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SPICE modeling of flux-controlled unipolar memristive devices

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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Universal conservation laws of the wave-particleentanglement triad:theory and experiment

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Light(Science & Applications) 2025年第3期14卷 861-868页

作者： Ziheng Ding Yaohao Deng Shao-Ming Fei Si-Qi Zhou Xiaojiong Chen Ziwen Rui Zhihao Ma Yunlong Xiao Jianwei Wang School of Mathematical Sciences MOE-LSCShanghai Jiao Tong UniversityShanghai 200240China State Key Laboratory for Mesoscopic Physics School of PhysicsPeking UniversityBejing 100871China Bejing Academy of Quantum Information Sciences Beijing 100871China School of Mathematical Sciences Capital Normal UniversityBeijing 100048China Max Planck Institute for Mathematics in the Sciences Leipzig 04103Germany Department of Physics and Astronomy Stony Brook UniversityNew YorkNY11794-3800USA Shanghai Seres Information Technology Co. LtdShanghai 200040China shenzhen Institute for Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China A*STAR Quantum Innovation Centre(Q.InC) Agency for ScienceTechnology and Research(A*STAR)2Fusionopolis WayInnovis\#08-03Singapore138634Republic of Singapore institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A*STAR)1 Fusionopolis WayConnexis\#16-16Singapore 138632Republic of Singapore

When observed,a quantum system exhibits either wave-like or particle-like properties,depending on how it is ***,this duality is affected by the entanglement of the system with its quantum memory,raising a fundamental question:how are wave-particle duality and entanglement related?Here,we broaden the scope of wave-particle duality to include entanglement,introduce universal conservation laws for the wave-particle-entanglement triad,and perform demonstrations on silicon-integrated nanophotonic quantum *** experiments not only mark the first confirmation of universal conservation laws but also highlight the potential of integrated photonics for exploring complex quantum phenomena in high-dimensional systems.

关键词： high dimensional systems universal conservation laws entanglement nanophotonic quantum chips quantum systems wave particle duality quantum system

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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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A generalized model of TiOx-based memristive devices and its application for image processing

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Chinese Physics B 2017年第9期26卷 70-81页

作者：张江伟汤振森许诺王耀孙红辉王之元方粮 State Key Laboratory of High Performance Computing National University of Defense Technology School of Computer National University of Defense Technology Department of Electrical and Computer Engineering University of Pittsburgh Department of Material Science and Engineering College of EngineeringSeoul National University School of Computer National University of Defense Technology

Memristive technology has been widely explored, due to its distinctive properties, such as nonvolatility, high density,versatility, and CMOS compatibility. For memristive devices, a general compact model is highly favorable for the realization of its circuits and applications. In this paper, we propose a novel memristive model of TiOx-based devices, which considers the negative differential resistance（NDR） behavior. This model is physics-oriented and passes Linn＇s criteria. It not only exhibits sufficient accuracy（IV characteristics within 1.5% RMS）, lower latency（below half the VTEAM model）,and preferable generality compared to previous models, but also yields more precise predictions of long-term potentiation/depression（LTP/LTD）. Finally, novel methods based on memristive models are proposed for gray sketching and edge detection applications. These methods avoid complex nonlinear functions required by their original counterparts. When the proposed model is utilized in these methods, they achieve increased contrast ratio and accuracy（for gray sketching and edge detection, respectively） compared to the Simmons model. Our results suggest a memristor-based network is a promising candidate to tackle the existing inefficiencies in traditional image processing methods.

关键词： memristor modeling memristor-based network gray sketching edge detection

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Solving constrained traveling salesman problems by genetic algorithms

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自然科学进展(英文版) 2004年第7期14卷 631-637页

作者： WU Chunguo LIANG Yanchun LEE Heowpueh LU Chun LIN Wuzhong College of Computer Science and Technology Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering Ministry of Education of China Changchun 130012 China Institute of High Performance Computing Singapore 117528 Singapore Institute of High Performance Computing Singapore 117528 Singapore

Three kinds of constrained traveling salesman problems (TSP) arising from application problems, namely the open route TSP, the end-fixed TSP, and the path-constrained TSP, are proposed. The corresponding approaches based on modified genetic algorithms (GA) for solving these constrained TSPs are presented. Numerical experiments demonstrate that the algorithm for the open route TSP shows its advantages when the open route is required, the algorithm for the end-fixed TSP can deal with route optimization with constraint of fixed ends effectively, and the algorithm for the path-constraint could benefit the traffic problems where some cities cannot be visited from each other.

关键词： constrained traveling salesman problem genetic algorithm Hamiltonian path open route fixed end

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F2UE-NET: An Uncertainty-Driven Dynamic Expert Network for Long-Tail Retinal Disease Classification

F2UE-NET: An Uncertainty-Driven Dynamic Expert Network for L...

引用

IEEE International Symposium on Biomedical Imaging

作者： Junyong Zhao Tian Lin Meng Wang Liang Sun Haoyu Chen Daoqiang Zhang Huazhu Fu MOE Key Laboratory of Brain Computer Intelligence Technology College of Artificial Intelligence Nanjing University of Aeronautics and Astronautics Nanjing China Joint Shantou International Eye Center Shantou University and the Chinese University of Hong Kong Shantou Guangdong China Department of Ophthalmology Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore Agency for Science Technology and Research (A*STAR) Institute of High Performance Computing (IHPC) Singapore Singapore

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

The automatic diagnosis of retinal diseases presents a significant challenge in medical image analysis, primarily due to the long-tail distribution in existing retinal datasets. This imbalance creates substantial difficulties in model training and performance. To address this issue, we propose an uncertainty-driven dynamic expert network for long-tail retinal disease classification, called $\mathrm{F}^{2}\text{UE}$ -Net. Specifically, we apply Low-Rank Adaptation (LoRA) technology to fine-tune the foundation model, enhancing its feature extraction capabilities and overall performance. Additionally, we introduce an uncertainty-driven dynamic expert participation strategy, which assigns appropriate weights to different categories during training, ensuring high accuracy even for rare, tail-end diseases. Experimental results on a long-tail fundus disease dataset demonstrate that $\mathrm{F}^{2}$ UE outperforms state-of-the-art methods in long-tail retinal disease classification.

关键词： Training Heavily-tailed distribution Image analysis Foundation models Biological system modeling Tail Retina Feature extraction Medical diagnostic imaging Diseases

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