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Convolutional Attention Fusion for RGBT Tracking

IEEE Signal Processing Letters 2025年 32卷 2149-2153页

作者： Zhang, Dawei Wang, Xuan Xiao, Xin Chen, Hao Zheng, Zhonglong Zhejiang Normal University School of Computer Science and Technology Jinhua321004 China Zhejiang Normal University Zhejiang Key Laboratory of Intelligent Education Technology and Application Jinhua321004 China Big Data AI Research Institute of China Telecom Corporation Limited Jinhua Branch China

RGBT target tracking accomplishes the tracking task by fusing visible and thermal infrared information. The development of Convolutional Neural Networks (CNNs) and Transformer has greatly advanced this field. Most existing transformer-based trackers focus on global modeling while neglecting the utilization of local information. In this paper, we propose a novel Convolutional Attention Fusion Module (CAFM) for RGBT target tracking. To be specific, this module continuously slides local windows on the image like convolution, and captures context features in each window like attention. Additionally, local position embedding is added to the window and works in conjunction with global position embedding to enhance the model's understanding of spatial information. Therefore, our CAFM enhances the extraction of local features by restricting the attention area and promotes multimodal fusion through cross-attention. We extend OSTrack to RGBT tracking and integrate the proposed CAFM into it. Experimental results show that our method performs well on the LasHeR, RGBT210, and RGBT234 datasets, and is superior to other advanced trackers. © 1994-2012 IEEE.

关键词： Aircraft detection

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Towards practical data alignment in production federated learning

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Frontiers of Computer science 2025年第1期19卷 167-169页

作者： Yexuan SHI Wei YU Yuanyuan ZHANG Chunbo XUE Yuxiang ZENG Zimu ZHOU Manxue GUO Lun XIN Wenjing NIE State Key Laboratory of Complex&Critical Software Environment and Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang UniversityBeijing 100191China Zhongguancun Pan Connected Mobile Communication Technology Innovation and Application Research Institute Beijing 100088China China Mobile Research Institute Beijing 100053China School of Data Science City University of Hong KongHong Kong 999077China

1 Introduction Federated learning has emerged as a promising par-adigm for collaborative model training that facilitates cooperation among multiple parties while ensuring data privacy[1].Successful alignment of data across parties is crucial for effective federated learning[2].This alignment involves harmonizing heterogeneous data from different parties to identify shared data for joint model *** set intersection(PSI)is a technique that allows the alignment of common entities between parties without revealing additional ***,efficiently performing data alignment with PSI in federated learning[3],especially when dealing with highly unbalanced data,remains challenging due to the low efficiency.

关键词： alignment dealing parties

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application of Copula in S&P 500 Index Option Pricing with non-Gaussian Stochastic Volatility Models

Application of Copula in S&P 500 Index Option Pricing with n...

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International Symposium on Computational and Business Intelligence (ISCBI)

作者： Yongbo Sun Guangdong Provincial/Zhuhai Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Zhuhai China

ISBN: (数字)9798331533786

ISBN: (纸本)9798331533793

In this paper, a newly derived quasi closed-form dynamic European option pricing approach using copula methods is proposed, extending the traditional Gaussian stochastic volatility models to non-Gaussian stochastic volatility models (with fat tails). This approach can save more computing costs compared to simulation techniques (e.g., Monte Carlo method). By integrating non-Gaussian stochastic volatility models with copula methods, we can more accurately model the underlying dependence structures between asset price and volatility. It is quite crucial for option pricing in real financial markets. Specifically, we apply the Heston model under CIR process and a log-volatility model under the Ornstein-Uhlenbeck process to construct the stochastic volatility models, which are also among the best-known stochastic volatility models in the field of stochastic processes and mathematical finance. Furthermore, we conduct empirical analysis based on S&P500 Index prices and the related European-style call option price to test the accuracy of our method and evaluate its robustness and efficiency using appropriate benchmarks.

关键词： Monte Carlo methods Computational modeling Stochastic processes Europe Finance Pricing Mathematical models Robustness Fats Indexes

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Edge-Driven Industrial Computing Power Networks: Digital Twin-Empowered Service Provisioning by Hybrid Soft Actor-Critic

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8095-8109页

作者： Zhang, Long Song, Deng-Ao Zhang, Hongliang Tian, Ni Zhuang, Zirui Niyato, Dusit Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

With the proliferation of data-intensive industrial applications, the collaboration of computing powers among standalone edge servers is vital to provision such services for smart devices. In this paper, we propose an edge-driven industrial computing power network (CPN) by orchestrating the computing and network resources of edge servers through the centralized resource scheduling and decentralized task computing. However, efficient task offloading and collaborative processing is challenging, which requires higher degrees of network automation and intelligence. Therefore, we incorporate digital twins (DTs) into the edge-driven CPN architecture, where the DTs are created as the digital replicas to assist both the computation offloading and collaborative processing. A joint optimization problem of the computing power assignment, service association, task partition, and transmit power control is formulated for maximizing the system average weighted utility. Due to the temporal-spatial variability of tasks and the resulted dynamic environment, we transform the original problem as a Markov decision process aiming at maximizing the long-term average weighted utility. To efficiently handle the high-dimensional discrete-continuous action space, a hybrid soft actor-critic based deep reinforcement learning algorithm is developed for optimizing the joint design. Simulation results validate the superiority of our proposed algorithm over the benchmarks, showing the significant gains obtained by integrating DTs into the edge-driven CPN. © 1967-2012 IEEE.

关键词： Markov processes

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Knowledge Transfer Across Modalities for Weakly Supervised Point Cloud Semantic Segmentation

Knowledge Transfer Across Modalities for Weakly Supervised P...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zihan Wang Yunhang Shen Mengtian Li Ke Li Xing Sun Shaohui Lin Lizhuang Ma East China Normal University Tencent Youtu Lab Shanghai University Key Laboratory of Advanced Theory and Application in Statistics and Data Science

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current weakly supervised point cloud semantic segmentation struggles with insufficient utilization of limited annotations in unimodal representation learning due to the sparse and textureless nature of point clouds. In this work, we leverage cross-modality information by transferring knowledge from image and text sources to the point cloud network. The intuition is that images contribute rich texture, color, and discriminative information, complementing point clouds to boost semantic segmentation performance. To reduce extensive computational resources for cross-modality fusion, we introduce the Multi-Scale Deformable Knowledge Transfer, an innovative training scheme that optimizes and extends the one-to-one mapping to flexible one-to-many relations between multi-modal data. Furthermore, we employ pre-trained image-text models to generate pseudo labels for point clouds and construct positive and negative samples for semantic contrastive regularization, facilitating the full exploitation of unlabeled data. The experimental results evaluated on SemanticKITTI and nuScenes demonstrate substantial improvements, achieving an average gain of 3.8% over the previous weakly supervised methods, and comparable performances to fully supervised approaches.

关键词： Point cloud compression Training Representation learning Weak supervision Three-dimensional displays Semantic segmentation Semantics Signal processing Speech processing Knowledge transfer

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Mutable hierarchy feature selection based on generalized fuzzy rough sets

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Applied Soft Computing 2025年 177卷

作者： Lin, Zilong Lin, Yaojin Wang, Chenxi Chen, Jinkun School of Computer Science Minnan Normal University Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Minnan Normal University Zhangzhou Fujian363000 China Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry Wuyi University Wuyishan Fujian354300 China School of Mathematics and Statistics Minnan Normal University Zhangzhou Fujian363000 China

Hierarchical classification divides data into correlated sub-tasks from coarse to fine. Compared to flat classification, it is more complex and suffers from the curse of dimensionality. Existing hierarchical Fuzzy Rough Sets (FRS) methods only stay at the fine-grained to select the features, which is a fine-grained search strategy. Thus, we propose a coarse-grained search strategy and use Generalized Fuzzy Rough Sets (GFRS) to enhance its robustness. Furthermore, we introduce the concept of tree fragmentation. Though assessing the degree of fragmentation in the tree structure, it selects an appropriate granularity search strategy for hierarchical tree-structured datasets. Finally, we combine both granularity search strategies and collectively named - Mutable Hierarchy Search Strategy (MHSS);the entire proposed algorithm is named - Mutable Hierarchy Feature Selection Based on Generalized Fuzzy Rough Sets (MHFS). We compare two FRS-based feature selection algorithms, three hierarchical optimization methods, and six flat feature selection methods. Extensive experiments demonstrate the performance of our method. © 2025 Elsevier B.V.

关键词： Feature extraction

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Origin of the tiny energy gap and Dirac points in monoclinic trilayer nickelate La4Ni3O10

arXiv

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arXiv 2025年

作者： Zhang, Hu Hebei Research Center of The Basic Discipline for Computational Physics Hebei Key Laboratory of High-precision Computation and Application of Quantum Field Theory College of Physics Science and Technology Hebei University Baoding071002 China

Superconductivity was recently found in trilayer nickelate La4Ni3O10 under high pressure with a phase transition from the monoclinic P21/a structure to the tetragonal I4/mmm structure. Previous experimental works have confirmed the existence of a tiny energy gap formed with Ni 3dz2 orbitals in monoclinic La4Ni3O10. Here we investigate the physical origin of this gap by analyzing symmetry properties of energy bands based on the group theory. The tiny gap comes from energy bands with opposite parity at the Brillouin zone center. In addition, we also find previously unknown Dirac points in some momentum directions around the Fermi level. An effective Hamiltonian is constructed to describe low energy physics of the tiny energy gap and Dirac points. Due to the low crystal symmetry of monoclinic La4Ni3O10, its energy bands display strong anisotropic properties. © 2025, CC BY.

关键词： Hamiltonians

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Symmetric quantum joint measurements on multiple qubits

arXiv

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arXiv 2025年

作者： Ding, Dong He, Ying-Qiu Gao, Ting Yan, Feng-Li College of Science North China Institute of Science and Technology Beijing101601 China School of Mathematical Sciences Hebei Normal University Shijiazhuang050024 China College of Physics Hebei Key Laboratory of Photophysics Research and Application Hebei Normal University Shijiazhuang050024 China

We investigate the generalization of symmetric quantum joint measurements on multiple qubits. We first describe a method for constructing a symmetric joint measurement basis for three qubits by utilizing single-qubit states corresponding to the four vertices of a tetrahedron on the Bloch sphere. We demonstrate the expected tetrahedral symmetry of the current measurement basis and discuss its application in a trilocal star-shaped network. This architecture enables us to generalize the two-qubit symmetric joint measurement to an n-qubit version, preserving the tetrahedral or hexahedral symmetry. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

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ON STOCHASTIC CONTROL PROBLEMS WITH HIGHER-ORDER MOMENTS

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SIAM Journal on Control and Optimization 2025年第3期63卷 1560-1589页

作者： Wang, Yike Liu, Jingzhen Bensoussan, Alain Cedric Yiu, Ka-Fai Wei, Jiaqin School of Finance Chongqing Technology and Business University Chongqing China China Institute for Actuarial Science Central University of Finance and Economics Beijing China SNaveen Jindal School of Management University of Texas at Dallas DallasTX75080 United States Department of Applied Mathematics Hong Kong Polytechnic University Hong Kong Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University Shanghai China

In this paper, we focus on a class of time-inconsistent stochastic control problems, where the objective function includes the mean and several higher-order central moments of the terminal value of state. To tackle the time-inconsistency, we seek both the closed-loop and the open-loop Nash equilibrium controls as time-consistent solutions. We establish a partial differential equation (PDE) system for deriving a closed-loop Nash equilibrium control, which does not include the equilibrium value function and is different from the extended Hamilton-Jacobi-Bellman (HJB) equations as in Bj\" ork, Khapko, and Murgoci [Finance Stoch., 21 (2017), pp. 331-360]. We show that our PDE system is equivalent to the extended HJB equations that seem difficult to solve for our higher-order moment problems. In deriving an open-loop Nash equilibrium control, due to the nonseparable higher-order moments in the objective function, we make some moment estimates in addition to the standard perturbation argument for developing a maximum principle. Then, the problem is reduced to solving a flow of forward-backward stochastic differential equations. In particular, we investigate linear controlled dynamics and some objective functions affine in the mean. The closed-loop and the open-loop Nash equilibrium controls are identical, which are independent of the state value, the random path, and the preference on the odd-order central moments. By sending the highest order of central moments to infinity, we obtain the time-consistent solutions to some control problems whose objective functions include some penalty functions for deviation. © 2025 Society for Industrial and Applied Mathematics.

关键词： Stochastic control systems

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Attention-based unsupervised prompt learning for SAM in leaf disease segmentation

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Knowledge-Based Systems 2025年 320卷

作者： Tian, Luda Yuan, Yingchun En, Qing Ma, Wei Zhang, Guanghui Liang, Fangfang School of Information Science and Technology Hebei Agricultural University Baoding071001 China Hebei Key Laboratory of Agricultural Big Data Baoding071001 China Hebei Engineering Research Center for Agricultural Remote Sensing Application Baoding071001 China Lab School of Computer Science Carleton University OttawaK1S 5B6 Canada Faculty of Information Technology Beijing University of Technology Beijing100124 China

In modern agriculture, leaf disease segmentation is crucial for crop disease management and yield improvement. Since most deep learning-based segmentation models require extensive annotations, pursuing unsupervised methods becomes a practical solution. Although advanced models like the Segment Anything Model (SAM) can produce precise class-agnostic masks without annotations, their reliance on human-interactive prompts limits their utility to unsupervised disease segmentation tasks. To address this limitation, we present UPLS, a novel three-stage framework that leverages unsupervised prompt learning to enable SAM for leaf disease segmentation. This method employs unsupervised contrastive learning to progressively segment leaf and disease areas and automatically generate disease prompts for SAM. Concretely, for fine and intricate lesions, we utilize an improved high-frequency attention mechanism to extract high-frequency features from the leaf area, and construct contrastive losses between global priors and foreground/background features. Furthermore, we devise a strategy to automatically generate disease-related prompts from the segmented leaf and initial lesion regions, enabling SAM to refine disease boundaries without human intervention. Experiments on three public plant disease segmentation datasets show that UPLS outperforms existing non-fully supervised segmentation methods in accuracy and robustness. Source code is available at https://***/Tianluda/UPLS. © 2025 Elsevier B.V.

关键词： Unsupervised learning

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