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arXiv 2025年

作者： Guo, Juncen Liu, Yang Zhu, Xiaogunag Sun, Lianlong Teng, Liangyu Wu, Jingyi Li, Di Zhou, Wei Song, Liang Academy for Engineering and Technology Fudan University Shanghai200433 China DataLab: Data Science and Informatics University of California DavisCA95616 United States University of Rochester New York14627 United States Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China Academy for Computer Science and Informatics Cardiff University Wales CF24 4AG United Kingdom

Class-Incremental Learning (CIL) enables models to continuously learn new class knowledge while retaining previous classes, facilitating adaptation and evolution in dynamic, real-world environments. Traditional CIL methods primarily rely on visual features, which limits their effectiveness in complex, multimodal scenarios. In contrast, VLMs show promising potential for enhancing CIL by leveraging pre-trained knowledge and integrating multi-modal semantic cues such as text and vision. However, existing approaches struggle to mitigate catastrophic forgetting while preserving the generalization strengths of VLMs across diverse modalities. To address these challenges, we propose a Feature Calibration Enhanced Parameter Synthesis (FCPS) framework. Specifically, FCPS introduces a dynamic parameter adjustment mechanism that iteratively calibrates the contribution of original visual features to the final class decision, thus preserving the model’s intrinsic generalization capability across modalities. Simultaneously, parameter integration enables effective knowledge transfer, maintaining a balance between acquiring new class representations and preserving old knowledge. Experimental results on popular benchmarks (e.g., CIFAR100 and ImageNet100) validate the superiority of the proposed method. Copyright © 2025, The Authors. All rights reserved.

关键词： Knowledge transfer

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Path optimization method for the sign problem caused by the fermion determinant

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Physical Review D 2025年第9期111卷 094503-094503页

作者： Kazuki Hisayoshi Kouji Kashiwa Yusuke Namekawa Hayato Takase Department of Computer Science and Engineering Faculty of Information Engineering Fukuoka Institute of Technology Fukuoka 811-0295 Japan Education and Research Center for Artificial Intelligence and Data Innovation Hiroshima University Hiroshima 730-0053 Japan

The path optimization method with machine learning is applied to the one-dimensional massive lattice Thirring model, which has the sign problem caused by the fermion determinant. This study aims to investigate how the path optimization method works for the sign problem. We show that the path optimization method successfully reduces statistical errors and reproduces the analytic results. We also examine an approximation of the Jacobian calculation in the learning process and show that it gives consistent results with those without an approximation.

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A Unified Analysis for Finite Weight Averaging

arXiv

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arXiv 2024年

作者： Wang, Peng Shen, Li Tao, Zerui Sun, Yan Zheng, Guodong Tao, Dacheng School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan China School of Cyber Science & Technology Shenzhen Campus of Sun Yat-sen University Shenzhen China RIKEN Center for Advanced Intelligence Project Tokyo Japan Faculty of Engineering The University of Sydney Australia School of Computer and Data Science Nanyang Technological University Singapore

Averaging iterations of Stochastic Gradient Descent (SGD) have achieved empirical success in training deep learning models, such as Stochastic Weight Averaging (SWA), Exponential Moving Average (EMA), and LAtest Weight Averaging (LAWA). Especially, with a finite weight averaging method, LAWA can attain faster convergence and better generalization. However, its theoretical explanation is still less explored since there are fundamental differences between finite and infinite settings. In this work, we first generalize SGD and LAWA as Finite Weight Averaging (FWA) and explain their advantages compared to SGD from the perspective of optimization and generalization. A key challenge is the inapplicability of traditional methods in the sense of expectation or optimal values for infinite-dimensional settings in analyzing FWA’s convergence. Second, the cumulative gradients introduced by FWA introduce additional confusion to the generalization analysis, especially making it more difficult to discuss them under different assumptions. Extending the final iteration convergence analysis to the FWA, this paper, under a convexity assumption, establishes a convergence bound O(log (T/k) /√T), where k ∈ [1, T/2] is a constant representing the last k iterations. Compared to SGD with O(log(T)/√T), we prove theoretically that FWA has a faster convergence rate and explain the effect of the number of average points. In the generalization analysis, we find a recursive representation for bounding the cumulative gradient using mathematical induction. We provide bounds for constant and decay learning rates and the convex and non-convex cases to show the good generalization performance of ***, experimental results on several benchmarks verify our theoretical results. © 2024, CC BY.

关键词： Stochastic systems

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Neural Network Driven by Density and Parallel Features for Field-Road Mode Mining

Neural Network Driven by Density and Parallel Features for F...

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International Conference on computer Supported Cooperative Work in Design

作者： Fengqi Hao Xiyuan Zhao Cunxiang Bian Jinqiang Bai Qingyan Ding Faculty of Data Science City University of Macau Macau China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Field-road mode mining (FRMM) has gained increasing attention because of its crucial role in machinery management. As the Global Navigation Satellite System (GNSS) is widely applied in agricultural machinery, many methods based on motion and spatial-temporal features of GNSS trajectory data have been proposed for FRMM. However, these methods ignore the density and parallel features of GNSS points. The two features are useful for FRMM because field points usually have a higher density and more parallel points than road points. Therefore, a neural network driven by density and parallel features is proposed for accurate FRMM. Firstly, a statistical method is designed to extract the density feature (i.e., the number of its neighbor points) and parallel feature (i.e., the number of approximately parallel points in its neighbors) of each point. Then, the two features and eight motion features (e.g., speed, direction, etc.) are fed into a neural network to extract valuable latent features. Finally, a linear classifier is used to identify the field and road categories of GNSS points based on the latent features. Experimental results show that our method outperforms state-of-the-art methods and achieves the accuracy of 91.69% and 86.44% on public Wheat and Paddy datasets, respectively.

关键词： Global navigation satellite system Accuracy Statistical analysis Federated learning Roads Neural networks Time series analysis Feature extraction Trajectory data mining

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EML-SAM: Incorporating Multi-Scale Fusion in SAM for Cine-CMR Segmentation

EML-SAM: Incorporating Multi-Scale Fusion in SAM for Cine-CM...

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International Conference on computer Supported Cooperative Work in Design

作者： Huijuan Hao Wenpeng Wang Qingyan Ding Haibo Cheng Fengqi Hao Hu Zhang Jinqiang Bai Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Faculty of Data Science City University of Macau Macau China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Accurate and reproducible assessment of myocardial conditions is essential for the diagnosis of prior infarctions, cardiomyopathies, and inflammatory diseases. Although cardiac magnetic resonance imaging (CMR) is regarded as the gold standard for evaluating myocardial anatomy and function, manual segmentation remains labor-intensive and susceptible to variability. The demand for high-resolution images, dense predictions, and comprehensive segmentation across all phases of the cardiac cycle introduces significant challenges for Cine-CMR analysis. To address these issues, we present EML-SAM, an innovative interactive segmentation model based on the Segment Anything Model (SAM). EML-SAM incorporates an early-mid-late (EML) multi-scale fusion strategy, effectively mitigating the premature dilution of interaction information, thereby reducing segmentation errors and enhancing feature utilization. Furthermore, the model utilizes a multi-scale linear attention (MLA) transformer to establish an efficient global receptive field. Comprising 12 Transformer layers, the model integrates multi-scale attention with multi-scale perceptrons, striking a balance between computational efficiency and capacity. The proposed methodology offers a robust and efficient solution for high-resolution Cine-CMR segmentation, delivering accurate and real-time segmentation performance throughout the various stages of temporal changes within the cardiac cycle.

关键词： Image segmentation Accuracy Computational modeling Refining Myocardium Transformers Real-time systems Reproducibility of results Reliability Diseases

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Higher-order approximation for uncertainty quantification in time series analysis

arXiv

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arXiv 2022年

作者： Betken, Annika Düker, Marie-Christine University of Twente Faculty of Electrical Engineering Mathematics and Computer Science Enschede Netherlands FAU Erlangen-Nürnberg Department of Data Science Erlangen Germany

For time series with high temporal correlation, the empirical process converges rather slowly to its limiting distribution. Many statistics in change-point analysis, goodness-of-fit testing and uncertainty quantification admit a representation as functionals of the empirical process and therefore inherit its slow convergence. Inference based on the asymptotic distribution of those quantities becomes highly impacted by relatively small sample sizes. We assess the quality of higher-order approximations of the empirical process by deriving the asymptotic distribution of the corresponding error terms. Based on the limiting distribution of the higher-order terms, we propose a novel approach to calculate confidence regions for statistical quantities such as the median. In a simulation study, we compare coverage rate and size of our confidence regions with those based on the asymptotic distribution of the empirical process and highlight some of our method's benefits. Copyright © 2022, The Authors. All rights reserved.

关键词： Time series analysis

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Hybrid Optimization Framework for Energy Efficiency Maximization in NOMA-Aided Internet of Vehicles with beyond Diagonal RIS

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IEEE Internet of Things Journal 2025年

作者： Yi, Xiaoxi Wang, Han Zhang, Chi Song, Huiling Dutta, Ashit Kumar Yichun University College of Physical Science and Technology Jiangxi Yichun336000 China City University of Macau Faculty of Data Science Macao999078 China Yichun University College of Physical Science and Engineering Yichun336000 China Harbin University of Commerce College of Basic Science Heilongjiang Harbin150006 China AlMaarefa University Ad Diriyah College of Applied Sciences Department of Computer Science and Information Systems Riyadh13713 Saudi Arabia

The integration of beyond diagonal reconfigurable intelligent surfaces (BD-RIS) with non-orthogonal multiple access (NOMA) in the Internet of Vehicles (IoV) networks presents the transformative potential for 6G vehicular communications, promising unprecedented gains in energy and spectral efficiency. However, existing research fails to address the joint optimization of BD-RIS configuration, NOMA power allocation, and dynamic IoV association-a critical oversight given their inherent interdependence in practical deployment scenarios. Current literature predominantly examines these components in isolation, with conventional RIS architectures and orthogonal multiple access schemes, leading to suboptimal performance in high-mobility vehicular environments. This work bridges this gap by proposing a novel hybrid optimization framework for NOMA-aided BD-RIS assisted IoV networks. The proposed framework simultaneously optimizes the IoV association with the base station (BS), NOMA power allocation, and BD-RIS phase shift design to maximize the energy efficiency of the system while ensuring the minimum signal-to-interference plus noise ratios (SINRs) of IoVs. The proposed framework is formulated as non-linear optimization due to joint decision variables and rate expressions of IoVs, resulting in an NP-hard problem where achieving a joint optimal solution is computationally complex. To handle this complexity, the original joint formulation is decomposed into three subproblems: IoV association with BS, BS power allocation, and BD-RIS phase shift design. An efficient iterative solution is then developed through the synergistic combination of deep reinforcement learning, first-order Taylor expansion, and manifold optimization methods. For comprehensive evaluation, we introduce a NOMA-aided conventional RIS assisted IoV framework as a benchmark. Extensive numerical results based on Monte Carlo simulations demonstrate that the proposed NOMA-aided BD-RIS assisted IoV network achieves

关键词： Reinforcement learning

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Dynamic Exit Selection and Counting Area Adjustment for Efficient Crowd Evacuation in Asymmetrical Layouts

Dynamic Exit Selection and Counting Area Adjustment for Effi...

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Information Technology (ACIT)

作者： Omar Khair Alla Alidmat Raed Alazaidah Mohammad Arabiat Hayel Khafajeh Mohammad Aljaidi Umi Kalsom Yusof Ali Elrashidi Computer Science Department Zarqa University Zarqa Jordan Data Science and Artifical Inteligence Department Zarqa University Zarqa Jordan Faculty of Information Technology Zarqa University Zarqa Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

This research introduces an innovative methodology to address the significant challenge of evacuating large crowds from buildings with multiple exits, particularly in scenarios with asymmetrical exit configurations and high population densities. The proposed model incorporates dynamic counting areas that adjust according to evacuee density, alongside a probabilistic exit selection mechanism influenced by enhanced dynamic parameters—congestion, distance, and empty. It also accounts for heterogeneous agent behaviors to more accurately represent evacuee dynamics in complex, asymmetrical environments with dense crowds. Key findings indicate that the model effectively alleviates bottlenecks at congested exits, optimizes exit usage, and enhances overall evacuation safety. Comparative analyses demonstrate that the proposed model significantly improves evacuation efficiency and reduces travel distances compared to previous models. Simulation results indicate that agent density directly affects evacuation time, with lower congestion levels facilitating faster egress. These advancements lead to more precise evacuation time predictions and crowd flow assessments, offering valuable insights for refining emergency egress strategies in complex settings. Future research will focus on refining the model with real-world data to expand its applicability in diverse settings, such as stadiums and public transportation hubs, thereby enhancing emergency preparedness and response.

关键词： Analytical models Simulation Refining Layout Probabilistic logic data models Safety Information technology Public transportation Population density

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Early Stage Design of a mHealth Intervention for Managing Gestational Diabetes Mellitus in Bangladeshi Women 25th

Early Stage Design of a mHealth Intervention for Managing...

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25th International Conference on Human-computer Interaction, HCII 2023

作者： Islam, Ashraful Ahmed, Eshtiak Islam, Md Rakibul EMPATHY Lab Center for Computational and Data Sciences Independent University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh Faculty of Information Technology and Communication Sciences Tampere University Tampere Finland Computer Science Department University of Wisconsin - Eau Claire Eau ClaireWI United States

ISBN: (纸本)9783031492112

Gestational Diabetes Mellitus (GDM) is a rising concern worldwide, particularly in low-resource countries such as Bangladesh, where access to healthcare facilities is limited and awareness of GDM management is inadequate. This work proposes an early-stage design of user interfaces (UIs) for a mobile health (mHealth) intervention using the Bengali language that allows users to input daily measures of blood glucose, weight, and other GDM-related health data. The intervention is designed to provide users with personalized feedback based on their input and the necessary coaching for managing GDM. It also reminds users to take medication if prescribed by their doctors. The app has six key features: blood glucose tracking, food logging, medication reminders, activity tracking, educational resources, and personalized recommendations. Overall, this work provides a valuable contribution to the field of mHealth-based maternal and child health in Bangladesh. However, further research is necessary to evaluate the effectiveness and feasibility of the intervention in real-world scenarios. Such evaluations will provide valuable insights into its potential impact and help refine the design to better serve the needs of GDM patients in Bangladesh. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： mHealth

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Construction Equipment Performance with Cloud data Analysis for Predictive Maintenance 6

Construction Equipment Performance with Cloud Data Analysis ...

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Seeni, Senthil Kumar Maheswari, P. Konidhala, Jothibabu Suguna, M. Raja Ishwarya, M.V. Srinivasan, S. Cognizant Technology Solutions Department of Mobile Application Development Buffalo GroveIL United States Srm Institute of Science and Technology Faculty of Management Studies Kattankulathur Chennai India University of Technology and Applied Sciences- Al Musannah Department of Electronics and Communication Engineering South Al Batina Musanna Oman Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Agni College of Technology Artificial intelligence and Data science Department Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350381689

The research uses Internet of Things technology for predictive maintenance in the construction sector. The system addresses incorporating Internet of Things sensors into construction machinery to enable real-time monitoring of operating parameters. The study suggests a proactive maintenance strategy that may anticipate equipment breakdowns by examining the data obtained and using sophisticated prediction models. With this strategy, construction teams can arrange maintenance activities systematically, which cuts down on downtime and helps avoid expensive interruptions. The abstract emphasizes the advantages of this practice, which include reducing unexpected downtime as much as possible, extending the life of equipment, and making the most of available resources. The study proves the usefulness of the IoT-based predictive maintenance approach via empirical investigations and the analysis of case studies. This validates the strategy's efficacy, contributing to enhanced equipment performance and increased work efficiency in the construction industry. © 2024 IEEE.

关键词： Predictive maintenance

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