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IEEE Conference on Decision and Control

作者： Yuexin Cao Yibei Li Zhixin Liu Lirong Zheng Xiaoming Hu Department of Mathematics KTH Royal Institute of Technology Sweden School of Electrical and Electronic Engineering Nanyang Technological University Singapore Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences China School of Information Science and Technology Fudan University China

In this paper, the minimal control placement prob-lem for Turing's reaction-diffusion model is studied. Turing's model describes the process of morphogens diffusing and reacting with each other and is considered as one of the most fundamental models to explain pattern formation in a devel-oping embryo. Controlling pattern formation artificially has gained increasing attention in the field of development biology, which motivates us to investigate this problem mathematically. In this work, the two-dimensional Turing's reaction-diffusion model is discretized into square grids. The minimal control placement problem for the diffusion system is investigated first. The symmetric control sets are defined based on the symmetry of the network structure. A necessary condition is provided to guarantee controllability. Under certain circumstances, we prove that this condition is also sufficient. Then we show that the necessary condition can also be applied to the reaction-diffusion system by means of suitable extension of the symmetric control sets. Under similar circumstances, a sufficient condition is given to place the minimal control for the reaction-diffusion system.

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Ray theory-Based Compounded Plane Wave Ultrasound Imaging for Aberration Corrected transcranial imaging: a Phantom Study

Ray theory-Based Compounded Plane Wave Ultrasound Imaging fo...

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IEEE Symposium (IUS) Ultrasonics

作者： Chen Jiang Shaoyuan Yan Chengcheng Liu Kailiang Xu Dean Ta Center for Biomedical Engineering School of Information Science and Technology Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China

Compounded plane wave imaging (CPWI) allows high-frame-rate measurement benefiting real-time brain imaging. The quality of transcranial brain imaging remains to be improved with the presence of skulls because of phase aberration. This study introduced methods of calculating the transmitting time-delays, and their accuracy was compared with numerical simulation using a transcranial model. Experiments were carried out on a 3D-printed skull phantom. High-quality intracranial images could be achieved using the right transmitting and receiving time-delays. This study suggested that the proposed methods might have the potential for real-time and non-invasive transcranial aberration-corrected imaging.

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2M-NER: Contrastive Learning for Multilingual and Multimodal NER with Language and Modal Fusion

arXiv

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arXiv 2024年

作者： Wang, Dongsheng Feng, Xiaoqin Liu, Zeming Wang, Chuan The Department of Science and Technology Teaching China University of Political Science and Law Beijing102249 China Mobvoi AI Lab Beijing100044 China School of Computer Science and Engineering Beihang University Beijing100191 China Institute of Information Engineering CAS Beijing100085 China

Named entity recognition (NER) is a fundamental task in natural language processing that involves identifying and classifying entities in sentences into pre-defined types. It plays a crucial role in various research fields, including entity linking, question answering, and online product recommendation. Recent studies have shown that incorporating multilingual and multimodal datasets can enhance the effectiveness of NER. This is due to language transfer learning and the presence of shared implicit features across different modalities. However, the lack of a dataset that combines multilingualism and multimodality has hindered research exploring the combination of these two aspects, as multimodality can help NER in multiple languages simultaneously. In this paper, we aim to address a more challenging task: multilingual and multimodal named entity recognition (MMNER), considering its potential value and influence. Specifically, we construct a large-scale MMNER dataset with four languages (English, French, German and Spanish) and two modalities (text and image). To tackle this challenging MMNER task on the dataset, we introduce a new model called 2M-NER, which aligns the text and image representations using contrastive learning and integrates a multimodal collaboration module to effectively depict the interactions between the two modalities. Extensive experimental results demonstrate that our model achieves the highest F1 score in multilingual and multimodal NER tasks compared to some comparative and representative baselines. Additionally, in a challenging analysis, we discovered that sentence-level alignment interferes a lot with NER models, indicating the higher level of difficulty in our dataset. Copyright © 2024, The Authors. All rights reserved.

关键词： Large datasets

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Microwave heating as a universal method to transform confined molecules into armchair graphene nanoribbons

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Nano Research 2023年第7期16卷 10644-10651页

作者： Haoyuan Zhang Yingzhi Chen Kunpeng Tang Ziheng Lin Xuan Li Hongwei Zhang Yifan Zhang Chi Ho Wong Chi Wah Leung Chee Leung Mak Yuan Hu Weili Cui Kecheng Cao Lei Shi State Key Laboratory of Optoelectronic Materials and Technologies Nanotechnology Research CenterGuangdong Province Key Lab of Display Material and TechnologySchool of Materials Science and EngineeringSun Yat-sen UniversityGuangzhou 510275China Huzhou Key Laboratory of Environmental Functional Materials and Pollution Control School of EngineeringHuzhou UniversityHuzhou 313000China Department of Industrial and Systems Engineering Research Institute for Advanced ManufacturingThe Hong Kong Polytechnic UniversityHong Kong 999077China Department of Applied Physics The Hong Kong Polytechnic UniversityHong Kong 999077China Shanghai Key Laboratory of High-resolution Electron Microscopy ShanghaiTech UniversityShanghai 201210China

Armchair graphene nanoribbons(AGNRs)with sub-nanometer width are potential materials for the fabrication of novel nanodevices thanks to their moderate direct band *** are usually synthesized by polymerizing precursor molecules on substrate ***,it is time-consuming and not suitable for large-scale *** can also be grown by transforming precursor molecules inside single-walled carbon nanotubes(SWCNTs)via furnace annealing,but the obtained AGNRs are normally *** this work,microwave heating is applied for transforming precursor molecules into *** fast heating process allows synthesizing the AGNRs in *** different molecules were successfully transformed into AGNRs,suggesting that it is a universal *** importantly,as demonstrated by Raman spectroscopy,aberrationcorrected high-resolution transmission electron microscopy and theoretical calculations,less twisted AGNRs are synthesized by the microwave heating than the furnace *** results reveal a route for rapid production of AGNRs in large scale,which would benefit future applications in novel AGNRs-based semiconductor devices.

关键词： armchair graphene nanoribbons(AGNRs) microwave heating single-walled carbon nanotubes(SWCNTs) Raman spectroscopy

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T-PickSeer: Visual Analysis of Taxi Pick-up Point Selection Behavior

arXiv

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arXiv 2023年

作者： Gu, Shuxian Dai, Yemo Feng, Zezheng Wang, Yong Zeng, Haipeng School of Intelligent Systems Engineering Sun Yat-sen University China Huawei Technologies Co. Ltd China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong School of Computing and Information Systems Singapore Management University Singapore

Taxi drivers often take much time to navigate the streets to look for passengers, which leads to high vacancy rates and wasted resources. Empty taxi cruising remains a big concern for taxi companies. Analyzing the pick-up point selection behavior can solve this problem effectively, providing suggestions for taxi management and dispatch. Many studies have been devoted to analyzing and recommending hot-spot regions of pick-up points, which can make it easier for drivers to pick up passengers. However, the selection of pick-up points is complex and affected by multiple factors, such as convenience and traffic management. Most existing approaches cannot produce satisfactory results in real-world applications because of the changing travel demands and the lack of interpretability. In this paper, we introduce a visual analytics system, T-PickSeer, for taxi company analysts to better explore and understand the pick-up point selection behavior of passengers. We explore massive taxi GPS data and employ an overview-to-detail approach to enable effective analysis of pick-up point selection. Our system provides coordinated views to compare different regularities and characteristics in different regions. Also, our system assists in identifying potential pick-up points and checking the performance of each pick-up point. Three case studies based on a real-world dataset and interviews with experts have demonstrated the effectiveness of our system. Copyright © 2023, The Authors. All rights reserved.

关键词： Taxicabs

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Leveraging Large Language Models for Integrated Satellite-Aerial-Terrestrial Networks: Recent Advances and Future Directions

arXiv

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arXiv 2024年

作者： Javaid, Shumaila Khalil, Ruhul Amin Saeed, Nasir He, Bin Alouini, Mohamed-Slim The Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201804 China Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China The Department of Electrical and Communication Engineering College of Engineering UAE University Al-Ain15551 United Arab Emirates Division King Abdullah University Of Science And Technology 23955 Saudi Arabia

Integrated satellite, aerial, and terrestrial networks (ISATNs) represent a sophisticated convergence of diverse communication technologies to ensure seamless connectivity across different altitudes and platforms. This paper explores the transformative potential of integrating Large Language Models (LLMs) into ISATNs, leveraging advanced Artificial Intelligence (AI) and Machine Learning (ML) capabilities to enhance these networks. We outline the current architecture of ISATNs and highlight the significant role LLMs can play in optimizing data flow, signal processing, and network management to advance 5G/6G communication technologies through advanced predictive algorithms and real-time decision-making. A comprehensive analysis of ISATN components is conducted, assessing how LLMs can effectively address traditional data transmission and processing bottlenecks. The paper delves into the network management challenges within ISATNs, emphasizing the necessity for sophisticated resource allocation strategies, traffic routing, and security management to ensure seamless connectivity and optimal performance under varying conditions. Furthermore, we examine the technical challenges and limitations associated with integrating LLMs into ISATNs, such as data integration for LLM processing, scalability issues, latency in decision-making processes, and the design of robust, fault-tolerant systems. The study also identifies key future research directions for fully harnessing LLM capabilities in ISATNs, which is crucial for enhancing network reliability, optimizing performance, and achieving a truly interconnected and intelligent global network system. © 2024, CC BY-NC-SA.

关键词： Decision making

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D4A: An efficient and effective defense across agnostic adversarial attacks

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Neural Networks 2025年 183卷 106938页

作者： Li, Xianxian Gan, Zeming Bai, Yan Su, Linlin Li, De Wang, Jinyan Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal University Guilin China School of Computer Science and Engineering Guangxi Normal University Guilin China School of Engineering and Technology University of Washington Tacoma TacomaWA United States

Recent studies show that Graph Neural Networks (GNNs) are vulnerable to structure adversarial attacks, which draws attention to adversarial defenses in graph data. Previous defenses designed heuristic defense strategies for specific attacks or graph properties, and are no longer sufficiently robust across all these attacks. To address this problem, we discuss the abnormal behaviors of GNNs in structure perturbations from a posterior distribution perspective. We suggest that the structural vulnerability of GNNs stems from their dependence on local graph smoothing, which can also lead to unfitting — a first-found phenomenon specific to the graph domain. We demonstrate that abnormal behaviors, except for unfitting, can attribute to a posterior distribution shift. To intrinsically prevent the occurrence of abnormal behaviors, we first propose smooth-less message passing to enhance the tolerance with respect to structure perturbations, while significantly mitigating the unfitting. We also propose the distribution shift constraint to restrict other abnormal behaviors of our model. Our approach is evaluated on six different datasets across over four kinds of attacks and compared to 11 representative baselines. The experimental results show that our method improves the defense performance across various attacks, and provides a great trade-off between accuracy and adversarial robustness. © 2024 Elsevier Ltd

关键词： Generative adversarial networks

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Formulation of Optimization Problems with Radiation Field Phases as Design Variables for Pattern Synthesis of Linear Antenna Arrays

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China Communications 2022年第12期19卷 86-100页

作者： Fei Zhao Jun He Sanyou Zeng Changhe Li Qinghui Xu Zhigao Zeng School of Mechanical Engineering and Electronic Information China University of GeosciencesWuhan 430074China Science and Technology on Blind Signal Processing Laboratory Southwest Electronics and Telecommunication Technology Research InstituteChengdu 610041China School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China College of Computer Hunan University of TechnologyZhuzhou CityHunan Province412007China

Pattern synthesise of antenna arrays is usually complicated optimization problems,while evolutionary algorithms(EAs)are promising in solving these *** paper does not propose a new EA,but does construct a new form of optimization *** new optimization formulation has two differences from the common *** is the objective function is the field error between the desired and the designed,not the usual amplitude error between the desired and the *** difference is beneficial to decrease complexity in some *** second difference is that the design variables are changed as phases of desired radiation field within shaped-region,instead of excitation *** difference leads to the reduction of the number of design variables.A series of synthesis experiments including equally and unequally spaced linear arrays with different pattern shape requirements are applied,and the effectiveness and advantages of the proposed new optimization problems are *** results show that the proposing a new optimization formulation with less complexity is as significant as proposing a new algorithm.

关键词： pattern synthesis evolutionary algorithms linear antenna arrays

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Retraction Note: Enabling secure and efficient industry 4.0 transformation through trust-authorized anomaly detection in cloud environments with a hybrid AI approach

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Optical and Quantum Electronics 2024年第10期56卷 1-2页

作者： Prakash, N. Vignesh, J. Ashwin, M. Ramadass, Sudhir Veeranjaneyulu, N. V. Athawale, Shashikant Ravuri, Ananda Subramanian, Balambigai Department of Information Technology B.S Abdur Rahman Crescent Institute of Science and Technology Vandalur India Department of ISE Jain University Bengaluru India Deparment of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram India Sterck Systems Chennai India Department of Information Technology VFSTR Deemed to Be University Vadlamudi Guntur District India Department of Computer Engineering AISSMS COE Pune India Intel Corporation Hillsboro USA Department of ECE Kongu Engineering College Perundurai India

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Real-Time IoT Cybersecurity using Machine Learning-based AI Threat Detection System to Train Generative Robots

Real-Time IoT Cybersecurity using Machine Learning-based AI ...

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： P Srilakshmi Koushik Reddy Chaganti Talachendri Suryam Sweety Julia I D. Chaithanya Jami Kavitha Department of CSE G Pulla Reddy Engineering College Dept. of Computer and Information Systems University of the Cumberlands Department of CSE (DATA SCIENCE) Vignan Institute of Technology and Science Department of AI&ML Malla Reddy University Department of CSE CVR College of Engineering Department of CSE Sanketika Vidya Parishad Engineering College

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Existing security protocols encounter major cybersecurity difficulties because the online devices' growing popularity continues to spread across networks. These security protocols are ineffective because they do not properly handle current cyber threats. The main goal of this study involves developing enhanced IoT cybersecurity through the development of a threat detection system which brings together adversarial training and deep learning models (CNN-LSTM) and Federated Learning (FL). The system enables distributed Internet of Things devices to work on security model development through Federated Learning while maintaining total privacy of their information. Security procedures controlled by generative artificial intelligence robots alongside real-time attack protection functions decrease security response durations. Through its Federated CNN-LSTM model the system upholds a 1.2% false positive rate alongside a 98.3% accuracy evaluation and 160 milliseconds of exact threat tracking time. The designed system sustains a minimal occurrence of incorrect alarm activations. The developed system provides real-time security for the Internet of Things framework because it enables adaptive protection systems while preserving user privacy in current IoT settings.

关键词： Training Privacy Protocols Federated learning Real-time systems Threat assessment Internet of Things Computer security Protection Robots

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