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A cross population study of retinal aging biomarkers with longitudinal pre-training and label distribution learning

NPJ digital medicine 2025年第1期8卷 344页

作者： Zhen Yu Ruiye Chen Peng Gui Wei Wang Imran Razzak Hamid Alinejad-Rokny Xiaomin Zeng Xianwen Shang Lei Zhang Xiaohong Yang Honghua Yu Wenyong Huang Huimin Lu Peter van Wijngaarden Mingguang He Zhuoting Zhu Zongyuan Ge The AIM for Health Lab Monash University Melbourne VIC Australia. Faculty of Information Technology Monash University Melbourne VIC Australia. Centre for Eye Research Australia University of Melbourne Melbourne VIC Australia. Ophthalmology Department of Surgery University of Melbourne Melbourne VIC Australia. School of Computer Science and Engineering Artificial Intelligence Wuhan Institute of Technology Wuhan China. State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangzhou China. Department of Computational Biology MBZUAI Abu Dhabi UAE. Graduate School of Biomedical Engineering University of New South Wales Sydney NSW Australia. School of Optometry The Hong Kong Polytechnic University Hong Kong China. School of Translational Medicine Monash University Melbourne VIC Australia. Department of Ophthalmology Guangdong Academy of Medical Sciences Guangdong Provincial People's Hospital Guangzhou China. School of Automation Southeast University Nanjing China. Centre for Eye Research Australia University of Melbourne Melbourne VIC Australia. mingguang.he@polyu.edu.hk. Ophthalmology Department of Surgery University of Melbourne Melbourne VIC Australia. mingguang.he@polyu.edu.hk. School of Optometry The Hong Kong Polytechnic University Hong Kong China. mingguang.he@polyu.edu.hk. Centre for Eye Research Australia University of Melbourne Melbourne VIC Australia. zzhu@cera.org.au. Ophthalmology Department of Surgery University of Melbourne Melbourne VIC Australia. zzhu@cera.org.au. The AIM for Health Lab Monash University Melbourne VIC Australia. zongyuan.ge@monash.edu. Faculty of Information Technology Monash University Melbourne VIC Australia. zongyuan.ge@monash.edu. Monash-Airdoc Research Centre Melbourne VIC Australia. zongyuan.ge@monash.edu.

Retinal age has emerged as a promising biomarker of aging, offering a non-invasive and accessible assessment tool. We developed a deep learning model to estimate retinal age with enhanced accuracy, leveraging retinal images from diverse populations. Our approach integrates self-supervised learning to capture chronological information from both snapshot and sequential images, alongside a progressive label distribution learning module to model biological aging variability. Trained and validated on healthy cohorts (34,433 participants from the UK Biobank and three Chinese cohorts), the model achieved a mean absolute error of 2.79 years, surpassing previous methods. When applied to broader populations, analysis of the retinal age gap-the difference between retina-predicted and chronological age-revealed associations with increased risks of all-cause mortality and multiple age-related diseases. These findings highlight the potential of retinal age as a reliable biomarker for predicting survival and aging outcomes, supporting targeted risk management and precision health interventions.

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Optimal denial-of-service attack energy management over an SINR-based network

arXiv

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arXiv 2018年

作者： Qin, Jiahu Li, Menglin Shi, Ling Kang, Yu Department of Automation University of Science and Technology of China Hefei230027 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Automation State Key Laboratory of Fire Science Institute of Advanced Technology University of Science and Technology of China Hefei230027 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Chinese Academy of Sciences Beijing100190 China

We consider a scenario in which a DoS attacker with the limited power resource and the purpose of degrading the system performance, jams a wireless network through which the packet from a sensor is sent to a remote estimator to estimate the system state. To degrade the estimation quality most effectively with a given energy budget, the attacker aims to solve the problem of how much power to obstruct the channel each time, which is the recently proposed optimal attack energy management problem. The existing works are built on an ideal network model in which the packet dropout never occurs when the attack is absent. To encompass wireless transmission losses, we introduce the signal-to-interference-plus-noise ratio-based network. First we focus on the case when the attacker employs the constant power level. To maximize the expected terminal estimation error at the remote estimator, we provide some sufficient conditions for the existence of an explicit solution to the optimal static attack energy management problem and the solution is constructed. Compared with the existing result in which corresponding sufficient conditions work only when the system matrix is normal, the obtained conditions in this paper are viable for a general system and shown to be more relaxed. For the other important index of system performance, the average expected estimation error, the associated sufficient conditions are also derived based on a different analysis approach with the existing work. And a feasible method is presented for both indexes to seek the optimal constant attack power level when the system fails to meet the proposed sufficient conditions. Then when the real-time ACK information can be acquired, the attacker desires a time-varying power attack strategy, based on which a Markov decision process (MDP) based algorithm is designed to solve the optimal dynamic attack energy management problem. We further study the optimal tradeoff between attack energy and system degradation. Spec

关键词： Signal interference

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Randomized consensus based distributed kalman filtering over wireless sensor networks

arXiv

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arXiv 2018年

作者： Qin, Jiahu Wang, Jie Shi, Ling Kang, Yu Department of Automation University of Science and Technology of China Hefei230027 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Automation State Key Laboratory of Fire Science Institute of Advanced Technology University of Science and Technology of China Hefei230027 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China

This paper is concerned with developing a novel distributed Kalman filtering algorithm over wireless sensor networks based on randomized consensus strategy. Compared with centralized algorithm, distributed filtering techniques require less computation per sensor and lead to more robust estimation since they simply use the information from the neighboring nodes in the network. However, poor local sensor estimation caused by limited observability and network topology changes which interfere the global consensus are challenging issues. Motivated by this observation, we propose a novel randomized gossip based distributed Kalman filtering algorithm. information exchange and computation in the proposed algorithm can be carried out in an arbitrarily connected network of nodes. In addition, the computational burden can be distributed for a sensor which communicates with a stochastically selected neighbor at each clock step under schemes of gossip algorithm. In this case, the error covariance matrix changes stochastically at every clock step, thus the convergence is considered in a probabilistic sense. We provide the mean square convergence analysis of the proposed algorithm. Under a sufficient condition, we show that the proposed algorithm is quite appealing as it achieves better mean square error performance theoretically than the noncooperative decentralized Kalman filtering algorithm. Besides, considering the limited computation, communication and energy resources in the wireless sensor networks, we propose an optimization problem which minimizes the average expected state estimation error based on the proposed algorithm. To solve the proposed problem efficiently, we transform it into a convex optimization problem. And a sub-optimal solution is attained. Examples and simulations are provided to illustrate the theoretical results. Copyright © 2018, The Authors. All rights reserved.

关键词： Kalman filters

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Multi-factorial evolutionary algorithm based on M2M decomposition 11th

Multi-factorial evolutionary algorithm based on M2M decompos...

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11th International Conference on Simulated Evolution and Learning, SEAL 2017

作者： Mo, Jiajie Fan, Zhun Li, Wenji Fang, Yi You, Yugen Cai, Xinye Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Shantou515063 China Department of Electronic Engineering Shantou University Shantou515063 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu210016 China

ISBN: (纸本)9783319687582

This paper proposes a decomposition-based multi-objective multi-factorial evolutionary algorithm (MFEA/D-M2M). The MFEA/D-M2M adopts the M2M approach to decompose multi-objective optimization problems into multiple constrained sub-problems for enhancing the diversity of population and convergence of sub-regions. An machine learning model augmented version is also been implemented, which utilized discriminative models for pre-selecting solutions. Experimental studies on nine multi-factorial optimization (MFO) problem sets are conducted. The experimental results demonstrated that MFEA/D-M2M outperforms the vanilla MFEA on six MFO benchmark problem sets and achieved comparable results on the other three problem sets with partial intersection of global optimal. © Springer International Publishing AG 2017.

关键词： Evolutionary algorithms

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Anatomy of an online misinformation network

arXiv

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arXiv 2018年

作者： Shao, Chengcheng Hui, Pik-Mai Wang, Lei Jiang, Xinwen Flammini, Alessandro Menczer, Filippo Ciampaglia, Giovanni Luca College of Computer National University of Defense Technology China School of Informatics Computing and Engineering Indiana University Bloomington United States MOE Key Laboratory of Intelligent Computing and Information Processing Xiangtan University China Indiana University Network Science Institute Bloomington United States

Massive amounts of fake news and conspiratorial content have spread over social media before and after the 2016 US Presidential Elections despite intense fact-checking efforts. How do the spread of misinformation and fact-checking compete? What are the structural and dynamic characteristics of the core of the misinformation diffusion network, and who are its main purveyors? How to reduce the overall amount of misinformation? To explore these questions we built Hoaxy, an open platform that enables large-scale, systematic studies of how misinformation and fact-checking spread and compete on Twitter. Hoaxy filters public tweets that include links to unverified claims or fact-checking articles. We perform k-core decomposition on a diffusion network obtained from two million retweets produced by several hundred thousand accounts over the six months before the election. As we move from the periphery to the core of the network, fact-checking nearly disappears, while social bots proliferate. The number of users in the main core reaches equilibrium around the time of the election, with limited churn and increasingly dense connections. We conclude by quantifying how effectively the network can be disrupted by penalizing the most central nodes. These findings provide a first look at the anatomy of a massive online misinformation diffusion network. Copyright © 2018, The Authors. All rights reserved.

关键词： Social networking (online)

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A new certificate-based aggregate signature scheme for wireless sensor networks

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Journal of information Hiding and Multimedia Signal processing 2018年第5期9卷 1264-1280页

作者： Chen, Jian-Neng Zou, Fu-Min Wu, Tsu-Yang Zhou, Yu-Ping College of computer Minnan Normal University ZhangzhouFujian363000 China Beidou Navigation and Smart Traffic Innovation Center of Fujian Province FuzhouFujian350008 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology China National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology FuzhouFujian350008 China College of Computer Science and Engineering Shandong University of Science and Technology QingdaoShandong266590 China Key Laboratory of Applied Mathematics Putian University Fujian Province University PutianFujian351100 China

Data transmission is one of the important basic technologies in wireless sensor networks (WSN). Due to the limited energy, storage capacity, computing power and communication ability of sensor nodes, the efficiency of data transmission is greatly limited. Certificate-based aggregate signature scheme provides an efficient way to combine numerous signatures into one short signature. In this paper, we propose a certificate-based aggregate signature scheme in WSN. In the random oracle model and under the computational Diffie-Hellman (CDH) Problem and bilinear Diffie-Hellman (BDH), we demonstrate that our scheme is provably secure against forgery attack. The performance analysis demonstrates that our scheme provides an efficient way for data transmission and is suitable in WSN. © 2018, Ubiquitous International. All rights reserved.

关键词： Data transfer

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Double Time Slot RFID Anti-collision Algorithm based on Gray Code

Double Time Slot RFID Anti-collision Algorithm based on Gray...

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ISCC 2017-the 5th International Conference on information Science and Cloud Computing

作者： Hongwei Deng Lang Li Hui Wang School of Computer Science and Technology Hengyang Normal University School of Information Science and Engineering Central South University Hunan Provincial Key Laboratory of Intelligent Information Processing and Application

To solve the radio frequency identification problem,an anti-collision algorithm based on the Gray code(BSGC) was developed. The proposed algorithm reduces the effort required to search for tag prefixes in accordance with Gray code regulations and enhances the identification speed through efficient division and branching of the collision tags. The results of simulation and subsequent analysis of the proposed algorithm confirm that it effectively reduces the number of collisions and transmission delays,enhances the throughput rate,and increases the tag identification efficiency.

关键词： tag Double Time Slot RFID Anti-collision Algorithm based on Gray Code

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Automatic detection of follicle ultrasound images based on improved Faster R-CNN

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Journal of Physics: Conference Series 2019年第4期1187卷

作者： Tianlong Zeng Jun Liu School of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430081 P. R. China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan 430081 P. R. China

Follicle Ultrasonic images detection technology plays an important role in the monitoring of bull-follicle. Because of follicle ultrasound image containing lots of speckle noise and fuzzy edges, the traditional image detection algorithm is difficult to get better detection results on the ultrasonic image, and the traditional image detection algorithm needs to carry out sample feature extraction for each image, which is time-consuming and time-consuming and labor-intensive. According to the characteristics of the cattle follicle ultrasound image sets, this paper proposes a model of image detection based on improved deep learning Faster R - CNN to automatically detect cattle ovarian follicles, through joint VGG-16 different network layer characteristic figure to replace single deepest characteristic figure, retain the deep semantic characteristics at the same time, also keep the shallow characterization information. The experimental results show that this method has a better effect on the ultrasonic image detection of bovine follicle.

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An improved adaptive weighted median filter algorithm

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Journal of Physics: Conference Series 2019年第4期1187卷 042107-042107页

作者： Song, Yuqin Liu, Jun School of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430081 P. R. China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan 430081 P. R. China

An improved adaptive weighted median filtering method is proposed to deal with the interference noise of ultrasonic RF signal. Firstly, edge pixel points are determined to be filtered by the method of extending edge points; secondly, mean value is used to replace the median value which considered to be noise points; finally, weighted smoothing processing is carried out. The final experimental results in this paper show that the proposed method has better effect on RF signal processing.

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A Subpixel Target Detection Approach to Hyperspectral Image Classification

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IEEE Transactions on Geoscience and Remote Sensing 2017年第9期55卷 5093-5114页

作者： Xue, Bai Yu, Chunyan Wang, Yulei Song, Meiping Li, Sen Wang, Lin Chen, Hsian-Min Chang, Chein-I Center for Hyperspectral Imaging in Remote Sensing Information and Technology College Dalian Maritime University Dalian China Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County BaltimoreMD21250 United States Key Laboratory of Spectral Imaging Technology Chinese Academy of Sciences Xi'an China State Key Laboratory of Integrated Services Networks Xi'an China School of Physics and Optoelectronic Engineering Xidian University Xi'an China Department of Medical Research Taichung Veterans General Hospital Taichung Taiwan Department of Computer Science and Information Management Providence University Taichung02912 Taiwan

Hyperspectral image classification faces various levels of difficulty due to the use of different types of hyperspectral image data. Recently, spectral-spatial approaches have been developed by jointly taking care of spectral and spatial information. This paper presents a completely different approach from a subpixel target detection view point. It implements four stage processes, a preprocessing stage, which uses band selection (BS) and nonlinear band expansion, referred to as BS-then-nonlinear expansion (BSNE), a detection stage, which implements constrained energy minimization (CEM) to produce subpixel target maps, and an iterative stage, which develops an iterative CEM (ICEM) by applying Gaussian filters to capture spatial information, and then feeding the Gaussian-filtered CEM-detection maps back to BSNE band images to reprocess CEM in an iterative manner. Finally, in the last stage Otsu's method is applied to converting ICEM-detected real-valued maps to discrete values for classification. The entire process is called BSNE-ICEM. Experimental results demonstrate BSNE-ICEM, which has advantages over support vector machine-based approaches in many aspects, such as easy implementation, fewer parameters to be used, and better false classification and precision rates. © 1980-2012 IEEE.

关键词： Support vector machines

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