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arXiv 2025年

作者： Cui, Shuang Li, Yi Li, Jiangmeng Tang, Xiongxin Su, Bing Xu, Fanjiang Xiong, Hui National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences China Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China China Department of Computer Science & Engineering the Hong Kong University of Science and Technology Hong Kong

Single image defocus deblurring (SIDD) aims to restore an all-in-focus image from a defocused one. Distribution shifts in defocused images generally lead to performance degradation of existing methods during out-of-distribution inferences. In this work, we gauge the intrinsic reason behind the performance degradation, which is identified as the heterogeneity of lens-specific point spread functions. Empirical evidence supports this finding, motivating us to employ a continual test-time adaptation (CTTA) paradigm for SIDD. However, traditional CTTA methods, which primarily rely on entropy minimization, cannot sufficiently explore task-dependent information for pixel-level regression tasks like SIDD. To address this issue, we propose a novel Siamese networks-based continual test-time adaptation framework, which adapts source models to continuously changing target domains only requiring unlabeled target data in an online manner. To further mitigate semantically erroneous textures introduced by source SIDD models under severe degradation, we revisit the learning paradigm through a structural causal model and propose Causal Siamese networks (CauSiam). Our method leverages large-scale pre-trained vision-language models to derive discriminative universal semantic priors and integrates these priors into Siamese networks, ensuring causal identifiability between blurry inputs and restored images. Extensive experiments demonstrate that CauSiam effectively improves the generalization performance of existing SIDD methods in continuously changing domains. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Visualizing functional network connectivity differences using an explainable machine-learning method

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Physiological Measurement 2025年第4期46卷 2025 Apr 17页

作者： Sendi, Mohammad S E Itkyal, Vaibhavi S Edwards-Swart, Sabrina J Chun, Ji Ye Mathalon, Daniel H Ford, Judith M Preda, Adrian van Erp, Theo G M Pearlson, Godfrey D Turner, Jessica A Calhoun, Vince D Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA United States Department of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States McLean Hospital Harvard Medical School BostonMA United States Tri-institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology Emory University AtlantaGA United States Department of Neuroscience Emory University AtlantaGA United States Department of Computer Science Georgia State University AtlantaGA United States Department of Psychiatry Weill Institute of Neurosciences University of California San FranciscoCA United States Mental Health Service Veterans Affairs San Francisco Healthcare System San FranciscoCA United States Department of Psychiatry and Human Behavior University of California IrvineCA United States Department of Psychiatry School of Medicine Yale University New HavenCT United States Department of Psychiatry and Behavioral Health College of Medicine The Ohio State University ColumbusOH United States

Objective. Functional network connectivity (FNC) estimated from resting-state functional magnetic resonance imaging showed great information about the neural mechanism in different brain disorders. But previous research has mainly focused on standard statistical learning approaches to find FNC features separating patients from control. While machine learning models can improve classification accuracy, they often lack interpretability, making it difficult to understand how they arrive at their decisions. Approach. Explainable machine learning helps address this issue by identifying which features contribute most to the model’s predictions. In this study, we introduce a novel framework leveraging SHapley Additive exPlanations (SHAPs) to identify crucial FNC features distinguishing between two distinct population classes. Main results. Initially, we validate our approach using synthetic data. Subsequently, applying our framework, we ascertain FNC biomarkers distinguishing between, controls and schizophrenia (SZ) patients with accuracy of 81.04% as well as middle aged adults and old aged adults with accuracy 71.38%, respectively, employing random forest, XGBoost, and CATBoost models. Significance. Our analysis underscores the pivotal role of the cognitive control network (CCN), subcortical network (SCN), and somatomotor network in discerning individuals with SZ from controls. In addition, our platform found CCN and SCN as the most important networks separating young adults from older. © 2025 The Author(s). Published on behalf of Institute of Physics and Engineering in Medicine by IOP Publishing Ltd.

关键词： Explainable AI Functional Network Connectivity Machine Learning Neuroimaging

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Mixed H_2/H_∞ Control Using a Fuzzy Singularly Perturbed Model with Multiple Perturbation Parameters for Gust Load Alleviation

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Tsinghua Science and technology 2011年第4期16卷 344-351页

作者：袁源胡叶楠孙富春 Department of Computer Science and Technology Tsinghua University Beijing Institute of Astronautical System Engineering

This paper presents a mixed H21H∞ control using fuzzy singularly perturbed model （FSPM） with multiple perturbation parameters. Since FSPM with multiple perturbation parameters is an extension of models with a single perturbation parameter, the theoretical results are applicable to a larger class of systems described by multiple time scale nonlinear models, such as flying aircraft and flexible space robots. The parameter-independent solution of the mixed H21H∞ controller was obtained in the form of linear matrix inequalities （LMIs）. The application of this approach to gust load alleviation of a flying vehicle verifies its effectiveness and flexibility.

关键词： fuzzy singularly perturbed model (FSPM) mixed H21∞Hcontrol multiple perturbation parameters gust load alleviation flying vehicle

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Quantum Linear Multistep Method for Using a Quantum Oracle with Differential Equations

arXiv

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arXiv 2025年

作者： Park, Kyoung Keun Choi, Kwangyeul Kim, Minwoo Song, Giwon Kim, Taehyun Department of Computer Science and Engineering Seoul National University Seoul08826 Korea Republic of Automation and System Research Institute Seoul National University Seoul08826 Korea Republic of NextQuantum Seoul National University Seoul08826 Korea Republic of Department of Physics and Astronomy Seoul National University Seoul08826 Korea Republic of Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Institute of Computer Technology Seoul National University Seoul08826 Korea Republic of Inter university Semiconductor Research Center Seoul National University Seoul08826 Korea Republic of Institute of Applied Physics Seoul National University Seoul08826 Korea Republic of

Differential equations are a crucial mathematical tool used in a wide range of applications. If the solution to an initial value problem (IVP) can be transformed into an oracle, it can be utilized in various fields such as search and optimization, achieving quadratic speedup with respect to the number of candidates compared to its classical counterpart. In the past, attempts have been made to implement such an oracle using the Euler method. In this study, we propose a quantum linear multistep method (QLMM) that applies the linear multistep method, commonly used to numerically solve IVPs on classical computers, to generate a numerical solution of the IVP for use in a quantum oracle. We also propose a method to find the optimal form of QLMM for a given IVP. Finally, through computer simulations, we derive the QLMM formulation for an example IVP and show that the solution from the optimized QLMM can be used in an optimization problem. © 2025, CC BY.

关键词： Initial value problems

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Mobile to server face recognition: A system overview

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World Academy of Science, Engineering and technology 2010年第70期70卷 767-771页

作者： Ismail, Nurulhuda Md. Sabri, Mas Idayu Department of System and Computer Technology University of Malaya 50603 Kuala Lumpur Malaysia

This paper presents a system overview of Mobile to Server Face Recognition, which is a face recognition application developed specifically for mobile phones. Images taken from mobile phone cameras lack of quality due to the low resolution of the cameras. Thus, a prototype is developed to experiment the chosen method. However, this paper shows a result of system backbone without the face recognition functionality. The result demonstrated in this paper indicates that the interaction between mobile phones and server is successfully working. The result shown before the database is completely ready. The system testing is currently going on using real images and a mock-up database to test the functionality of the face recognition algorithm used in this system. An overview of the whole system including screenshots and system flow-chart are presented in this paper. This paper also presents the inspiration or motivation and the justification in developing this system.

关键词： Face recognition

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Development of an embedded smart home management scheme

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International Journal of Smart Home 2013年第2期7卷 15-26页

作者： Perumal, Thinagaran Sulaiman, Nasir Md Sharif, Khaironi Yatim Ramli, Abd Rahman Leong, Chui Yew Department of Computer Science Faculty of Computer Science and Information Technology UPM Serdang Malaysia Department of Computer and Communication System Engineering UPM Serdang Malaysia

This paper describes the development of an embedded smart home management scheme over the Ethernet network. The platform of the smart home management system is built using bespoke embedded system design. An embedded control module developed by exploiting the Web Services mechanism, consist of 15 monitoring channels based on XML SOAP standards. Each channel is integrated to dedicated smart home management scheme and performs bi-directional real-time control. In the event of server unavailability, a mobile based communication module using GSM has been deployed as an alternate management mechanism. The proposed embedded-enabled solution offers bi-directional real-time management as well as optimized performance for smart home environment.

关键词： Interoperability

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SAND: semi-automated adaptive network defense via programmable rule generation and deployment

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Science China(Information Sciences) 2022年第7期65卷 121-138页

作者： Haoyu CHEN Deqing ZOU Hai JIN Shouhuai XU Bin YUAN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabHubei Engineering Research Center on Big Data Security School of Cyber Science and EngineeringHuazhong University of Science and Technology Department of Computer Science University of Colorado at Colorado Springs

Cyber security is dynamic as defenders often need to adapt their defense postures. The state-ofthe-art is that the adaptation of network defense is done manually(i.e., tedious and error-prone). The ideal solution is to automate adaptive network defense, which is however a difficult problem. As a first step towards automation, we propose investigating how to attain semi-automated adaptive network defense(SAND). We propose an approach extending the architecture of software-defined networking, which is centered on providing defenders with the capability to program the generation and deployment of dynamic defense rules enforced by network defense tools. We present the design and implementation of SAND, as well as the evaluation of the prototype implementation. Experimental results show that SAND can achieve agile and effective dynamic adaptations of defense rules(less than 15 ms on average for each operation), while only incurring a small performance overhead.

关键词： network defense adaptive defense automated defense programmable defense security services software-defined networking security management

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Monte Carlo analysis of a low power domino gate under parameter fluctuation

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Journal of Semiconductors 2009年第12期30卷 113-117页

作者：汪金辉吴武臣宫娜侯立刚彭晓宏高大明 VLSI and System Laboratory Beijing University of Technology Department of Computer Science and Engineering State University of New York at Buffalo

Using the multiple-parameter Monte Carlo method, the effectiveness of the dual threshold voltage technique （DTV） in low power domino logic design is analyzed. Simulation results indicate that under significant temperature and process fluctuations, DTV is still highly effective in reducing the total leakage and active power consumption for domino gates with speed loss. Also, regarding power and delay characteristics, different structure domino gates with DTV have different robustness against temperature and process fluctuation.

关键词： domino gate temperature and process fluctuation effectiveness

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Mobile Collaborative Informal Learning Design: Study of Collaborative Effectiveness Using Activity Theory

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2012年第3期6卷 34-41页

作者： Baloch, Hasnain Zafar Abdulrhaman, Aziziah Ihad, Noorminshah A. University of Technology Malaysia Malaysia Faculty of Computer Science and Information system Department of Information system University of Technology Malaysia Malaysia

Smart Mobile Devices (SMD) are there for many years but using them as learning tools started to emerge as new research area. The trend to merge collaborative learning methodology by using mobile devices in informal context is important for implementation of Learner Centric Learning (LCL). Survey and numerous studies show that more than 95% of students in colleges are users of these smart mobile devices in developed world. Developing counties are also catching up and we can see this percentage is almost same in university level in these countries. Students are using SMDs for learning in some form. Higher education Institutions also try to embark their E-learning to Mobile learning (ML). The aim of this paper is to do propose operational framework for designing Mobile Collaborative Informal learning activities using SMDs. Show results of experimental and case study done to study the Mobile Collaborative Informal learning using Activity Theory (AT). Core Components of framework are Mobile Learning Activities/Objects, Wireless/Mobile Smart devices, Collaborative knowledge and Collaborative learning. The research mention here is its infancy stage. © 2012, International Federation of Engineering Education Societies (IFEES). All rights reserved.

关键词： Students

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Examining the Fourier Spectrum of Speech Signal from a Time-Frequency Perspective for Automatic Depression Level Prediction

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IEEE Transactions on Affective Computing 2025年

作者： Niu, Mingyue Tao, Jianhua He, Yongjun Zhang, Shiqing Li, Ming YanShan University School of Information Science and Engineering Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province China Tsinghua University Department of Automation Bejing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Taizhou University Institute of Intelligent Information Processing Taizhou318000 China Zhejiang Normal University Zhejiang Key Laboratory of Intelligent Education Technology and Application Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China

Currently, many studies use Fourier amplitude spectra of speech signals to predict depression levels. However, those works often treat Fourier amplitude spectra as images or sequences to capture depression cues using convolutional neural networks or multilayer perceptrons. Therefore, they ignore the complex element composition and time-frequency attributes of Fourier spectra, which is not conducive to capturing the differences among individuals with different depression levels. For this reason, we construct a Time-Frequency Self-Embedding (TFSE) module, which not only stores the correlation relationship among real (imaginary) parts of Fourier spectra of different subjects from the time-frequency perspective, but also maintain the physical properties of data through the weight embedding process. Besides, Global Average Pooling (GAP) or linear layers are difficult to balance both temporal and frequency dimensions in the vectorization process. Therefore, we construct a Time-Frequency Tensor Vectorization (TFTV) module, which summarizes each channel along time and frequency dimensions, and then generates the vectorization result by integrating various channels. In this way, we combine TFSE and TFTV modules to form our SpectrumFormer model for predicting depression levels. Evaluation indicators on AVEC 2013 and AVEC 2014 depression databases imply the progressiveness of our model. © 2010-2012 IEEE.

关键词： Depression level prediction Real and imaginary parts Tensor vectorization Time-frequency attributes

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