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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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Single-Atom Nanozymes Linked Immunosorbent Assay for Sensitive Detection of Aβ1-40:A Biomarker of Alzheimer’s Disease

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Research 2020年第1期2020卷 777-787页

作者： Zhaoyuan Lyu Shichao Ding Nan Zhang Yang Zhou Nan Cheng Maoyu Wang Mingjie Xu Zhenxing Feng Xiangheng Niu Yuan Cheng Chao Zhang Dan Du Yuehe Lin School of Mechanical and Materials Engineering Washington State UniversityPullmanWA 99164USA Institute of High Performance Computing Institute of High Performance ComputingSingapore 138632 School of Chemical Biologicaland Environmental EngineeringOregon State UniversityCorvallisOR 97331USA Irvine Materials Research Institute(IMRI) University of CaliforniaIrvineCA 92697USA Department of Electrical and Computer Engineering National University of SingaporeEngineering Drive 3Singapore 117583

Single-atom nanozymes(SANs)possess unique features of maximum atomic utilization and present highly assembled enzyme-like structure and remarkable enzyme-like *** introducing SANs into immunoassay,limitations of ELISA such as low stability of horseradish peroxidase(HRP)can be well addressed,thereby improving the performance of the *** this work,we have developed novel Fe-N-C single-atom nanozymes(Fe-N_(x)SANs)derived from Fe-doped polypyrrole(PPy)nanotube and substituted the enzymes in ELISA kit for enhancing the detection sensitivity of amyloid beta *** indicate that the Fe-N_(x)SANs contain high density of single-atom active sites and comparable enzyme-like properties as HRP,owing to the maximized utilization of Fe atoms and their abundant active sites,which could mimic natural metalloproteases *** designed SAN-linked immunosorbent assay(SAN-LISA)demonstrates the ultralow limit of detection(LOD)of 0.88 pg/mL,much more sensitive than that of commercial ELISA(9.98 pg/mL).The results confirm that the Fe-N_(x)SANs can serve as a satisfactory replacement of enzyme labels,which show great potential as an ultrasensitive colorimetric immunoassay.

关键词： Alzheimer thereby limitations

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IEEE Non-Volatile Memory Systems and Applications Symposium (NVMSA)

作者： Shih-Chun Chou Yi-Shen Chen Ping-Xiang Chen Yuan-Hao Chang Ming-Chang Yang Tei-Wei Kuo Yu-Fang Chen Yu-Ming Chang Department of Computer Science and Information Engineering National Taiwan University Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Department of Computer Science National Tsing Hua University Institute of Information Science Academia Sinica Department of Computer Science and Engineering The Chinese University of Hong Kong Mohamed bin Zayed University of Artificial Intelligence High Performance and Scientific Computing Center National Taiwan University Wolley Inc.

Recently, solid-state drives (SSDs) have been widely used in modern storage systems because of their excellent access performance and high energy efficiency. Therefore, secure deletion is critical for users to protect their data privacy against unauthorized access. However, the inherent access characteristics of SSDs bring in a grand challenge to thoroughly remove the sensitive data from the storage devices or make them inaccessible permanently. Moreover, the existing erase mechanism of SSDs may indirectly reduce the SSD lifetime when performing a secure deletion because of the massive block erases. To address these issues, we propose FSD, a file-related secure deletion that exploits the file information hints via multi-streamed SSDs to optimize the data allocation to alleviate the impact of secure deletion for prolonging SSD lifetime. The extensive experiments show that FSD satisfies the requirement of secure deletion and significantly prolongs the SSD lifetime by mitigating about 80% of block erases as well as extra data movement.

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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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Convergence-Aware Optimal Checkpointing for Exploratory Deep Learning Training Jobs

SSRN

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SSRN 2024年

作者： Li, Hongliang Wang, Zichen Zhao, Hairui Zhang, Meng Li, Xiang Xu, Haixiao College of Computer Science and Technology Jilin University Qianjin street 2699 Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering The Ministry of Education Qianjin street 2699 Jilin Changchun130012 China High Performance Computing Center Qianjin street 2699 Jilin Changchun130012 China

Training Deep Learning (DL) models are becoming more time-consuming, thus interruptions to the training processes are inevitable. Existing fault-tolerant work adopted checkpoint/recovery mechanism from traditional HPC platforms that makes periodical persistent copies of model states to save execution progress. These checkpoints have fixed time intervals that they are evenly placed across the training. We can obtain an optimal checkpointing interval for an HPC job with the precondition that the progress of a job is proportional to its execution time. Unfortunately, it is not the case in DL model training where a DL training job yields diminishing returns across its lifetime. It makes the early progress of a DL training job more valuable than the later ones. Even placement of checkpoints would either increase the risks in the early stages or waste resources overprotecting the latter stages. Meanwhile, the issue can get amplified for exploratory training jobs, where early terminations are common. Moreover, in data parallelism, the state-of-art quality-driven scheduling strategies allocate more resources for the early stages of a job than the later ones to accelerate the training progress which further amplifies the issue. This paper introduces a novel checkpointing interval problem for exploratory DL training jobs based on model convergence progress. We present COCI, an approach to compute optimal checkpointing configuration for a DL training job, minimizing the fault-tolerant overhead, including checkpointing cost and recovery cost. We implement COCI based on state-of-art iteration-level checkpointing mechanism, as a pluggable module compatible with PyTorch. COCI requires no extra user input. We conduct comprehensive evaluations with real DL application setups. The experimental results show that COCI reduces up to 40.18% fault-tolerant overhead compared to existing state-of-the-art DL fault-tolerant methods in serial scenario, 57.26% in data parallel scenario and 63.8

关键词： Iterative methods

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A First-Order Multi-Gradient Algorithm for Multi-Objective Bi-Level Optimization

arXiv

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arXiv 2024年

作者： Ye, Feiyang Lin, Baijiong Cao, Xiaofeng Zhang, Yu Tsang, Ivor W. Department of Computer Science and Engineering Southern University of Science and Technology China Australian Artificial Intelligence Institute University of Technology Sydney Australia China School of Artificial Intelligence Jilin University China Shanghai Artificial Intelligence Laboratory China Centre for Frontier AI Research Agency for Science Technology and Research Singapore Institute of High Performance Computing Agency for Science Technology and Research School of Computer Science and Engineering Nanyang Technological University Singapore

In this paper, we study the Multi-Objective Bi-Level Optimization (MOBLO) problem, where the upper-level subproblem is a multi-objective optimization problem and the lower-level subproblem is for scalar optimization. Existing gradient-based MOBLO algorithms need to compute the Hessian matrix, causing the computational inefficient problem. To address this, we propose an efficient first-order multi-gradient method for MOBLO, called FORUM. Specifically, we reformulate MOBLO problems as a constrained multi-objective optimization (MOO) problem via the value-function approach. Then we propose a novel multi-gradient aggregation method to solve the challenging constrained MOO problem. Theoretically, we provide the complexity analysis to show the efficiency of the proposed method and a non-asymptotic convergence result. Empirically, extensive experiments demonstrate the effectiveness and efficiency of the proposed FORUM method in different learning problems. In particular, it achieves state-of-the-art performance on three multi-task learning benchmark datasets. The code is available at https://***/Baijiong-Lin/FORUM. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization algorithms

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Is Dataset Quality Still a Concern in Diagnosis Using Large Foundation Model?

arXiv

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arXiv 2024年

作者： Lin, Ziqin Li, Heng Li, Zinan Fu, Huazhu Liu, Jiang Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Institute of High Performance Computing Agency for Science Technology and Research Singapore Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

Recent advancements in pre-trained large foundation models (LFM) have yielded significant breakthroughs across various domains, including natural language processing and computer vision. These models have been particularly impactful in the domain of medical diagnostic tasks. With abundant unlabeled data, an LFM has been developed for fundus images using the Vision Transformer (VIT) and a self-supervised learning framework. This LFM has shown promising performance in fundus disease diagnosis across multiple datasets. On the other hand, deep learning models have long been challenged by dataset quality issues, such as image quality and dataset bias. To investigate the influence of data quality on LFM, we conducted explorations in two fundus diagnosis tasks using datasets of varying quality. Specifically, we explored the following questions: Is LFM more robust to image quality? Is LFM affected by dataset bias? Can fine-tuning techniques alleviate these effects? Our investigation found that LFM exhibits greater resilience to dataset quality issues, including image quality and dataset bias, compared to typical convolutional networks. Furthermore, we discovered that overall fine-tuning is an effective adapter for LFM to mitigate the impact of dataset quality issues. Copyright © 2024, The Authors. All rights reserved.

关键词： Image quality

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DESIGN AND DEVELOPMENT OF MORPHOLOGICAL ANALYZER FOR TIGRIGNA VERBS USING HYBRID APPROACH

Research Square

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Research Square 2023年

作者： Gebremeskel, Hagos Gebremedhin Chong, Feng Heyan, Huang Beijing Engineering Research Center of High Volume Language Information Processing Cloud Computing Applications Laboratory School of Computer Science and Technology Beijing Institute of Technology China Department of Computer Science Mekelle University Tigray Mekelle231 Ethiopia

Morphological analyzer is the base for various high-level NLP applications such as information retrieval, spell checking, grammar checking, machine translation, speech recognition, POS tagging and automatic sentence construction. This paper is carefully designed for design and analysis of morphological analyzer Tigrigna verbs using hybrid of memory learning and rules based approaches. The experiment have conducted using Python 3 where TiMBL algorithms IB2 and TRIBL2, and Finite State Transducer rules are used. The performance of the system has been evaluated using 10 fold cross validation technique. Testing was conducted using optimized parameter settings for regular verbs and linguistic rules of the Tigrigna language allomorph and phonology for the irregular verbs. The accuracy of the memory based approach with optimized parameters of TiMBL algorithm IB2 and TRIBL2 was 93.24% and 92.31%, respectively. Finally, the hybrid approach had an actual performance of 95.6% using linguistic rules for handling irregular and copula verbs. © 2023, CC BY.

关键词： Machine learning

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Conception Et Optimisation Analytique Simultanée Defiltres Micro-Ondes À Cavités Rectangulaires Superposéespour Applications Radar

SSRN

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SSRN 2024年

作者： Djianga, Anicet Nzepang Mbinack, Clement Eyebe, Guy Ayissi Zhao, Ping Fouda, Jean Sire Armand Eyebe Department of Physics University of Yaounde I PO Box: 812-Yaoude Yaounde Cameroon Department of Electrical and Computer Engineering University of Quebec Trois-Rivieres Canada The state Key Laboratory of Electromechanical Integrated Manufacturing of High-Performance Electronic Equipments Xidian University Xi’an710071 China

In this paper, a method for the rapid design of filters with superimposed rectangular cavities is proposed. It startswith a good initialization of the filter dimensions from the targeted coupling matrix, which serves as the basis forcalculating the bandwidth of the inter-cavity couplings (CBW) and the group delay (GD) of the input/output ***, an analytical and simultaneous optimization of all filter dimensions is applied. It is based on the extractionof the S-parameter coupling matrix from Model-based Vector Fitting (MVF) coupled to the Generalized IsospectralFlow Method (GIFM). The differences between the coefficients of the targeted matrix and the extracted matrix are thencalculated, and depending on their positive or negative signs, appropriate adjustments are applied to the filter *** CAD process was successfully used to design one single-band and one dual-band 6th-order microwave filters withsuperimposed-cavity , with respective center frequencies of 10 GHz and 9.99 GHz, and taking into account millingconstraints for eventual manufacture. © 2024, The Authors. All rights reserved.

关键词： Microwave filters

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Topic-Guided Self-Introduction Generation for Social Media Users

arXiv

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arXiv 2023年

作者： Xu, Chunpu Li, Jing Li, Piji Yang, Min Department of Computing The Hong Kong Polytechnic University Hong Kong College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China Shenzhen Key Laboratory for High Performance Data Mining Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

Millions of users are active on social media. To allow users to better showcase themselves and network with others, we explore the auto-generation of social media self-introduction, a short sentence outlining a user’s personal interests. While most prior work profiles users with tags (e.g., ages), we investigate sentence-level self-introductions to provide a more natural and engaging way for users to know each other. Here we exploit a user’s tweeting history to generate their self-introduction. The task is non-trivial because the history content may be lengthy, noisy, and exhibit various personal interests. To address this challenge, we propose a novel unified topic-guided encoder-decoder (UTGED) framework;it models latent topics to reflect salient user interest, whose topic mixture then guides encoding a user’s history and topic words control decoding their self-introduction. For experiments, we collect a large-scale Twitter dataset, and extensive results show the superiority of our UTGED to the advanced encoder-decoder models without topic modeling. 1 © 2023, CC BY.

关键词： User profile

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