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arXiv 2025年

作者： He, Haibin Ye, Maoyuan Zhang, Jing Cai, Xiantao Liu, Juhua Du, Bo Tao, Dacheng School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan University China College of Computing & Data Science Nanyang Technological University Singapore

Large Multimodal Models (LMMs) have become increasingly versatile, accompanied by impressive Optical Character Recognition (OCR) related capabilities. Existing OCR-related benchmarks emphasize evaluating LMMs’ abilities of relatively simple visual question answering, visual-text parsing, etc. However, the extent to which LMMs can deal with complex logical reasoning problems based on OCR cues is relatively unexplored. To this end, we introduce the Reasoning-OCR benchmark, which challenges LMMs to solve complex reasoning problems based on the cues that can be extracted from rich visual-text. Reasoning-OCR covers six visual scenarios and encompasses 150 meticulously designed questions categorized into six reasoning challenges. Additionally, Reasoning-OCR minimizes the impact of field-specialized knowledge. Our evaluation offers some insights for proprietary and open-source LMMs in different reasoning challenges, underscoring the urgent to improve the reasoning performance. We hope Reasoning-OCR can inspire and facilitate future research on enhancing complex reasoning ability based on OCR cues. Reasoning-OCR is publicly available at https://***/Hxyz-123/ReasoningOCR. Copyright © 2025, The Authors. All rights reserved.

关键词： Case based reasoning

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Deep learning for macular fovea detection based on ultra-widefield fundus images 2

Deep learning for macular fovea detection based on ultra-wid...

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2nd International Conference on Electrical, Electronics, and Information engineering, EEIE 2023

作者： Wang, Mini Han Huang, Lina Hou, Guanghui Yang, Jie Xing, Lumin Yuan, Qiting Chong, Kelvin Kam-Lung Lin, Zhiyuan Zeng, Peijin Fang, Xiaoxiao Yao, Xiaoping Li, Qingqian Liu, Jiang Lin, Chen Department of Ophthalmology & Visual Sciences Faculty of Medicine Chinese University of Hong Kong Hong Kong999077 Hong Kong The faculty of Data Science City University of Macau 999078 China Zhuhai People's Hospital Zhuhai Hospital Affiliated with Jinan University Zhuhai519000 China The Department of AI and big data application Zhuhai Institute of Advanced Technology Chinese Academy of Sciences Zhuhai519000 China Perspective Technology Group Zhuhai519000 China Jinan University Affiliated Shenzhen Eye Hospital Shenzhen518000 China Aier Eye Hospital of Zhuhai Zhuhai519000 China College of Artificial Intelligence Chongqing Industry & Trade Polytechnic Chongqing408000 China The First Affiliated Hospital Shandong First Medical University Shandong Provincial Qianfoshan Hospital Jinan250014 China Business School Hong Kong University of Science and Technology Hong Kong999077 Hong Kong Company Ltd. Guangdong Shenzhen518000 China School of Computer Beijing Institute of Technology Zhuhai Zhuhai519000 China Union Hospital of Fujian Medical University Fuzhou353000 China Department of Ophthalmology People's Hospital of Yantian District Shenzhen518000 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518000 China Department of Ophthalmology Shenzhen People's Hospital Shenzhen518000 China

ISBN: (纸本)9781510672840

Macula fovea detection is a crucial molecular biological prerequisite for screening and diagnosing macular diseases. Without early detection and proper treatment, any abnormality involving the macula may lead to blindness. However, with the ophthalmologist shortage and time-consuming artificial evaluation, neither the accuracy nor effectiveness of the diagnosis process could be guaranteed. In this project, we proposed a light-weighted deep learning model based on ultra-widefield fundus (UWF) images for macula fovea detection tasks. This study collected 2300 ultra-widefield fundus images from Shenzhen Aier Eye Hospital in China. A light-weighted method based on a U-shape network (Unet) and Fully Convolution Network (FCN) approach is implemented on 1800 (before amplifying process) training fundus images, 400 (before amplifying process) validation images, and 100 test images. Three professional ophthalmologists were invited to mark the fovea. A method from the anatomy perspective is investigated. This approach is derived from the spatial relationship between the macula fovea and optic disc center in UWF. A set of parameters of this method is set based on the experience of ophthalmologists and verified to be effective. The ultra-widefield swept-source optical coherence tomography (UWF-OCT) approach is the grounded method. Through a comparison of proposed methods, we conclude that the proposed light-weighted Unet method outperformed other methods on macula fovea detection tasks. © 2024 SPIE.

关键词： Convolution

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Artificial intelligence for geoscience:Progress,challenges,and perspectives

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The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

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MC3D-AD: A Unified Geometry-aware Reconstruction Model for Multi-category 3D Anomaly Detection

arXiv

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arXiv 2025年

作者： Cheng, Jiayi Gao, Can Zhou, Jie Wen, Jiajun Dai, Tao Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

3D Anomaly Detection (AD) is a promising means of controlling the quality of manufactured products. However, existing methods typically require carefully training a task-specific model for each category independently, leading to high cost, low efficiency, and weak generalization. Therefore, this paper presents a novel unified model for Multi-Category 3D Anomaly Detection (MC3D-AD) that aims to utilize both local and global geometry-aware information to reconstruct normal representations of all categories. First, to learn robust and generalized features of different categories, we propose an adaptive geometry-aware masked attention module that extracts geometry variation information to guide mask attention. Then, we introduce a local geometry-aware encoder reinforced by the improved mask attention to encode group-level feature tokens. Finally, we design a global query decoder that utilizes point cloud position embeddings to improve the decoding process and reconstruction ability. This leads to local and global geometry-aware reconstructed feature tokens for the AD task. MC3D-AD is evaluated on two publicly available Real3D-AD and Anomaly-ShapeNet datasets, and exhibits significant superiority over current state-of-the-art single-category methods, achieving 3.1% and 9.3% improvement in object-level AUROC over Real3D-AD and Anomaly-ShapeNet, respectively. The source code will be released upon acceptance. Copyright © 2025, The Authors. All rights reserved.

关键词： Geometry

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Insulating materials for realising carbon neutrality:Opportunities,remaining issues and challenges

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High Voltage 2022年第4期7卷 610-632页

作者： Chuanyang Li Yang Yang Guoqiang Xu Yao Zhou Mengshuo Jia Shaolong Zhong Yu Gao Chanyeop Park Qiang Liu Yalin Wang Shakeel Akram Xiaoliang Zeng Yi Li Fangwei Liang Bin Cui Junpeng Fang Lingling Tang Yulin Zeng Xingtao Hu Jiachen Gao Giovanni Mazzanti Jinliang He Jianxiao Wang Davide Fabiani Gilbert Teyssedre Yang Cao Feipeng Wang Yunlong Zi The State Key Laboratory of Power System Department of Electrical EngineeringTsinghua UniversityBeijingChina Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong KongChina Simpson Querrey Institute Northwestern UniversityEvanstonIllinoisUSA Department of Materials Science and Engineering The Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA Department of Information Technology and Electrical Engineering ETH ZürichZürichSwitzerland School of Electrical and Information Engineering Tianjin UniversityTianjinChina Department of Electrical and Computer Engineering Mississippi State UniversityMississippi StateMississippiUSA Electrical and Electronic Engineering The University of ManchesterManchesterUK Department of Electrical Engineering School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina Key Laboratory of Control of Power Transmission and Conversion(SJTU) Ministry of EducationShanghaiChina College of Electrical Engineering Sichuan UniversityChengduChina Shenzhen Institute of Advanced Technology Chinese Academy of Sciences ShenzhenShenzhenChina School of Electrical Engineering Wuhan UniversityWuhanChina School of Integrated Circuits Tsinghua UniversityBeijingChina Jiangsu Jinxin Electric Appliance Co.Ltd. YangzhouChina Tai'an Tiancheng Safety Evaluation Co. Ltd.Tai'anChina Department Electrical Electronic and Information EngineeringUniversity of BolognaBolognaItaly National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijingChina Laplace Paul Sabatier Universityand CNRSToulouseFrance Electrical and Computer Engineering University of ConnecticutStorrsConnecticutUSA State Key Laboratory of Power Transmission Equipment&System Security and New Technology School of Electrical EngineeringChongqing UniversityChongqingChina Thrust of Sustainable Energy and Environment The Hong Kong University of Science and Technology(Guangzhou)GuangzhouChina De

The 2050 carbon-neutral vision spawns a novel energy structure revolution,and the construction of the future energy structure is based on equipment *** material,as the core of electrical power equipment and electrified transportation asset,faces unprecedented challenges and *** goal of carbon neutral and the urgent need for innovation in electric power equipment and electrification assets are first *** engineering challenges constrained by the insulation system in future electric power equipment/devices and electrified transportation assets are *** materials,including intelligent insulating material,high thermal conductivity insulating material,high energy storage density insulating material,extreme environment resistant insulating material,and environmental-friendly insulating material,are cat-egorised with their scientific issues,opportunities and challenges under the goal of carbon neutrality being *** the context of carbon neutrality,not only improves the understanding of the insulation problems from a macro level,that is,electrical power equipment and electrified transportation asset,but also offers opportunities,remaining issues and challenges from the insulating material *** is hoped that this paper en-visions the challenges regarding design and reliability of insulations in electrical equipment and electric vehicles in the context of policies towards carbon neutrality *** authors also hope that this paper can be helpful in future development and research of novel insulating materials,which promote the realisation of the carbon-neutral vision.

关键词： neutral insulation extreme

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A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models

arXiv

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arXiv 2024年

作者： Ahmad, Muhammad Distefano, Salvatore Khan, Adil Mehmood Mazzara, Manuel Li, Chenyu Li, Hao Aryal, Jagannath Ding, Yao Vivone, Gemine Hong, Danfeng Department of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan Dipartimento di Matematica e Informatica-MIFT University of Messina Messina98121 Italy School of Computer Science University of Hull HullHU6 7RX United Kingdom Institute of Software Development and Engineering Innopolis University Innopolis420500 Russia Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Big Geospatial Data Management Technical University of Munich Munich85521 Germany Department of Infrastructure Engineering University of Melbourne ParkvilleVIC Australia Intelligent Control Laboratory PLA Rocket Force University of Engineering Xi’an710025 China Institute of Methodologies for Environmental Analysis National Research Council Tito85050 Italy School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

Hyperspectral Image Classification (HSC) presents significant challenges owing to the high dimensionality and intricate nature of Hyperspectral (HS) data. While traditional Machine Learning (TML) approaches have demonstrated effectiveness, they often encounter substantial obstacles in real-world applications, including the variability of optimal feature sets, subjectivity in human-driven design, inherent biases, and methodological limitations. Specifically, TML suffers from the curse of dimensionality, difficulties in feature selection and extraction, insufficient consideration of spatial information, limited robustness against noise, scalability issues, and inadequate adaptability to complex data distributions. In recent years, Deep Learning (DL) techniques have emerged as robust solutions to address these challenges. This survey offers a comprehensive overview of current trends and future prospects in HSC, emphasizing advancements from DL models to the increasing adoption of Transformer and Mamba Model architectures. We systematically review key concepts, methodologies, and state-of-the-art approaches in DL for HSC. Furthermore, we investigate the potential of Transformer-based models and the Mamba Model in HSC, detailing their advantages and challenges. Emerging trends in HSC are explored, including in-depth discussions on Explainable AI and Interoperability concepts, alongside Diffusion Models for image denoising, feature extraction, and image fusion. To substantiate the efficacy of various conventional DL models, Transformers, and the Mamba Model, comprehensive experimental results were conducted on three HS datasets, yielding notable accuracies: 99.94% on Pavia University, 99.41% on Indian Pines, and 99.97% on the University of Houston dataset. Additionally, we identify several open challenges and pertinent research questions in the field of HSC. Finally, we outline future research directions and potential applications aimed at enhancing the accuracy and effic

关键词： Convolutional neural networks

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Application of Kronecker convolutions in deep learning technique for automated detection of kidney stones with coronal CT images

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Information sciences 2023年 640卷

作者： Patro, Kiran Kumar Allam, Jaya Prakash Neelapu, Bala Chakravarthy Tadeusiewicz, Ryszard Acharya, U Rajendra Hammad, Mohamed Yildirim, Ozal Plawiak, Pawel Department of Electronics and Communication Engineering Aditya Institute of Technology and Management AP Tekkali532201 India Department of Electronics and Communication Engineering National Institute of Technology Rourkela Odisha Rourkela769008 India Department of Biotechnology and Medical Engineering National Institute of Technology Rourkela Odisha Rourkela769008 India AGH University of Science and Technology Department of Biocybernetics and Biomedical Engineering Krakow Poland School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology Faculty of Computers and Information Menoufia University Shibin El Kom32511 Egypt Department of Software Engineering Firat University Elazig Turkey Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 Krakow31-155 Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland

Kidney stone disease is a serious public health concern that is getting worse with changes in diet, obesity, medical conditions, certain supplements etc. A kidney stone also called a renal calculus, is a hard buildup of urine minerals that form in the kidneys. Computed tomography (CT) is one of the imaging models used to identify kidney stones by clinical experts. Due to the low resolution of these images, sometimes detecting kidney stones is tedious with the naked eye, which may lead to false alarms. In this work, a computer-based diagnosis system with a deep learning technique has been developed as a practical solution to aid clinicians in their diagnosis. The traditional convolutional neural network (CNN)-based deep learning technology can detect stones in the kidney. Still, it suffers from the performance and standard implementation of the convolution operations in convolution layers. A Kronecker product-based convolution technique is incorporated in the proposed deep learning architecture to reduce the redundancy in feature maps without convolution overlapping. Our proposed method helps to make the network more effective by extracting abstract and in-depth features from the input images. The publicly available GitHub kidney stone CT scans are utilized to develop the proposed architecture. Our automated model detected kidney stones with an accuracy of 98.56% utilizing CT images. Our system is more effective than the most recent and cutting-edge techniques developed for identifying kidney stones of any size, including the smallest ones. © 2023 The Author(s)

关键词： Convolution

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Foster noisy label learning by exploiting noise-induced distortion in foreground localization

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Neural Networks 2025年

作者： Ang Chen Feng Xu Xin Lyu Tao Zeng Xin Li College of Computer Science and Software Engineering Hohai University Nanjing 211100 China School of Computer Engineering Jiangsu Ocean University Lianyungang 222005 China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing 211100 China

Large-scale, well-annotated datasets are crucial for training deep neural networks. However, the prevalence of noisy-labeled samples can cause irreversible impairment to the generalization of models. Existing approaches have attempted to mitigate the impact of noisy labels by exploiting the different loss or confidence distributions between clean and noisy data to detect and correct noisy labels. This paper investigates the noise-induced distorting effect on foreground localization by tracking the model’s spatial attention distribution on visual activation maps. We observe that in clean samples, highly responsive regions usually focus on label-relevant foreground regions, whereas in noisy data, the model accidentally attends to uninformative background regions or cluttered object edges due to interference from label noise. Inspired by the observations, we propose a novel two-stage foreground localization-augmented noisy label learning framework named FLSC to concurrently boost the accuracy of sample selection and label correction for robust training. Specifically, FLSC first quantifies noise-induced distortion in foreground localization to foster conventional loss-based selection criteria by calculating the information reduction when deriving the foreground images from the original images based on the attention distribution. Next, we propose a noise-adaptive adversarial erasing strategy, which suppresses background activation by imposing adaptive erasure regularization, to eliminate overfitting to noisy samples while enhancing the learning of robust representations. To the best of our knowledge, it’s the first attempt to exploit localization quality evaluation based on feature activation to address the label noise problem. Extensive experiments on synthetic and real-world datasets validate the superior performance of FLSC compared to state-of-the-art methods.

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PScalpel: A Machine Learning-based Guider for Protein Phase-Separating Behaviour Alteration 39

PScalpel: A Machine Learning-based Guider for Protein Phase-...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Jia Zhu, Liyan Wang, Zhe Zhang, Chenqiu Wu, Yaoxing Cui, Jun Li, Jianqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China MOE Key Laboratory of Gene Function and Regulation Guangdong Province Key Laboratory of Pharmaceutical Functional Genes State Key Laboratory of Biocontrol School of Life Sciences Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Pharmaceutical Functional Genes First Affiliated Hospital of Sun Yat-sen University School of Life Sciences Sun Yat-sen University Guangzhou China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (纸本)157735897X

Missense mutations could affect the Liquid-Liquid Phase Separation (LLPS) propensity of proteins and lead to aberrant phase-separating behaviours, which are recently found to be associated with many diseases including Alzheimer’s and cancer. However, the regulatory role of mutations in LLPS remains unclear due to challenges in accurately characterizing the LLPS ability of mutants, including the high similarity in features, lack of labeled data, and vast amounts of data involved. To bridge this gap and facilitate the discovery of therapeutic strategies, we propose the first machine learning-based guider for protein phase-separating behaviour alteration, PScalpel. PScalpel leverages both structural information and an auxiliary tasks-based graph contrastive learning framework to distinguish the mutants’ LLPS ability, and incorporates a genetic algorithms-based recommendation method to identify mutants with desired LLPS properties. Comprehensive computational and biological experiments validate the effectiveness of PScalpel as a versatile tool for guiding alterations in protein phase separation behavior. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Genetic algorithms

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PRAD: Unsupervised KPI Anomaly Detection by Joint Prediction and Reconstruction of Multivariate Time Series

PRAD: Unsupervised KPI Anomaly Detection by Joint Prediction...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Zhiying Xiong Qilin Fan Kai Wang Xiuhua Li Xu Zhang Qingyu Xiong School of Big Data and Software Engineering Chongqing University Chongqing China Chongqing Key Laboratory of Digital Cinema Art Theory and Technology Chongqing University Chongqing China School of Computer Science and Technology Harbin Institute of Technology Weihai China Research Institute of Cyberspace Security Harbin Institute of Technology Weihai China Haihe Laboratory of Information Technology Application Innovation Tianjin China Department of Computer Science University of Exeter Exeter UK

Detecting anomalies for key performance indicator (KPI) data is of paramount importance to ensure the quality and reliability of network services. However, building the anomaly detection system for KPI is challenging due to the scarcity of abnormal labels and highly dynamic or even unseen patterns. In this paper, we propose an unsupervised KPI anomaly detection framework named PRAD by jointly optimizing prediction-based and reconstruction-based modules. Specifically, PRAD employs two parallel graph attention networks (GATs) to learn metric-oriented and time-oriented relationships. A temporal convolutional attention network (TCAN) is used to capture complex long-term dependencies. Furthermore, to tackle the overfitting issue in the variational auto-encoder (VAE), PRAD designs a reconstruction-based module that combines VAE with a generative adversarial network (GAN). The experimental results on the two public datasets demonstrate that the proposed PRAD outperforms existing anomaly detection methods.

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