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31st International Conference on Multimedia Modeling, MMM 2025

作者： Liu, Jiajie Zhang, Zhibin Engineering Research Center of Ecological Big Data Ministry of Education Beijing China Key Laboratory of Wireless Networks and Mobile Computing Inner Mongolia University Hohhot010021 China

ISBN: (纸本)9789819620609

Most existing Salient Object Detection (SOD) methods focus on achieving better performance, often resulting in models with a large number of parameters. However, there is limited research on lightweight models in this field. To address this gap, our goal is to maintain performance while reducing the number of network parameters. Thanks to the development of large-kernel convolutions in recent years, we have improved the U2Net as the base network by adding our lightweight dual large-kernel fusion module. Our module better utilizes the depth information of U2Net and, due to the large receptive field of large-kernel convolutions, better captures the relationships between image elements. This allows our network to remain lightweight while maintaining performance. We designed a large-kernel (DLK) fusion module and a lightweight dual grouped large-kernel Unet network (DGLKUNET). Our network uses SRUS (an improved Residual U-blocks module, RUS) to construct DGLKUNET that predicts image contours and labels. Compared to the base RUS network (U2Net), our network reduces the number of parameters by up to 70% and the computational cost by 40% while maintaining performance. Evaluation results on five datasets demonstrate the performance of our lightweight network. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Object recognition

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Improving Adaptive Runoff Forecasts in data-Scarce Watersheds Through Personalized Federated Learning 27th

Improving Adaptive Runoff Forecasts in Data-Scarce Watershe...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Xie, Zaipeng Zhang, Xiangqin Wang, Yunfei Jie, Xuanyao Fang, Wenhao Cai, Yanping Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9783031781827

Runoff forecasting plays a crucial role in water resource management and flood mitigation, but it often faces significant challenges due to data deficiency and decentralized datasets. Inadequate hydrological data in many watersheds hinders the development of accurate prediction models, while organizational barriers and concerns about data privacy lead to information silos, preventing effective collaborative modeling. To address these issues, we propose FedHydroDSW, a novel personalized federated learning framework for runoff forecasting in data-scarce watersheds. Our approach enables decentralized model training across multiple organizations without requiring direct data sharing, thus preserving data privacy. FedHydroDSW incorporates unique model similarity metrics and parameter adaptation methods, utilizing insights from data-rich areas to refine forecasts in data-scarce regions. Experiments on the CAMELS dataset show that FedHydroDSW significantly outperforms standalone and conventional federated learning methods, with an average 30.1% increase in NSE, and reductions of 28% in RAE and 26.6% in RMSE across multiple data-scarce basins. By enabling secure cross-silo learning, our FedHydroDSW strategy paves the way toward more equitable runoff forecasting globally. The customized training and pattern recognition techniques represent major progress in deploying federated intelligence for impactful hydrological predictions. Our code is publicly available at https://***/xyjie37/FedHydroDSW. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Clause-level Relationship-aware Math Word Problems Solver

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Machine Intelligence Research 2022年第5期19卷 425-438页

作者： Chang-Yang Wu Xin Lin Zhen-Ya Huang Yu Yin Jia-Yu Liu Qi Liu Gang Zhou Anhui Province Key Laboratory of Big Data Analysis and Application School of Data ScienceUniversity of Science and Technology of ChinaHefei 230026China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China Laboratory of Mathematical Engineering and Advanced Computing Information Engineering UniversityZhengzhou 450001China

Automatically solving math word problems,which involves comprehension,cognition,and reasoning,is a crucial issue in artificial intelligence *** math word problem solvers mainly work on word-level relationship extraction and the generation of expression solutions while lacking consideration of the clause-level *** this end,inspired by the theory of two levels of process in comprehension,we propose a novel clause-level relationship-aware math solver(CLRSolver)to mimic the process of human comprehension from lower level to higher ***,in the lower-level processes,we split problems into clauses according to their natural division and learn their *** the higher-level processes,following human′s multi-view understanding of clause-level relationships,we first apply a CNN-based module to learn the dependency relationships between clauses from word relevance in a local ***,we propose two novel relationship-aware mechanisms to learn dependency relationships from the clause semantics in a global ***,we enhance the representation of clauses based on the learned clause-level dependency *** expression generation,we develop a tree-based decoder to generate the mathematical *** conduct extensive experiments on two datasets,where the results demonstrate the superiority of our framework.

关键词： Artificial intelligence(AI) artificial neural network(ANN) computational mathematics machine intelligence machine learning

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Surface-enhanced Raman scattering on nanomaterials for cancer and pathogens diagnosis

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Nano Research 2025年第2期18卷 650-674页

作者： Jingjing Wu Baohong Zhang Lei Xu Yanyu Zhao Tao Chen Jie Lin Anran Li School of Engineering Medicine Beihang UniversityBeijing 100191China Key Laboratory of Big Data-Based Precision Medicine Ministry of Industry and Information Technology of the People’s Republic of ChinaBeihang UniversityBeijing 100191China School of Optics and Photonics Beijing Institute of TechnologyBeijing 100089China Shanxi Lu'an Taihang Lubricant Technology Co. Ltd.Changzhi 046204China Cixi Institute of Biomedical Engineering Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo 315201China

Surface-enhanced Raman scattering(SERS)is a non-invasive spectroscopic technique that provides specific chemical fingerprint information for biomarkers in cancer and pathogen ***,the SERS strategies are limited by the non-specific interactions between substrates and co-existing substances in biological matrices and the challenges of obtaining molecular fingerprint information from the complex vibrational *** recent years,the rapid development of novel substrates with high SERS activity has opened up new opportunities for their applications in cancer and pathogen *** aim of this review is to present the recent progress and perspectives of novel SERS-based substrates for cancer and pathogen diagnostic ***,we will introduce recently developed SERS-active nanomaterials and discuss the influencing factors of the SERS ***,the advantages of SERS in the diagnosis of cancer and pathogens will be ***,we will review the latest breakthroughs in cancer and pathogen detection research with SERS technology,as well as the new opportunities for SERS applications brought about by artificial intelligence(Al)*** addition,the novel microfluidic-SERS platforms for cancer and pathogens diagnosis will also be ***,we will summarize the challenges and future perspectives of SERS technology in the field of early cancer diagnosis and rapid pathogen *** is highly expected that this review could benefit a comprehensive understanding of the research status of the SERS-active nanomaterials and arouse the research enthusiasm for them,leading to accelerated clinical translation of SERS technology in cancer and pathogen diagnosis.

关键词： surface-enhanced Raman scattering(SERS) SERS-active nanomaterials cancer and pathogen diagnosis artificial intelligence(Al)technology

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Solidification modes and delta-ferrite of two types of 316L stainless steels:a combination of as-cast microstructure and HT-CLSM research

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Journal of Iron and Steel Research International 2025年第2期32卷 426-436页

作者： Yang Wang Chao Chen Xiao-yu Yang Zheng-rui Zhang Jian Wang Zhou Li Lei Chen Wang-zhong Mu College of Materials Science and Engineering Taiyuan University of TechnologyTaiyuan 030024ShanxiChina Department of Materials Science and Engineering KTH Royal Institute of TechnologyStockholm 10044Sweden College of Artificial Intelligence and Big Data for Medical Sciences Shandong First Medical University and Shandong Academy of Medical SciencesJinan 250117ShandongChina College of Mechanical Engineering Taiyuan University of TechnologyTaiyuan 030024ShanxiChina Key Laboratory of Electromagnetic Processing of Materials(Ministry of Education) Northeastern UniversityShenyang 110819LiaoningChina

In 316L austenitic stainless steel,the presence of ferrite phase severely affects the non-magnetic properties.316L austenitic stainless steel with low-alloy type(L-316L)and high-alloy type(H-316L)has been *** microstructure and solidification kinetics of the two as-cast grades were in situ observed by high temperature confocal laser scanning microscopy(HT-CLSM).There are significant differences in the as-cast microstructures of the two 316L stainless steel *** L-316L steel,ferrite morphology appears as the short rods with a ferrite content of 6.98%,forming a dual-phase microstructure consisting of austenite and ***,in H-316L steel,the ferrite appears as discontinuous network structures with a content of 4.41%,forming a microstructure composed of austenite and sigma(σ)*** alloying elements in H-316L steel exhibit a complex distribution,with Ni and Mo enriching at the austenite grain ***-CLSM experiments provide the real-time observation of the solidification processes of both 316L specimens and reveal distinct solidification modes:L-316L steel solidifies in an FA mode,whereas H-316L steel solidifies in an AF *** differences result in ferrite and austenite predominantly serving as the nucleation and growth phases,*** solidification mode observed by experiments is similar to the thermodynamic calculation *** L-316L steel solidified in the FA mode and showed minimal element segregation,which lead to a direct transformation of ferrite to austenite phase(δ→γ)during phase transformation after ***,the H-316L steel solidified in the AF mode and showed severe element segregation,which lead to Mo enrichment at grain boundaries and transformation of ferrite into sigma and austenite phases through the eutectoid reaction(δ→σ+γ).

关键词： 316L austenitic stainless steel As-cast microstructure High-temperature confocal laser scanning microscopy Solidification mode Ferrite Characterization

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A coordinated bus bridging and metro short turning model in response to urban metro disruptions

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Transportation Safety and Environment 2022年第1期4卷 85-94页

作者： Zijian Wu Xiaolong Zhao Longwen Xiao Pu Wang Rail Data Research and Application Key Laboratory of Hunan Province School of Traffic and Transportation EngineeringCentral South UniversityChangsha 410075China

When an urban metro disruption occurs,the urban metro usually operates in a short turning mode and bus bridging is the typical way to relink the disconnected *** emergency response models dealing with urban metro disruptions,minimizing passenger delay in the bus bridging process and the metro short turning process is usually the optimization *** this study,we simultaneously consider the passenger delay in the bus bridging process and the metro short turning process to develop a coordinated emergency response model dealing with urban metro *** proposed coordinated model is validated in an actual urban metro line using actual passenger boarding demand *** insights in response to urban metro disruptions are obtained.

关键词： bus bridging metro short turning coordinated model urban metro disruption

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An Improved Multi-objective Particle Swarm Optimization Algorithm with Reduced Initial Search Space 21st

An Improved Multi-objective Particle Swarm Optimization Algo...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Zhiguang, Chu Yingchen, Fan Xiaolei, Zhang Ruyan, Zhang Xing, Zhang School of Electronics and Information Engineering Liaoning University of Technology Jinzhou121000 China Key Laboratory of Security for Network and Data in Industrial Internet of Liaoning Province Jinzhou121000 China

ISBN: (纸本)9789819601240

As data dimensions continue to increase, the challenges of data storage become more severe. While feature selection can reduce the complexity of high-dimensional data, existing methods are prone to local optima and high computational costs. To address these issues, this paper proposes a hybrid feature selection method based on MOPSO (Multi-Objective Particle Swarm Optimization). This method first uses a filter approach to narrow the search space, then employs a particle swarm algorithm to generate particles, incorporating mutation, crossover, and selection strategies during particle updates to enhance their search capabilities. Additionally, an archive set is established, utilizing grid-based and density-based methods during the update process to avoid local optima. Finally, the proposed method is compared with two single-objective optimization methods and two multi-objective optimization methods on multiple datasets. Experimental results demonstrate that the proposed method significantly reduces classification error rates and effectively reduces the number of features, providing an efficient solution for feature selection in high-dimensional data and addressing the challenges in high-dimensional data analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Particle swarm optimization (PSO)

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Joint-Motion Mutual Learning for Pose Estimation in Video 24

Joint-Motion Mutual Learning for Pose Estimation in Video

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wu, Sifan Chen, Haipeng Yin, Yifang Hu, Sihao Feng, Runyang Jiao, Yingying Yang, Ziqi Liu, Zhenguang College of Computer Science and Technology Jilin University Changchun China A STAR Singapore Singapore Georgia Institute of Technology AtlantaGA United States School of Artificial Intelligence Jilin University Changchun China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China Institute of Blockchain and Data Security Hangzhou China

ISBN: (纸本)9798400706868

Human pose estimation in videos has long been a compelling yet challenging task within the realm of computer vision. Nevertheless, this task remains difficult because of the complex video scenes, such as video defocus and self-occlusion. Recent methods strive to integrate multi-frame visual features generated by a backbone network for pose estimation. However, they often ignore the useful joint information encoded in the initial heatmap, which is a by-product of the backbone generation. Comparatively, methods that attempt to refine the initial heatmap fail to consider any spatio-temporal motion features. As a result, the performance of existing methods for pose estimation falls short due to the lack of ability to leverage both local joint (heatmap) information and global motion (feature) dynamics. To address this problem, we propose a novel joint-motion mutual learning framework for pose estimation, which effectively concentrates on both local joint dependency and global pixel-level motion dynamics. Specifically, we introduce a context-aware joint learner that adaptively leverages initial heatmaps and motion flow to retrieve robust local joint feature. Given that local joint feature and global motion flow are complementary, we further propose a progressive joint-motion mutual learning that synergistically exchanges information and interactively learns between joint feature and motion flow to improve the capability of the model. More importantly, to capture more diverse joint and motion cues, we theoretically analyze and propose an information orthogonality objective to avoid learning redundant information from multi-cues. Empirical experiments show our method outperforms prior arts on three challenging benchmarks. © 2024 ACM.

关键词： Motion estimation

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Does time matter? Temporal Dynamics and Configurational Hierarchy in Pedestrian Movement 14

Does time matter? Temporal Dynamics and Configurational Hier...

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14th International Space Syntax Symposium, SSS 2024

作者： Mara, Federico Altafini, Diego Hacar, Özge Öztürk Hacar, Müslüm Gülgen, Fatih Cutini, Valerio Department of Energy Systems Territory and Construction Engineering University of Pisa Italy Welsh School of Architecture Cardiff University United Kingdom Department of Geomatic Engineering Yildiz Technical University Turkey Knowledge Discovery and Data Mining Laboratory ISTI-CNR Italy Department of Energy Systems Territory and Construction Engineering University of Pisa Italy

ISBN: (纸本)9791256690329

The Space Syntax theoretical framework sustains that the urban spaces’ configuration possesses intrinsic information which allows to model and foresee patterns of human movement and social interaction. Critics argue, however, that there are several factors beyond configuration that influence pedestrian behaviour. In particular, critics focus on the absence of the temporal component by saying that, while Space Syntax can reveal systemic logics, it cannot depict the actual time-dependent movement variation in micro-urban scales and their causes. Nevertheless, if accounted pedestrian flows maintain their hierarchies over time and those coincide with the street hierarchies denoted by Space Syntax, this would reinforce the role of the configurational component in movement. Given this premise, the paper discusses pedestrian flow data and Visibility Graph Analysis in Piazza delle Vettovaglie, Pisa (Italy), to assess: a) if pedestrian flows hierarchy actually changes, by comparing people counts registered in different timestamps, along three days, at different access gates;b) if different Space Syntax metrics express the same gate hierarchy;c) if-and-how the modelled and real hierarchies differ from each other. This discussion aims to unveil the role of time in micro-urban spaces usage, and provide a first answer to the question: do changes in pedestrian movement across time follow the configurational hierarchy established with Space Syntax? Results inform that despite changing in flows, the hierarchy proved rather regular across time, thus confirming the Space Syntax premises and the role of configurational component in movement. © 14th International Space Syntax Symposium, SSS 2024.

关键词： Syntactics

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Analysis of Factors Influencing Driver Lane-Changing Intentions Based on a Naturalistic Trajectory data Set on Highways 24

Analysis of Factors Influencing Driver Lane-Changing Intenti...

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24th COTA International Conference of Transportation Professionals: Resilient, Intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Wang, Guangchen Lu, Guangquan Wang, Jinghua Liu, Miaomiao School of Transportation Science and Engineering Beihang Univ. Beijing China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang Univ. Beijing China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

Accurately analyzing and predicting driver lane-changing intentions is of paramount importance, as it significantly enhances the safety of self-driving vehicles in their decision-making processes, holding great promise for the future of intelligent transportation systems. This study extracts fundamental lane-changing characteristic parameters from the Next Generation Simulation (NGSIM) trajectory data set and employs Structural Equation Modeling (SEM) to construct a model that elucidates the factors influencing drivers’ lane-changing intentions. Furthermore, it provides insight into how lane-changing intentions are influenced by lane-changing behavior and explores the intricate connection between intention and associated risks. The results emphasize the substantial negative impact of the longitudinal speed difference between the self-vehicle and the preceding vehicle on lane-changing intentions. Additionally, the consideration of lane-changing risk plays a pivotal role in drivers’ decisions to engage in lane-changing behavior. These findings have significant implications for a deeper comprehension of lane-changing intentions, guiding lane-changing decisions, and enhancing overall traffic safety. © ASCE.

关键词： Autonomous vehicles

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