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arXiv 2025年

作者： Hu, Yan Gong, Mingdao Qiu, Zhongxi Liu, Jiabao Shen, Hongli Yuan, Mingzhen Zhang, Xiaoqing Li, Heng Lu, Hai Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University Beijing Ophthalmology and Visual Sciences Key Laboratory Beijing China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Retinal image registration is vital for diagnostic therapeutic applications within the field of ophthalmology. Existing public datasets, focusing on adult retinal pathologies with high-quality images, have limited number of image pairs and neglect clinical challenges. To address this gap, we introduce COph100, a novel and challenging dataset known as the Comprehensive Ophthalmology Retinal Image Registration dataset for infants with a wide range of image quality issues constituting the public "RIDIRP" database1. COph100 consists of 100 eyes, each with 2 to 9 examination sessions, amounting to a total of 491 image pairs carefully selected from the publicly available dataset. We manually labeled the corresponding ground truth image points and provided automatic vessel segmentation masks for each image. We have assessed COph100 in terms of image quality and registration outcomes using state-of-the-art algorithms. This resource enables a robust comparison of retinal registration methodologies and aids in the analysis of disease progression in infants, thereby deepening our understanding of pediatric ophthalmic conditions. Copyright © 2025, The Authors. All rights reserved.

关键词： Image registration

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Integrating Generative Artificial Intelligence Into Future Internet of Vehicles [From the Guest Editors]

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IEEE Vehicular Technology Magazine 2025年第2期20卷 40-42页

作者： Gang Sun Hongyang Du Abbas Jamalipour Gabriel-Miro Muntean Lin Cai Jiawen Kang Dusit Niyato University of Electronic Science and Technology of China Chengdu China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong China University of Sydney Sydney NSW Australia Performance Engineering Laboratory Dublin City University Dublin Ireland Department of Electrical and Computer Engineering University of Victoria Victoria BC Canada Guangdong University of Technology Guangzhou China College of Computing and Data Science Nanyang Technological University Singapore Singapore

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Joint Optimization Air Base Stations Deployment and User Association in Heterogeneous Space-Air-Ground Integrated Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Ao, Shaoyou Niu, Yong Han, Zhu Xiong, Lei Wang, Ning Ai, Bo Beijing Jiaotong University Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications School of electronic and Information Engineering Beijing100044 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation Beijing100044 China Zhengzhou University School of Information Engineering Zhengzhou450001 China

With the development of 6 G and beyond, space-air-ground integrated networks (SAGINs) have evolved into key elements to promote high-speed and seamless connections for users. This paper studies a heterogeneous SAGIN with satellite, multiple unmanned aerial vehicles (UAVs) and high-altitude platforms (HAPs) serve as aerial base stations (ABSs) in a scenario where ground base stations (GBSs) are overloaded. To optimize the number of ABSs within the system's power limit and maximize the system's downlink throughput, we optimize the 3D deployment of ABSs and user association in the millimeter wave (mmWave) frequency band. We formulate an optimization problem to satisfy the access quality of service (QoS) requirements. This problem is a mixed- integer nonlinear programming problem. Therefore, we divide it into two subproblems: the heterogeneous network ABS deployment problem and the user association problem. We propose a Broyden-Fletcher-Goldfarb-Shanno (BFGS) formula based sequential quadratic programming (SQP) algorithm for the ABS deployment problem and a coalition game algorithm for the user association problem. Finally, we employ a block coordinate descent (BCD) algorithm to iteratively solve the two subproblems until convergence. Extensive simulations with various parameters and user deployments demonstrate that our algorithm outperforms other selected schemes. © 1967-2012 IEEE.

关键词： Quadratic programming

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Exploring the Impact of Personality Traits on LLM Bias and Toxicity

arXiv

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arXiv 2025年

作者： Wang, Shuo Li, Renhao Chen, Xi Wong, Derek F. Yuan, Yulin Yang, Min NLP2CT Lab Department of Computer and Information Science University of Macau China Shenzhen Key Laboratory for High Performance Data Mining Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Linguistics and Multilingual Studies Nanyang Technological University Singapore Department of Chinese Language and Literature University of Macau China

With the different roles that AI is expected to play in human life, imbuing large language models (LLMs) with different personalities has attracted increasing research interests. While the "personification" enhances human experiences of interactivity and adaptability of LLMs, it gives rise to critical concerns about content safety, particularly regarding bias, sentiment and toxicity of LLM generation. This study explores how assigning different personality traits to LLMs affects the toxicity and biases of their outputs. Leveraging the widely accepted HEXACO personality framework developed in social psychology, we design experimentally sound prompts to test three LLMs’ performance on three toxic and bias benchmarks. The findings demonstrate the sensitivity of all three models to HEXACO personality traits and, more importantly, a consistent variation in the biases, negative sentiment and toxicity of their output. In particular, adjusting the levels of several personality traits can effectively reduce bias and toxicity in model performance, similar to humans’ correlations between personality traits and toxic behaviors. The findings highlight the additional need to examine content safety besides the efficiency of training or fine-tuning methods for LLM personification. They also suggest a potential for the adjustment of personalities to be a simple and low-cost method to conduct controlled text generation. © 2025, CC BY.

关键词： Social psychology

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Adaptive Wavelet Filters as Practical Texture Feature Amplifiers for Parkinson’s Disease Screening in OCT

arXiv

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arXiv 2025年

作者： Zhang, Xiaoqing Shi, Hanfeng Li, Xiangyu Ye, Haili Xu, Tao Li, Na Hu, Yan Lv, Fan Chen, Jiangfan Liu, Jiang Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Ophthalmology Optometry and Vision Science Wenzhou Medical University Wenzhou China Centre for Computational Science and Mathematical Modelling Coventry University Coventry United Kingdom School of Computer Science University of Nottingham Ningbo China Ningbo315100 China School of Ophthalmology and Optometry Wenzhou Medical University Wenzhou325035 China Department of Electronic and Information Engineering Changchun University Changchun130022 China

Parkinson’s disease (PD) is a prevalent neurodegenerative disorder globally. The eye’s retina is an extension of the brain and has great potential in PD screening. Recent studies have suggested that texture features extracted from retinal layers can be adopted as biomarkers for PD diagnosis under optical coherence tomography (OCT) images. Frequency domain learning techniques can enhance the feature representations of deep neural networks (DNNs) by decomposing frequency components involving rich texture features. Additionally, previous works have not exploited texture features for automated PD screening in OCT. Motivated by the above analysis, we propose a novel Adaptive Wavelet Filter (AWF) that serves as the Practical Texture Feature Amplifier to fully leverage the merits of texture features to boost the PD screening performance of DNNs with the aid of frequency domain learning. Specifically, AWF first enhances texture feature representation diversities via channel mixer, then emphasizes informative texture feature representations with the well-designed adaptive wavelet filtering token mixer. By combining the AWFs with the DNN stem, AWFNet is constructed for automated PD screening. Additionally, we introduce a novel Balanced Confidence (BC) Loss by mining the potential of sample-wise predicted probabilities of all classes and class frequency prior, to further boost the PD screening performance and trustworthiness of AWFNet. The extensive experiments manifest the superiority of our AWFNet and BC over state-of-the-art methods in terms of PD screening performance and trustworthiness. © 2025, CC BY-NC-SA.

关键词： Neurodegenerative diseases

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FundusGAN: A Hierarchical Feature-Aware Generative Framework for high-Fidelity Fundus Image Generation

arXiv

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arXiv 2025年

作者： Hou, Qingshan Wang, Meng Cao, Peng Ke, Zou Liu, Xiaoli Fu, Huazhu Zaiane, Osmar R. Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China Alberta Machine Intelligence Institute University of Alberta Edmonton Canada Ophthalmology Yong Loo Lin School of Medicine National University of Singapore Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore

Recent advancements in ophthalmology foundation models such as RetFound have demonstrated remarkable diagnostic capabilities but require massive datasets for effective pre-training, creating significant barriers for development and deployment. To address this critical challenge, we propose FundusGAN, a novel hierarchical feature-aware generative framework specifically designed for high-fidelity fundus image synthesis. Our approach leverages a Feature Pyramid Network within its encoder to comprehensively extract multi-scale information, capturing both large anatomical structures and subtle pathological features. The framework incorporates a modified StyleGAN-based generator with dilated convolutions and strategic upsampling adjustments to preserve critical retinal structures while enhancing pathological detail representation. Comprehensive evaluations on the DDR, DRIVE, and IDRiD datasets demonstrate that FundusGAN consistently outperforms state-of-the-art methods across multiple metrics (SSIM: 0.8863, FID: 54.2, KID: 0.0436 on DDR). Furthermore, disease classification experiments reveal that augmenting training data with FundusGAN-generated images significantly improves diagnostic accuracy across multiple CNN architectures (up to 6.49% improvement with ResNet50). These results establish FundusGAN as a valuable foundation model component that effectively addresses data scarcity challenges in ophthalmological AI research, enabling more robust and generalizable diagnostic systems while reducing dependency on large-scale clinical data collection. Copyright © 2025, The Authors. All rights reserved.

关键词： Ophthalmology

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The impact of indoor environmental quality on tourist accommodation ratings using guest reviews

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Building and Environment 2025年 280卷

作者： Zhang, Fan Seshadri, Karthick Liu, Shichao Santamouris, Matthaios School of Engineering and Built Environment Griffith University SouthportQLD Australia Department of Computer Science and Engineering National Institute of Technology Andhra Pradesh Tadepalligudem India Department of Civil Environmental Architectural Engineering Worcester Polytechnic Institute MA United States High Performance Architecture School of Built Environment University of New South Wales SydneyNSW Australia

Guest satisfaction is pivotal for the hospitality industry, yet the role of Indoor Environmental Quality (IEQ) in shaping guest ratings remains inadequately explored. This study addresses this gap by leveraging web-mining, natural language processing and the Three-Factor Theory to analyse guest reviews of Australian hotels and serviced apartments listed on ***. Findings indicate that most IEQ factors—thermal comfort, indoor air quality, luminous environment, acoustics, space, facilities, cleanliness, and layout—function as Basic Factors. While deficiencies in these areas drive dissatisfaction, their adequacy alone does not significantly enhance satisfaction. Notably, exterior view serves as an Excitement Factor in budget accommodations (three stars or below) but transitions to a Basic Factor in luxury settings. Among all IEQ factors, cleanliness, indoor air quality, and acoustics had the strongest negative impacts when underperforming, whereas satisfactory exterior views, cleanliness, and available space positively influenced guest ratings. The COVID-19 pandemic intensified these effects, heightening guest sensitivity to IEQ deficiencies (e.g., poor air quality) while elevating expectations for high-performing attributes like exterior views and available space. Overall, IEQ factors accounted for 32.8 % of customer ratings in budget hotels and 23.9 % in luxury accommodations. The findings underscore the necessity of ensuring satisfactory performance across all IEQ factors, particularly cleanliness and indoor air quality, while strategically enhancing exterior views and spatial configuration to optimize guest satisfaction. These insights provide hospitality managers and policymakers with data-driven guidance for enhancing guest satisfaction and operational resilience in post-pandemic hospitality settings. © 2025 The Author(s)

关键词： Indoor air pollution

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SPICE modeling of flux-controlled unipolar memristive devices

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Chinese Physics B 2013年第7期22卷 596-601页

作者：方旭东唐玉华吴俊杰朱玄周静黄达 State Key Laboratory of High Performance Computing School of ComputerNational University of Defense Technology Department of Computer Science and Technology School of ComputerNational University of Defense Technology

Unipolar memristive devices are an important kind of resistive switching devices. However, few circuit models of them have been proposed. In this paper, we propose the SPICE modeling of flux-controlled unipolar memristive devices based on the memristance versus state map. Using our model, the flux thresholds, ON and OFF resistance, and compliance current can easily be set as model parameters. We simulate the model in HSPICE using model parameters abstracted from real devices, and the simulation results show that the proposed model caters to the real device data very well, thus demonstrating that the model is correct. Using the same modeling methodology, the SPICE model of charge-controlled unipolar memristive devices could also be developed. The proposed model could be used to model resistive memory cells, logical gates as well as synapses in artificial neural networks.

关键词： unipolar memristive devices memristive SPICE model

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Comparative study of indirect and direct feature extraction algorithms in classifying tea varieties using near-infrared spectroscopy

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Current Research in Food science 2025年 10卷 101065页

作者： Zhou, Xuefan Wu, Xiaohong Wu, Bin School of Electrical and Information Engineering Jiangsu University Zhenjiang China High-tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province Jiangsu University Zhenjiang China Department of Information Engineering Chuzhou Polytechnic Chuzhou China School of Computer Science and Engineering Southeast University Nanjing 211102 China

Tea, a globally cherished beverage, has become an integral part of daily life, particularly in China. Given the extensive variety of teas, each distinguished by unique price points, flavors, and health benefits, effective classification within the tea industry is crucial to address the diverse preferences of consumers. This study utilized indirect and direct feature extraction algorithms to analyze the Near-Infrared (NIR) spectra of various tea varieties and compared their classification outcomes. Principal Component Analysis (PCA) was employed as a dimensionality reduction technique for indirect feature extraction algorithms. The study began with the collection of NIR spectra from different tea varieties, followed by the application of three spectral preprocessing algorithms. Indirect and direct feature extraction algorithms were then used to reduce the dimensionality of the preprocessed data. A K-Nearest Neighbors (KNN) classifier analyzed the dimensionality-reduced data to determine classification accuracy. The findings revealed that the classification accuracies of indirect feature extraction algorithms consistently exceeded those of direct feature extraction algorithms, with the former generally surpassing 90.0 %, while the latter remained lower. This indicates that indirect feature extraction algorithms are more adept at handling complex spectral data. A significant decline in classification accuracy was observed when data were processed with Savitzky-Golay (SG). An in-depth analysis led to the development of an optimization plan incorporating the Successive Projections Algorithm (SPA), which effectively enhanced all classification accuracies to above 90 %. © 2025 The Authors

关键词： Classification Feature extraction Near-infrared spectroscopy Spectral preprocessing Tea

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Empirical topological investigation of practical supply chains based on complex networks

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Chinese Physics B 2017年第11期26卷 144-150页

作者：廖好沈婧吴兴桐陈博奎周明洋 National Engineering Laboratory for Big Data Computing Systems Guangdong Province Key Laboratory of Popular High Performance ComputersCollege of Computer Science and Software Engineering Shenzhen University Department of Computer Science School of Computing National University of Singapore

The industrial supply chain networks basically capture the circulation of social resource, dominating the stability and efficiency of the industrial system. In this paper, we provide an empirical study of the topology of smartphone supply chain network. The supply chain network is constructed using open online data. Our experimental results show that the smartphone supply chain network has small-world feature with scale-free degree distribution, in which a few high degree nodes play a key role in the function and can effectively reduce the communication cost. We also detect the community structure to find the basic functional unit. It shows that information communication between nodes is crucial to improve the resource utilization. We should pay attention to the global resource configuration for such electronic production management.

关键词： China supply chain networks complex networks data science network science

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