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A metasurface-based light-to-microwave transmitter for hybrid wireless communications

Light(science & Applications) 2022年第6期11卷 1139-1148页

作者： Xin Ge Zhang Ya Lun Sun Bingcheng Zhu Wei Xiang Jiang Qian Yu Han Wei Tian Cheng-Wei Qiu Zaichen Zhang Tie Jun Cui State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast University210096 NanjingChina National Mobile Communications Research Laboratory School of Information Science and EngineeringSoutheast University210096 NanjingChina Frontiers Science Center for Mobile Information Communication and Security Southeast University210096 NanjingChina Purple Mountain Laboratories 211111 NanjingChina Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore

Signal conversion plays an important role in many applications such as communication,sensing,and *** signal conversion between optical and microwave frequencies is a crucial step to construct hybrid communication systems that combine both optical and microwave wireless technologies to achieve better features,which are highly desirable in the future wireless ***,such a signal conversion process typically requires a complicated relay to perform multiple operations,which will consume additional hardware/time/energy ***,we report a light-to-microwave transmitter based on the time-varying and programmable metasurface integrated with a high-speed photoelectric detection circuit into a *** a transmitter can convert a light intensity signal to two microwave binary frequency shift keying signals by using the dispersion characteristics of the metasurface to implement the frequency division *** illustrate the metasurface-based transmitter,a hybrid wireless communication system that allows dual-channel data transmissions in a light-to-microwave link is demonstrated,and the experimental results show that two different videos can be transmitted and received simultaneously and *** metasurface-enabled signal conversion solution may enrich the functionalities of metasurfaces,and could also stimulate new information-oriented applications.

关键词： microwave surface light

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MatChat: A large language model and application service platform for materials science

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Chinese Physics B 2023年第11期32卷 173-178页

作者：陈子逸谢帆恺万萌袁扬刘淼王宗国孟胜王彦棡 Computer Network Information Center Chinese Academy of SciencesBeijing 100083China University of Chinese Academy of Sciences Beijing 100049China Institute of Physics Chinese Academy of SciencesBeijing 100190China School of Physical Sciences University of Chinese Academy of SciencesBeijing 100190China Songshan Lake Materials Laboratory Dongguan 523808China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China

The prediction of chemical synthesis pathways plays a pivotal role in materials science research. Challenges, such as the complexity of synthesis pathways and the lack of comprehensive datasets, currently hinder our ability to predict these chemical processes accurately. However, recent advancements in generative artificial intelligence(GAI), including automated text generation and question–answering systems, coupled with fine-tuning techniques, have facilitated the deployment of large-scale AI models tailored to specific domains. In this study, we harness the power of the LLaMA2-7B model and enhance it through a learning process that incorporates 13878 pieces of structured material knowledge *** specialized AI model, named Mat Chat, focuses on predicting inorganic material synthesis pathways. Mat Chat exhibits remarkable proficiency in generating and reasoning with knowledge in materials science. Although Mat Chat requires further refinement to meet the diverse material design needs, this research undeniably highlights its impressive reasoning capabilities and innovative potential in materials science. Mat Chat is now accessible online and open for use, with both the model and its application framework available as open source. This study establishes a robust foundation for collaborative innovation in the integration of generative AI in materials science.

关键词： MatChat materials science generative artificial intelligence

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Scale‐wise interaction fusion and knowledge distillation network for aerial scene recognition

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1178-1190页

作者： Hailong Ning Tao Lei Mengyuan An Hao Sun Zhanxuan Hu Asoke K.Nandi School of Computer Science and Technology Xi'an University of Posts and TelecommunicationsShaanxi Key Laboratory of Network Data Analysis and Intelligent ProcessingXi'anChina Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'anChina School of Electronic Information and Artificial Intelligence Shaanxi University of Science and TechnologyXi'anChina School of Computer Central China Normal UniversityWuhanChina Department of Electronic and Electrical Engineering Brunel University LondonLondonUK Xi'an Jiaotong University Xi'anChina

Aerial scene recognition(ASR)has attracted great attention due to its increasingly essential *** of the ASR methods adopt the multi‐scale architecture because both global and local features play great roles in ***,the existing multi‐scale methods neglect the effective interactions among different scales and various spatial locations when fusing global and local features,leading to a limited ability to deal with challenges of large‐scale variation and complex background in aerial scene *** addition,existing methods may suffer from poor generalisations due to millions of to‐belearnt parameters and inconsistent predictions between global and local *** tackle these problems,this study proposes a scale‐wise interaction fusion and knowledge distillation(SIF‐KD)network for learning robust and discriminative features with scaleinvariance and background‐independent *** main highlights of this study include two *** the one hand,a global‐local features collaborative learning scheme is devised for extracting scale‐invariance features so as to tackle the large‐scale variation problem in aerial scene ***,a plug‐and‐play multi‐scale context attention fusion module is proposed for collaboratively fusing the context information between global and local *** the other hand,a scale‐wise knowledge distillation scheme is proposed to produce more consistent predictions by distilling the predictive distribution between different scales during *** experimental results show the proposed SIF‐KD network achieves the best overall accuracy with 99.68%,98.74%and 95.47%on the UCM,AID and NWPU‐RESISC45 datasets,respectively,compared with state of the arts.

关键词： deep learning image analysis image classification information fusion

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MISP: An Efficient Quantum-resistant Misbehavior Preventing Scheme with Self-enforcement for Vehicle-to-Everything

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Chen, Ying He, Debiao Bao, Zijian Wang, Huaqun Luo, Min Wuhan University School of Cyber Science and Engineering Wuhan430072 China Key Laboratory of Computing Power Network & Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Nanjing University of Posts and Telecommunications College of Computer Nanjing210023 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network & Information System Security Jinan250014 China

In the Vehicle-to-Everything (V2X) communication system, the presence of ambiguous warnings significantly increases the risk of severe accidents, posing a substantial threat to the safety of autonomous driving. It is crucial to detect such confusing warnings to avoid danger. Existing solutions to address this issue heavily rely on trust entities or are constructed based on number theory assumptions, leading to low efficiency and vulnerability to quantum attacks. In this paper, we leverage the double authentication-preventing signature scheme (DAPS) to present a revocable identity-based double-authentication preventing signature scheme (RIDAPS) and provides an instantiation from lattice. Furthermore, we propose a post-quantum secure misbehavior preventing scheme (Misp) based on our RIDAPS scheme. We give a detailed proof in the random oracle model (ROM) to demonstrate that our contribution achieves security requirements. Additionally, the efficiency evaluation results demonstrate that our scheme is suitable to be applied in V2X. © 2004-2012 IEEE.

关键词： Authentication

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Generalization-Enhanced Few-Shot Object Detection in Remote Sensing

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5445-5460页

作者： Lin, Hui Li, Nan Yao, Pengjuan Dong, Kexin Guo, Yuhan Hong, Danfeng Zhang, Ying Wen, Congcong China Academy of Electronics and Information Technology Beijing100846 China China Meteorological Administration National Satellite Meteorological Center Innovation Center for FengYun Meteorological Satellite Beijing100081 China Tsinghua University State Key Laboratory of Hydroscience and Engineering Department of Hydraulic Engineering Beijing100084 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100049 China University of Science and Technology Beijing School of Automation and Electrical Engineering Beijing100083 China New York University Abu Dhabi Department of Electrical and Computer Engineering Abu Dhabi United Arab Emirates University of Science and Technology of China School of Cyber Science and Technology Anhui Hefei230052 China

Object detection is a fundamental task in computer vision that involves accurately locating and classifying objects within images or video frames. In remote sensing, this task is particularly challenging due to the high resolution, multi-scale features, and diverse ground object characteristics inherent in satellite and UAV imagery. These challenges necessitate more advanced approaches for effective object detection in such environments. While deep learning methods have achieved remarkable success in remote sensing object detection, they typically rely on large amounts of labeled data. Acquiring sufficient labeled data, particularly for novel or rare objects, is both challenging and time-consuming in remote sensing scenarios, limiting the generalization capabilities of existing models. To address these challenges, few-shot learning (FSL) has emerged as a promising approach, aiming to enable models to learn new classes from limited labeled examples. Building on this concept, few-shot object detection (FSOD) specifically targets object detection challenges in data-limited conditions. However, the generalization capability of FSOD models, particularly in remote sensing, is often constrained by the complex and diverse characteristics of the objects present in such environments. In this paper, we propose the Generalization-Enhanced Few-Shot Object Detection (GE-FSOD) model to improve the generalization capability in remote sensing FSOD tasks. Our model introduces three key innovations: the Cross-Level Fusion Pyramid Attention network (CFPAN) for enhanced multi-scale feature representation, the Multi-Stage Refinement Region Proposal network (MRRPN) for more accurate region proposals, and the Generalized Classification Loss (GCL) for improved classification performance in few-shot scenarios. GE-FSOD demonstrates superior robustness and accuracy in remote sensing FSOD tasks through these enhancements. Extensive experiments on the DIOR and NWPU VHR-10 datasets show that our

关键词： Labeled data

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GPT4ESG: Streamlining Environment, Society, and Governance Analysis with Custom AI Models 4

GPT4ESG: Streamlining Environment, Society, and Governance A...

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Lin, Lydia Hsiao-Mei Ting, Fang-Kai Chang, Ting-Jui Wu, Jun-Wei Tsai, Richard Tzong-Han National Taipei University of Technology Department of Interaction Design Taipei City Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan City Taiwan Center for Gis Research Center for Humanities and Social Sciences Academia Sinica Taipei City Taiwan

ISBN: (纸本)9798350360721

Many companies now prioritize Environment, Society, and Governance (ESG) and financial performance due to concerns about climate change. We created GPT4ESG, a quick method to analyze a company's ESG investment and influence. GPT4ESG is a model system architecture based on BERT and GPT with five parts:(1) Data cleaning: The data is cleaned and preprocessed to ensure it is in a suitable format for the model. (2) Tokenizer adaptation: A customized GPT assistant is used to assist in scoring, and results are given after expert review. (3) Model adjustment: A classification layer is added to the output. (4) Fine-tuning: The pre-trained model is fine-tuned according to the ESG field know-how. (5) Evaluation and Testing: The model was tested on a validation set during training and then on a testing set. We collected ESG reports of well-known listed companies in the technology industries in the United States as a data set. The experimental results showed that the customized GPT4ESG model was more effective than ESG-BERT in classifying ESG data. This model simplifies ESG reporting for stakeholders to make responsible decisions. © 2024 IEEE.

关键词： Climate change

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On Partial Optimal Transport: Revising the Infeasibility of Sinkhorn and Efficient Gradient Methods 38

On Partial Optimal Transport: Revising the Infeasibility of ...

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38th AAAI Conference on Artificial Intelligence, AAAI 2024

作者： Nguyen, Anh Duc Nguyen, Tuan Dung Nguyen, Quang Minh Nguyen, Hoang H. Nguyen, Lam M. Toh, Kim-Chuan Department of Mathematics National University of Singapore Singapore Department of Computer and Information Science University of Pennsylvania United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States School of Industrial and Systems Engineering Georgia Institute of Technology United States IBM Research Thomas J. Watson Research Center United States Institute of Operations Research and Analytics National University of Singapore Singapore

ISBN: (纸本)1577358872

This paper studies the Partial Optimal Transport (POT) problem between two unbalanced measures with at most n supports and its applications in various AI tasks such as color transfer or domain adaptation. There is hence a need for fast approximations of POT with increasingly large problem sizes in arising applications. We first theoretically and experimentally investigate the infeasibility of the state-of-the-art Sinkhorn algorithm for POT, which consequently degrades its qualitative performance in real world applications like point-cloud registration. To this end, we propose a novel rounding algorithm for POT, and then provide a feasible Sinkhorn procedure with a revised computation complexity of Oe(n2/Ε4). Our rounding algorithm also permits the development of two first-order methods to approximate the POT problem. The first algorithm, Adaptive Primal-Dual Accelerated Gradient Descent (APDAGD), finds an Ε-approximate solution to the POT problem in Oe(n2.5/Ε). The second method, Dual Extrapolation, achieves the computation complexity of Oe(n2/Ε), thereby being the best in the literature. We further demonstrate the flexibility of POT compared to standard OT as well as the practicality of our algorithms on real applications where two marginal distributions are unbalanced. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Gradient methods

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Raise Your Eyebrows Higher: Facilitating Emotional Communication in Social Virtual Reality Through Region-Specific Facial Expression Exaggeration 25

Raise Your Eyebrows Higher: Facilitating Emotional Communica...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Wang, Xueyang Zhao, Sheng Wang, Yihe Han, Howard Ziyu Liu, Xinge Yi, Xin Tong, Xin Li, Hewu Institute for Network Sciences and Cyberspace Tsinghua University Beijing China Zhongguancun Laboratory Beijing China Weiyang College Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China Human-Computer Interaction Institute Carnegie Mellon University pittsburghPA United States Beijing National Research Center for Information Science and Technology Beijing China Guangzhou China

ISBN: (纸本)9798400713941

While exaggerated facial expressions in cartoon avatars can enhance emotional communication in social virtual reality (VR), they risk triggering the uncanny valley effect. Our research reveals that this effect varies significantly across different emotions. In Study 1 (N=30), participants evaluated scaled facial expressions during simulated VR conversations. We found that expression exaggeration had opposing effects: it decreased facial realism for joy, surprise, and disgust due to overly dramatic mouth movements, while enhancing realism for fear, sadness, and anger - emotions that rely on upper facial expressions typically constrained by HMD pressure. Based on these findings, we developed a region-specific facial expression exaggeration strategy that enhances under-expressed upper facial features while maintaining natural lower facial movements. Study 2 (N=20) validated this approach, demonstrating enhanced emotional intensity and contagion for negative emotions while mitigating the uncanny valley effect. Our research provides practical guidelines for optimizing avatar-mediated emotional communication in social VR environments. © 2025 Copyright held by the owner/author(s).

关键词： Virtualization

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Preserving Privacy of User Identity Based on Pseudonym Variable in 5G

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computers, Materials & Continua 2022年第3期70卷 5551-5568页

作者： Mamoon M.Saeed Mohammad Kamrul Hasan Rosilah Hassan Rania Mokhtar Rashid A.Saeed Elsadig Saeid Manoj Gupta Electrical Engineering Department Faculty of EngineeringAlzaiem Alazahri UniversityKhartoumSudan Center for Cyber Security Faculty of Information Science and TechnologyUniversiti KebangsaanMalaysia(UKM)43600BangiMalaysia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityPO Box 11099Taif21944Saudi Arabia Department of Electronics Engineering College of EngineeringSudan University of Science and TechnologyKhartoumSudan Department of Electronics and Communication Engineering JECRC UniversityJaipurIndia

The fifth generation(5G)system is the forthcoming generation of the mobile communication *** has numerous additional features and offers an extensively high data rate,more capacity,and low ***,these features and applications have many problems and issues in terms of security,which has become a great challenge in the telecommunication *** paper aimed to propose a solution to preserve the user identity privacy in the 5G system that can identify permanent identity by using Variable Mobile Subscriber Identity,which randomly changes and does not use the permanent identity between the user equipment and home *** this mechanism,the user identity privacy would be secured and ***,it improves the synchronization between mobile users and home ***,its compliance with the Authentication and Key Agreement(AKA)structure was adopted in the previous *** can be deployed efficiently in the preceding generations because the current architecture imposes minimal modifications on the network parties without changes in the authentication vector’s message ***,the addition of any hardware to the AKA carries minor adjustments on the network *** this paper,the ProVerif is used to verify the proposed scheme.

关键词： 5G privacy and security user identity IMSI authentication and key agreement(AKA)

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Blockchain-Based Secure and Efficient Secret Image Sharing With Outsourcing Computation in Wireless networks

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IEEE Transactions on Wireless Communications 2024年第1期23卷 423-435页

作者： Zhou, Zhili Wan, Yao Cui, Qi Yu, Keping Mumtaz, Shahid Yang, Ching-Nung Guizani, Mohsen Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou510006 China Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China Graduate School of Science and Engineering Hosei University Tokyo184-8584 Japan RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Department of Applied Informatics Silesian University of Technology Gliwice44-100 Poland Department of Engineering Nottingham Trent University NottinghamNG1 4FQ United Kingdom Department of Computer Science and Information Engineering National Dong Hwa University Hualien97401 Taiwan Abu Dhabi United Arab Emirates

Secret Image Sharing (SIS) is the technology that shares any given secret image by generating and distributing n shadow images in the way that any subset of k shadow images can restore the secret image. However, in the existing SIS schemes, the shadow images will be easily tampered and corrupted during the communication, which will pose serious security issues. Recently, blockchain has emerged as a promising paradigm in the field of data communication and information security. To securely communicate and effectively protect the secret image data in wireless networks, we propose a Blockchain-based Secure and Efficient Secret Image Sharing (BC-SESIS) scheme with outsourcing computation in wireless networks. In the proposed BC-SESIS scheme, the shadow images are encrypted and stored in the blockchain to prevent them from being tampered and corrupted. The identity authentication-enabled smart contract is deployed to achieve the (k,n) threshold for secret image restoring. Furthermore, to reduce the computational burden of smart contract and users, an efficient outsourcing computation method is designed to outsource the restoring task, which is securely implemented by agent miners in the encryption domain. Theoretical analysis and extensive experiments demonstrate that the BC-SESIS scheme can achieve desirable communication security and high computational efficiency in the wireless networks. © 2023 IEEE.

关键词： Smart contract

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