE Transactions on Communications 2025年

作者： Wu, Min Guo, Kefeng Li, Xingwang Lin, Zhi Yang, Liang Tsiftsis, Theodoros A. Yuen, Chau Beihang University Institute of Artificial Intelligence Beijing100191 China Beihang University Bejing Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing100191 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing210016 China Space Engineering University School of Space Information Beijing101407 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454003 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China National University of Defense Technology College of Electronic Engineering China Anhui Province Key Laboratory of Electronic Restriction Hefei230037 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China University of Nottingham Ningbo China Department of Electrical and Electronic Engineering Ningbo315100 China University of Thessaly Department of Informatics and Telecommunications Lamia35100 Greece Nanyang Technological University School of Electrical and Electronics Engineering 50 Nanyang Ave 639798 Singapore

Given the rapid growth of diverse communication demands, future large-scale satellite-aerial-terrestrial integrated networks (SATINs) need to simultaneously provide services to users while guaranteeing spectral efficiency, secrecy and energy efficiency. This paper addresses the problem of maximising secrecy energy efficiency (SEE) in SATINs, which can accurately describe the effective trade-off between security, spectral efficiency and transmit power. Particularly, we investigate a secure beamforming (BF) scheme in cognitive SATINs that employs rate-splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) in the presence of multiple eavesdroppers (Eves) in a UAV-aided secondary network (SN). To optimize the SEE for secondary vehicle users while satisfying the constraints of primary users (PUs), we utilize deep reinforcement learning (DRL) to address the coupling between different optimized parameters based on the improved long short-term memory proximal policy optimization (LSTM-PPO) algorithm. The main innovation of this paper is to design a sophisticated reward function, action space, and state space according to each constraint to speed up the convergence. In addition, simulation results show that the proposed DRL-based optimization scheme exhibits significant advantages in terms of SEE compared with benchmark schemes, validating the effectiveness of this work. © 1972-2012 IEEE.

关键词： Reinforcement learning

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RCGA-Net: An Improved Multi-hybrid Attention Mechanism Network in Biomedical Image Segmentation

RCGA-Net: An Improved Multi-hybrid Attention Mechanism Netwo...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Feng Xiao Cong Shen Yu Chen Tian Yang Shengyong Chen Zhijun Liao Jijun Tang School of Computer Science and Engineering Tianjin University of Technology Tianjin P.R.China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin P.R.China School of Basic Medical Sciences Fujian Medical University Fuzhou Fujian P.R.China College of Intelligence and Computing Tianjin University Tianjin P.R.China Key Laboratory of Systems Bioengineering Ministry of Education Tianjin P.R.China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong P.R.China University of South Carolina Columbia SC U.S.A.

ISBN: (纸本)9781665429825

Drawing support from an effective Medical Image Segmentation (MIS) is conducive to a substantial diagnostic basis for the physicians to identify the focus lesion in the patient body and give the subsequent clinical assessment of the patient status. Although various works have tried the challenging quantitative analysis problem, it is still difficult to conduct precise automatic segmentation, especially the soft tissue organs. In this decade, with the increased amount of available datasets, deep learning-based networks have achieved remarkable performance in image processing. Inspired by the state-of-the-art deep learning works, in this paper, we propose an end-to-end multi-layer network named RCGA-Net. It consists of an encoder-decoder backbone that integrates a coordinate attention mechanism based on space and channel and a global context extraction module to highlight more valuable information. To evaluate the performance of RCGA-Net, we apply it to different kinds of clinical and experimental MIS tasks to testify its generalization ability. Extensive experiments represent that our schema has taken the outperform or compatible results among the comparison methods group. Specifically, the numeric result of RCGA-Net on the pulmonary dataset has achieved a 99.12% optimum F1-score.

关键词： Deep learning Image segmentation Statistical analysis Lung Predictive models Feature extraction Generative adversarial networks

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Physical Layer Security in Untrusted Decode-and-Forward Relay Networks Allowing Intra-Link Errors

Physical Layer Security in Untrusted Decode-and-Forward Rela...

引用

International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Xingjian Pan Shuxin Ge Xiaobo Zhou Zhao Wang Tianjin Key Laboratory of Advanced Networking School of Computer Science and Technology College of Intelligence and Computing Tianjin University Tianjin China Graduate school of Civil engineering Tianjin University Tianjin China

ISBN: (数字)9781728152127

ISBN: (纸本)9781728152134

In this paper, we investigate the physical layer security performance of untrusted decode-and-forward (DF) relay networks allowing intra-link errors with cooperative jamming. Different from traditional DF relaying protocol, the decoded message at the relay will always be forwarded to the destination node in decode-and-forward relaying allowing intra-link errors (DF-IE), which can be leveraged to enhance the security of relay networks with untrusted relays. To further improve the security performance of DF-IE, we allow the destination node to send jamming signals to impair the signal reception at the relay node, which is referred to as DF-IE with cooperative jamming (DF-IE-CJ). Reliable-and-secure probability (RSP) is used to evaluate the performance of the untrusted relay networks, which represents the probability that the destination node can successfully decode the original message sent from the source node while the relay node can not decode the original message. First, the RSP of DF-IE-CJ is derived from a fading scenario where all the channels between the nodes suffer from block Rayleigh fading. Next, we propose a power allocation scheme to maximize the RSP with different signal-to-noise ratios (SNRs). To verify the effectiveness of DF-IE-CJ, we compare the RSP performance of DF-IE-CJ with that of a widely used schemes, cooperative jamming (CJ). A series of numerical simulations verified the accuracy of the theoretical analysis, and the superiority of DF-IE-CJ over CJ about 5% in RSP.

关键词： Protocols Jamming Reliability Relay networks (telecommunications) Physical layer Security

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Transformative Effects of ChatGPT on Modern Education: Emerging Era of AI Chatbots

arXiv

引用

arXiv 2023年

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, was released to provide coherent and useful replies based on analysis of large volumes of data. In this article, leading scientists, researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research seeks to improve our knowledge of ChatGPT capabilities and its use in the education sector, identifying potential concerns and challenges. Our preliminary evaluation concludes that ChatGPT performed differently in each subject area including finance, coding and maths. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within Generative AI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023, CC0.

关键词： Machine learning

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