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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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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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The Joint Model of Multi-Intent Detection and Slot Filling Based on Bidirectional Interaction Structure

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Wuhan University Journal of Natural Sciences 2025年第1期30卷 21-31页

作者： WANG Changjing ZENG Xianghui WANG Yuxin SUN Yuxin ZUO Zhengkang School of Digital Industry Jiangxi Normal UniversityShangrao 334000JiangxiChina School of Computer Information Engineering Jiangxi Normal UniversityNanchang 330022JiangxiChina Jiangxi Provincial Key Laboratory for High Performance Computing Jiangxi Normal UniversityNanchang 330022JiangxiChina State International Science and Technology Cooperation Base of Networked Supporting Software Jiangxi Normal UniversityNanchang 330022JiangxiChina

Intent detection and slot filling are two important components of natural language *** their relevance,joint training is often performed to improve *** studies mostly use a joint model of multi-intent detection and slot-filling with unidirectional interaction,which improves the overall performance of the model by fusing the intent information in the slot-filling *** this basis,in order to further improve the overall performance of the model by exploiting the correlation between the two,this paper proposes a joint multi-intent detection and slot-filling model based on a bidirectional interaction structure,which fuses the intent encoding information in the encoding part of slot filling and fuses the slot decoding information in the decoding part of intent *** results on two public multi-intent joint training datasets,MixATIS and MixSNIPS,show that the bidirectional interaction structure proposed in this paper can effectively improve the performance of the joint *** addition,in order to verify the generalization of the bidirectional interaction structure between intent and slot,a joint model for single-intent scenarios is proposed on the basis of the model in this *** model also achieves excellent performance on two public single-intent joint training datasets,CAIS and SNIPS.

关键词： natural language understanding multi-intent detection slot filling bidirectional interaction joint training

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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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RFAE: A high-robust feature selector based on fractal autoencoder

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Expert Systems with Applications 2025年 285卷

作者： Ou, Jingfeng Li, Jiawei Xia, Zhiliang Dai, Shurui Guo, Yan Jiang, Limin Tang, Jijun College of Engineering Southern University of Science and Technology Shenzhen 518055 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518107 China Department of Public Health Sciences University of Miami Miami FL33136 United States Centre for High Performance Computing Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China

Feature selection aims to consistently identify an optimal subset of features that effectively represents the entire dataset or enhances performance in downstream tasks. While deep learning-based approaches have made significant progress in feature selection, they continue to face key challenges, including instability in selected features, limited receptive fields in feature-selection layers due to architectural constraints, and suboptimal utilization of available sample information. To address these limitations, we propose the Robust Fractal Autoencoder (RFAE), an enhanced variant of the Fractal Autoencoder (FAE) designed to improve feature selection stability and adaptability. RFAE introduces three critical advancements: 1) Novel utilization of weight exponentiation to rectify the concern of FAE selecting a reduced number of features than designated. 2) Adoption of a dynamic and tailored strategy to optimize feature selection weights during the training process. 3) Introduction of a optional classification module, facilitating extension to supervised feature selection scenarios. We systematically evaluate RFAE against 14 established feature selection methods. Our experiments span 14 publicly available benchmark datasets, a large-scale GEO gene expression dataset, and a synthetic dataset with known ground-truth features. The results demonstrate that RFAE consistently selects features that achieve lower reconstruction errors while ensuring higher stability across repeated experiments, highlighting its robustness and effectiveness in feature selection tasks. © 2025

关键词： Feature Selection

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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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On construction of a BioGrid platform for parallel bioinformatics applications

Proceedings of the ACM Symposium on Applied Computing

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Proceedings of the ACM Symposium on Applied computing 2007年 144-145页

作者： Yang, Chao-Tung Han, Tsu-Fen Kan, Heng-Chuan High Performance Computing Laboratory Department of Computer Science and Information Engineering Tunghai University Taichung 40704 Taiwan Southern Business Unit National Center for High-performance Computing Hsinshi Tainan County 74147 Taiwan

ISBN: (纸本)1595934804;9781595934802

In this paper, we report on implementing an experimental distributed computing application for bioinformatics consisting of basic high-performance computing environments (Grid and PC Cluster systems), multiple interfaces at user portals that provide useful graphical interfaces to enable biologists who are not IT specialists to benefit directly from the use of high-performance technology. Copyright 2007 ACM.

关键词： Bioinformatics

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Programming for scientific computing on peta-scale heterogeneous parallel systems

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Journal of Central South University 2013年第5期20卷 1189-1203页

作者：杨灿群吴强唐滔王锋薛京灵 State Key Laboratory of High Performance Computing(National University of Defense Technology) School of Computer Science and Engineering University of New South Wales

Peta-scale high-perfomlance computing systems are increasingly built with heterogeneous CPU and GPU nodes to achieve higher power efficiency and computation throughput. While providing unprecedented capabilities to conduct computational experiments of historic significance, these systems are presently difficult to program. The users, who are domain experts rather than computer experts, prefer to use programming models closer to their domains （e.g., physics and biology） rather than MPI and OpenME This has led the development of domain-specific programming that provides domain-specific programming interfaces but abstracts away some performance-critical architecture details. Based on experience in designing large-scale computing systems, a hybrid programming framework for scientific computing on heterogeneous architectures is proposed in this work. Its design philosophy is to provide a collaborative mechanism for domain experts and computer experts so that both domain-specific knowledge and performance-critical architecture details can be adequately exploited. Two real-world scientific applications have been evaluated on TH-IA, a peta-scale CPU-GPU heterogeneous system that is currently the 5th fastest supercomputer in the world. The experimental results show that the proposed framework is well suited for developing large-scale scientific computing applications on peta-scale heterogeneous CPU/GPU systems.

关键词： heterogeneous parallel system programming framework scientific computing GPU computing molecular dynamic

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Computation speedup in the dynamic simulation of MEMS by macromodels

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自然科学进展(英文版) 2003年第3期13卷 219-227页

作者： LIN Wuzhong LIANG Yanchun LEE Kwok Hong LIM Siak Piang LEE Heow Pueh Institute of High Performance Computing Singapore 117528 Institute of High Performance Computing Singapore 117528 College of Computer Science and Technology Jilin UniversityChangchun 130012 China Department of Mechanical Engineering National University of Singapore Singapore 119260

A doubly clamped microbeam actuated by electrostatic force with squeezed gas film damping is a well-known and standard micro-device in microelectromechanical system (MEMS) for many researchers to demonstrate how reduced-order dynamic macromodel is an effective way to faithfully capture the device behaviors. However it still takes time to directly recompute the time-dependant nonlinear terms in macromodels which are generated by a proper orthogonal decomposition (POD) method with Galerkin procedure at every time step during the macromodel simulation. This paper proposes two methods for speeding up the computation of macromodel simulations. In the first method, the computation speedup is achieved based on the concept of precomputation upon the basis functions are available. In the second method, cubic splines approximation is used to interpolate the basis functions and their first and second derivatives, and spatial integration is performed by application of the Gaussian quadrature. Numerical results show both methods could enhance the efficiency of the macromodel simulation compared with our previous computation results.

关键词： MEMS macromodels model order reduction proper orthogonal decomposition

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