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arXiv 2024年

作者： Chen, Hao Zhao, Wei Li, Yingli Zhong, Tianyang Wang, Yisong Shang, Youlan Guo, Lei Han, Junwei Liu, Tianming Liu, Jun Zhang, Tuo The School of Automation Northwestern Polytechnical University Xi’an710072 China Department of Radiology The Second Xiangya Hospital Central South University Changsha410011 China Clinical Research Center for Medical Imaging in Hunan Province Changsha China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China The School of Computing University of Georgia AthensGA30602 United States

Medical image analysis is crucial in modern radiological diagnostics, especially given the exponential growth in medical imaging data. The demand for automated report generation systems has become increasingly urgent. While prior research has mainly focused on using machine learning and multimodal language models for 2D medical images, the generation of reports for 3D medical images has been less explored due to data scarcity and computational complexities. This paper introduces 3D-CT-GPT, a Visual Question Answering (VQA)- based medical visual language model specifically designed for generating radiology reports from 3D CT scans, particularly chest CTs. Extensive experiments on both public and private datasets demonstrate that 3D-CT-GPT significantly outperforms existing methods in terms of report accuracy and quality. Although current methods are few, including the partially open-source CT2Rep and the open-source M3D, we ensured fair comparison through appropriate data conversion and evaluation methodologies. Experimental results indicate that 3D-CT-GPT enhances diagnostic accuracy and report coherence, establishing itself as a robust solution for clinical radiology report generation. Future work will focus on expanding the dataset and further optimizing the model to enhance its performance and applicability. © 2024, CC BY-SA.

关键词： Computerized tomography

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BIG-FUSION: Brain-Inspired Global-Local Context Fusion Framework for Multimodal Emotion Recognition in Conversations 39

BIG-FUSION: Brain-Inspired Global-Local Context Fusion Frame...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Yusong Fang, Xuanye Yin, Huifeng Li, Dongyuan Li, Guoqi Xu, Qi Xu, Yi Zhong, Shuai Xu, Mingkun Guangdong Institute of Intelligence Science and Technology Hengqin Zhuhai China Department of Information and Communications Engineering Tokyo Institute of Technology Tokyo Japan School of Computer Science and Technology Dalian University of Technology Dalian China Center for Brain Inspired Computing Research (CBICR) Department of Precision Instrument Tsinghua University Beijing China Institute of Automation Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)157735897X

Considering the importance of capturing both global conversational topics and local speaker dependencies for multimodal emotion recognition in conversations, current approaches first utilize sequence models like Transformer to extract global context information, then apply Graph Neural Networks to model local speaker dependencies for local context information extraction, coupled with Graph Contrastive Learning (GCL) to enhance node representation learning. However, this sequential design introduces potential biases: the extracted global context information inevitably influences subsequent processing, compromising the independence and diversity of the original local features;current graph augmentation methods in GCL cannot consider both global and local context information in conversations to evaluate the node importance, hindering the learning of key information. Inspired by the human brain excels at handling complex tasks by efficiently integrating local and global information processing mechanisms, we propose an aligned global-local context fusion framework for sequence-based design to address these problems. This design includes a dual-attention Transformer and a dual-evaluation method for graph augmentation in GCL. The dual-attention Transformer combines global attention for overall context extraction with sliding-window attention for local context capture. Meanwhile, spiking neuron dynamics are introduced to enhance the representation capability of the extracted features, supporting the effective the interaction between different modalities from these global and local cues. The dual-evaluation method in GCL comprises global importance evaluation to identify nodes crucial for overall conversation context, and local importance evaluation to detect nodes significant for local semantics, generating augmented graph views that preserve both global and local information. This approach ensures balanced information processing throughout the pipeline, enhancing biologica

关键词： Graph neural networks

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Urban trip generation forecasting based on gradient boosting algorithm 1

Urban trip generation forecasting based on gradient boosting...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Li, Zhishuai Xiong, Gang Zhang, Yu Zheng, Meng Dong, Xisong Lv, Yisheng Institute of Automation Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China Beijing Municipal Institute of City Planning and Design Traffic Planning Department Beijing China

ISBN: (纸本)9781665433372

The four-step transportation model plays an important role in urban planning. The quality of the first phase, i.e. trip generation, determines the performance of the global course. The majority of trip generation forecasting models highly rely on mathematical derivation and have many predictor variables during the prediction, which leads to low accuracy of results and requires laboriously hand-crafted design of input vectors. This paper is the first to introduce the gradient boosting decision tree (GBDT) algorithm for trip generation prediction, and harmonizes such a powerful machine learning method with traditional urban planning requirements to achieve better prediction performance. Unlike the commonly used linear regression method, GBDT can automatically perform feature selection and model the non-linear relationships between input and output variables. Experimental results on real-world residential travel census in Beijing prove that the GBDT model significantly outperforms the baseline and can forecast the trip generation more accurately. © 2021 IEEE.

关键词： Decision trees

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Contrastive Learning Based Human Activity Recognition Using Body Sensors

Contrastive Learning Based Human Activity Recognition Using ...

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International Conference on Big Data computing and Communications (BIGCOM)

作者： Chuanshi Xie Yu Zhou Dandan Yu Shilong Sun Xiao Zhang College of Computer Science and Software Engineering Shenzhen University Shenzhen China Information Center The First Affiliated Hospital of Dalian Medical University Dalian China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Department of Computer Science South-Central Minzu University State Ethnic Affairs Commission Key Laboratory of Cyber-Physical Fusion Intelligent Computing South-Central Minzu University Wuhan China

Pervasive sensing and wearable sensor techniques have been increasingly employed to monitor and recognize human activities through body sensors in areas of smart healthcare and manufacturing. However, conventional machine learning or deep learning based human activity recognition (HAR) requires a large amount of labeled data, which is cost-expensive in real-world scenarios. To tackle this issue, we propose a contrastive learning framework for HAR (CL-HAR), which utilizes the generated unlabeled data as the input and explores the supervised features of the unlabeled data under the principle of self-supervised learning. A simple yet effective backbone network as a feature extractor for subsequent activity recognition is proposed. By using a small portion of the labeled samples as the training set which is fed into our learned feature extractor, we build a classifier and use the rest of the data to verify the feasibility and effectiveness of our CL method. Extensive experiments on three benchamark datasets and one real-world dataset demonstrate that CL-HAR can achieve better classification accuracy than compared supervised and semi-supervised methods with less labelled samples, which is of practical use.

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Spatio-temporal fusion for remote sensing data:an overview and new benchmark

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Science China(Information Sciences) 2020年第4期63卷 7-23页

作者： Jun LI Yunfei LI Lin HE Jin CHEN Antonio PLAZA Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation School of Geography and Planning Sun Yat-sen University School of Automation Science and Engineering South China University of Technology State Key Laboratory of Earth Surface Processes and Resource Ecology Institute of Remote Sensing Science and Engineering Faculty of Geographical ScienceBeijing Normal University Hyperspectral Computing Laboratory Department of Technology of Computers and CommunicationsEscuela Politécnica University of Extremadura

Spatio-temporal fusion(STF) aims at fusing(temporally dense) coarse resolution images and(temporally sparse) fine resolution images to generate image series with adequate temporal and spatial resolution. In the last decade, STF has drawn a lot of attention and many STF methods have been ***, to date the STF domain still lacks benchmark datasets, which is a pressing issue that needs to be addressed in order to foster the development of this field. In this review, we provide(for the first time in the literature) a robust benchmark STF dataset that includes three important characteristics:(1) diversity of regions,(2) long timespan, and(3) challenging scenarios. We also provide a survey of highly representative STF techniques, along with a detailed quantitative and qualitative comparison of their performance with our newly presented benchmark dataset. The proposed dataset is public and available online.

关键词： spatio-temporal fusion(STF) multi-temporal remote sensing data benchmark data experimental validation

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A preliminary study about gender gap perception in informatics studies in Spain 12

A preliminary study about gender gap perception in informati...

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12th International Conference on Virtual Campus, JICV 2022

作者： Garcia-Holgado, Alicia Gonzalez-Gonzalez, Carina S. Fernandez, Inmaculada Garcia Lopez, Lourdes Moreno Barrenechea, Edurne Rueda, Silvia Navarro, Angeles G. Martinez, Paloma Masia, Belen Navarro, Elena Fornes, Alicia Grial Research Group Universidad DeSalamanca Computer Science and Automation Department Salamanca Spain University of la Laguna Computer Engineering Department San Cristobal de La Laguna Spain Universidad de Málaga Computer Architecture Department Málaga Spain Universidad Carlos Iii de Madrid Computer Science and Engineering Department Madrid Spain Universidad Pública de Navarra Departamento de Estadística Informática y Matemáticas Pamplona Spain Universitat de València Departament of Computer Science Valencia Spain Universidad de Zaragoza Computer Science Department Zaragoza Spain Universidad de Castilla la Mancha Departament of Computing Systems Albacete Spain Computer Vision Center Universitat Autónoma de Barcelona Barcelona Spain

ISBN: (数字)9781665451062

ISBN: (纸本)9781665451062

The gender gap in STEM is an issue that affects regions and countries worldwide. Furthermore, the percentage of women in these areas depends on a range of different factors. In particular, the gender gap is critical in technology in general and, more specifically, in informatics. This problem affects not only the business sector but also society. Informatics is part of our lives;however, 50% of the world population is not represented in the teams that are developing solutions for solving society's problems. In Spain, the number of women with informatics degrees is around 15%. This work describes a case study developed by the committee of Women in Informatics at the Scientific Society of Spanish Informatics (SCIE) to analyse the perception of informatics scientists on the gender gap in informatics. © 2022 IEEE.

关键词： Computers

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Generative AI for Game Theory-based Mobile Networking

arXiv

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arXiv 2024年

作者： He, Long Sun, Geng Niyato, Dusit Du, Hongyang Mei, Fang Kang, Jiawen Debbah, Mérouane Han, Zhu College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong School of Automation Guangdong University of Technology Guangzhou China Khalifa University of Science and Technology P O Box 127788 Abu Dhabi United Arab Emirates University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446701 Korea Republic of

With the continuous advancement of network technology, various emerging complex networking optimization problems have created a wide range of applications utilizing game theory. However, since game theory is a mathematical framework, game theory-based solutions often rely heavily on the experience and knowledge of human experts. Recently, the remarkable advantages exhibited by generative artificial intelligence (GAI) have gained widespread attention. In this work, we propose a novel GAI-enabled game theory solution that combines the powerful reasoning and generation capabilities of GAI to the design and optimization of mobile networking. Specifically, we first outline the game theory and key technologies of GAI, and explore the advantages of combining GAI with game theory. Then, we review the contributions and limitations of existing research and demonstrate the potential application values of GAI applied to game theory in mobile networking. Subsequently, we develop a large language model (LLM)-enabled game theory framework to realize this combination, and demonstrate the effectiveness of the proposed framework through a case study in secured UAV networks. Finally, we provide several directions for future extensions. Copyright © 2024, The Authors. All rights reserved.

关键词： Game theory

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Hyper-YOLO: When Visual Object Detection Meets Hypergraph Computation

arXiv

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arXiv 2024年

作者： Feng, Yifan Huang, Jiangang Du, Shaoyi Ying, Shihui Yong, Jun-Hai Li, Yipeng Ding, Guiguang Ji, Rongrong Gao, Yue School of Software BNRist THUIBCS BLBCI Tsinghua University Beijing100084 China Institute of Artificial Intelligence and Robotics College of Artificial Intelligence Xi’an Jiaotong University Xi’an710049 China The Department of Mathematics School of Science Shanghai University Shanghai200444 China The Department of Automation Tsinghua University Beijing100084 China The Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Institute of Artificial Intelligence Fujian Engineering Research Center of Trusted Artificial Intelligence Analysis and Application Xiamen University 361005 China

We introduce Hyper-YOLO, a new object detection method that integrates hypergraph computations to capture the complex high-order correlations among visual features. Traditional YOLO models, while powerful, have limitations in their neck designs that restrict the integration of cross-level features and the exploitation of high-order feature interrelationships. To address these challenges, we propose the Hypergraph Computation Empowered Semantic Collecting and Scattering (HGC-SCS) framework, which transposes visual feature maps into a semantic space and constructs a hypergraph for high-order message propagation. This enables the model to acquire both semantic and structural information, advancing beyond conventional feature-focused learning. Hyper-YOLO incorporates the proposed Mixed Aggregation Network (MANet) in its backbone for enhanced feature extraction and introduces the Hypergraph-Based Cross-Level and Cross-Position Representation Network (HyperC2Net) in its neck. HyperC2Net operates across five scales and breaks free from traditional grid structures, allowing for sophisticated high-order interactions across levels and positions. This synergy of components positions Hyper-YOLO as a state-of-the-art architecture in various scale models, as evidenced by its superior performance on the COCO dataset. Specifically, HyperYOLO-N significantly outperforms the advanced YOLOv8-N and YOLOv9-T with 12% APval and 9% APval improvements. The source codes are at https://***/iMoonLab/Hyper-YOLO. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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Chaotic dynamics, topological analysis and flat analog electronic control by physiological signals of a neurally-inspired system

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Chinese Journal of Physics 2025年 96卷 20-52页

作者： Tabekoueng Njitacke, Zeric Ngamsa Tegnitsap, Joakim Vianney Zhao, Manyu Barà, Chiara Fozin, Théophile Fonzin Awrejcewicz, Jan Yoshimura, Natsue Valdes-Sosa, Pedro A. Letellier, Christophe Minati, Ludovico School of Life Science and Technology University of Electronic Science and Technology of China Chengdu 611731 China Department of Electrical and Electronic Engineering College of Technology (COT) University of Buea P.O. Box 63 Buea Cameroon Fotso-Victor University Institute of Technology University of Dschang P.O. Box 134 Dschang Cameroon Research Unit of Condensed Matter Electronics and Signal Processing Department of Physics University of Dschang Cameroon Department of Engineering University of Palermo Palermo 90128 Italy Department of Electrical and Electronic Engineering Faculty of Engineering and Technology (FET) University of Buea P.O. Box 63 Buea Cameroon Department of Automation Biomechanics and Mechatronics Łódź University of Technology Stefanowskiego Str. 1/15 Lodz 90-924 Poland School of Computing Institute of Science Tokyo (formerly Tokyo Institute of Technology) Yokohama Japan ATR Brain Information Communication Research Laboratory Group Kyoto Japan Department of Neuroinformatics Cuban Neuroscience Center Havana 11600 Cuba CORIA Rouen Normandie University Saint-Etienne du Rouvray 76800 France Center for Mind/Brain Sciences (CIMeC) University of Trento Trento 38123 Italy

A novel abstract bicompartmental neural model based on a cubic nonlinearity, consisting of introducing a Josephson junction as a coupling element, is presented. Through numerical simulations and experimental measurements involving an analog electronic circuit, it is found that the system can generate a double-scroll chaotic attractor endowed with a remarkably complex topology. This is accompanied by diversification of the spectral signature across its four variables and multistability. An analysis of the global observability and controllability is conducted, leading to the synthesis of a flat controller that can steer the dynamics towards those of structurally different entities, as exemplified by a modified Lorenz system. The first analog realization of flat control is then introduced. The possibility of low-latency and high-accuracy manipulation of the dynamics is demonstrated by considering the challenging case of tracking extraneous external signals, namely, physiological recordings indexing autonomic and central nervous system activity. Besides showing the flexibility and adaptability of the approach, the controller enables the concomitant generation of multiple additional signals with spectrogram features compared to the individual inputs, offering a promising substrate for future applications in physical time series data augmentation. © 2025 The Physical Society of the Republic of China (Taiwan)

关键词： Chaotic circuits Flat control Josephson junction Multistability Neural models Physiological recordings Topological analysis

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A Quantum Convolutional Neural Network for Image Classification

A Quantum Convolutional Neural Network for Image Classificat...

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第40届中国控制会议

作者： Yanxuan Lü Qing Gao Jinhu Lü Maciej Ogorza?ek Jin Zheng School of Automation Science and Electrical Engineering Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Department of Information Technologies Jagiellonian University

Artificial neural networks have achieved great success in many fields ranging from image recognition to video understanding. However, its high requirements for computing and memory resources have limited further development on processing big data with high dimensions. In recent years, advances in quantum computing show that building neural networks on quantum processors is a potential solution to this problem. In this paper, we propose a novel neural network model named Quantum Convolutional Neural Network(QCNN), aiming at utilizing the computing power of quantum systems to accelerate classical machine learning tasks. The designed QCNN is based on implementable quantum circuits and has a similar structure as classical convolutional neural networks. Numerical simulation results on the MNIST dataset demonstrate the effectiveness of our model.

关键词： Quantum computing Machine Learning Neural Network Quantum Convolutional Neural Network

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