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Listen with Seeing: Cross-modal Contrastive Learning for Audio-Visual Event Localization

IEEE Transactions on Multimedia 2025年 27卷 2650-2665页

作者： Sun, Chao Chen, Min Zhu, Chuanbo Zhang, Sheng Lu, Ping Chen, Jincai Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China School of Computer Science and Technology Wuhan430074 China South China University of Technology School of Computer Science and Engineering Guangzhou510640 China Pazhou Laboratory Guangzhou510640 China

In real-world physiological and psychological scenarios, there often exists a robust complementary correlation between audio and visual signals. Audio-Visual Event Localization (AVEL) aims to identify segments with Audio-Visual Events (AVEs) that contain both audio and visual tracks in unconstrained videos. Prior studies have predominantly focused on audio-visual cross-modal fusion methods, overlooking the fine-grained exploration of the cross-modal information fusion mechanism. Moreover, due to the inherent heterogeneity of multi-modal data, inevitable new noise is introduced during the audio-visual fusion process. To address these challenges, we propose a novel Cross-modal Contrastive Learning Network (CCLN) for AVEL, comprising a backbone network and a branch network. In the backbone network, drawing inspiration from physiological theories of sensory integration, we elucidate the process of audio-visual information fusion, interaction, and integration from an information-flow perspective. Notably, the Self-constrained Bi-modal Interaction (SBI) module is a bi-modal attention structure integrated with audio-visual fusion information, and through gated processing of the audio-visual correlation matrix, it effectively captures inter-modal correlation. The Foreground Event Enhancement (FEE) module emphasizes the significance of event-level boundaries by elongating the distance between scene events during training through adaptive weights. Furthermore, we introduce weak video-level labels to constrain the cross-modal semantic alignment of audio-visual events and design a weakly supervised cross-modal contrastive learning loss (WCCL Loss) function, which enhances the quality of fusion representation in the dual-branch contrastive learning framework. Extensive experiments conducted on the AVE dataset for both fully supervised and weakly supervised event localization, as well as Cross-Modal Localization (CML) tasks, demonstrate the superior performance of our model compa

关键词： information fusion

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AdaptHM: A Fully Adaptive Data Migration Strategy for Hybrid Memory systems

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and systems 2025年第3期44卷 923-936页

作者： Peng, Zhouxuan Feng, Dan Chen, Jianxi Hu, Jing Liu, Yachun Hu, Jinlei Zhang, Jintong Wan, Tianyu Chen, Zuoning Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology MoE of China Wuhan430074 China Chinese Academy of Engineering Beijing100088 China

Data migration strategies (DMSs) improve the overall performance of hybrid memory systems by migrating frequently accessed (hot) data to faster memory. However, designing an efficient DMS is challenging since the key metrics of DMS - hot data selection, migration granularity, and migration frequency - are sensitive to access patterns of workloads. Most existing strategies focus on only one of these metrics and often overlook the crucial impact of access patterns, resulting in suboptimal performance and unnecessary migration traffic. In this article, we propose AdaptHM, a fully access-pattern-aware Adaptive DMS for Hybrid Memory systems. AdaptHM achieves adaptability on all three metrics through its unique multilevel data framework. First, AdaptHM adopts a group-level competition policy to select hot blocks, which responds faster to access patterns than threshold-based policies. Second, AdaptHM enables segment-level dynamic migration granularity by decoupling migration from remapping, which shows better-access pattern resilience than existing schemes with fixed-size global migration granularity. Third, AdaptHM adjusts the migration frequency at set-level by periodically assessing the migration benefit, avoiding unnecessary migrations. Experimental results demonstrate that AdaptHM improves the performance by an average of 12.78% and reduces energy consumption by up to 37.24% compared to the state-of-the-art scheme. © 1982-2012 IEEE.

关键词： Frequency modulation

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Joint Task Offloading and Resource Allocation Strategy for Space-Air-Ground Integrated Vehicular Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1027-1043页

作者： Yuanshuo Gang Yuexia Zhang Zhihai Zhuo School of Information and Communication Engineering Beijing Information Science and Technology UniversityBeijing 100101China Key Laboratory of Modern Measurement and Control Technology Ministry of EducationBeijing Information Science and Technology UniversityBeijing 100101China

Space-Air-Ground integrated Vehicular Network(SAGVN)aims to achieve ubiquitous connectivity and provide abundant computational resources to enhance the performance and efficiency of the vehicular ***,there are still challenges to overcome,including the scheduling of multilayered computational resources and the scarcity of spectrum *** address these problems,we propose a joint Task Offloading(TO)and Resource Allocation(RA)strategy in SAGVN(namely JTRSS).This strategy establishes an SAGVN model that incorporates air and space networks to expand the options for vehicular TO,and enhances the edge-computing resources of the system by deploying edge *** minimize the system average cost,we use the JTRSS algorithm to decompose the original problem into a number of subproblems.A maximum rate matching algorithm is used to address the channel allocation and the Lagrangian multiplier method is employed for computational *** acquire the optimal TO decision,a differential fusion cuckoo search algorithm is *** simulation results demonstrate the significant superiority of the JTRSS algorithm in optimizing the system average cost.

关键词： edge computing Resource Allocation(RA) Space-Air-Ground integrated Vehicular Network(SAGVN) Task Offloading(TO)

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Deep Reinforcement Learning-based Multi-Objective Scheduling for Distributed Heterogeneous Hybrid Flow Shops with Blocking Constraints

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engineering 2025年第3期46卷 278-291页

作者： Xueyan Sun Weiming Shen Jiaxin Fan Birgit Vogel-Heuser Fandi Bi Chunjiang Zhang State Key Laboratory of Intelligent Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China Institute of Automation and Information Systems Technical University of MunichGarching 85748Germany

This paper investigates a distributed heterogeneous hybrid blocking flow-shop scheduling problem(DHHBFSP)designed to minimize the total tardiness and total energy consumption simultaneously,and proposes an improved proximal policy optimization(IPPO)method to make real-time decisions for the DHHBFSP.A multi-objective Markov decision process is modeled for the DHHBFSP,where the reward function is represented by a vector with dynamic weights instead of the common objectiverelated scalar value.A factory agent(FA)is formulated for each factory to select unscheduled jobs and is trained by the proposed IPPO to improve the decision *** FAs work asynchronously to allocate jobs that arrive randomly at the shop.A two-stage training strategy is introduced in the IPPO,which learns from both single-and dual-policy data for better data *** proposed IPPO is tested on randomly generated instances and compared with variants of the basic proximal policy optimization(PPO),dispatch rules,multi-objective metaheuristics,and multi-agent reinforcement learning *** experimental results suggest that the proposed strategies offer significant improvements to the basic PPO,and the proposed IPPO outperforms the state-of-the-art scheduling methods in both convergence and solution quality.

关键词： Multi-objective Markov decision process Multi-agent deep reinforcement learning Proximal policy optimization Distributed hybrid flow-shop scheduling Blocking constraints

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IPFA-Net: Important Points Feature Aggregating Net for Point Cloud Classification and Segmentation

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Chinese Journal of Electronics 2025年第1期34卷 322-337页

作者： Jingya Wang Yu Zhang Bin Zhang Jinxiang Xia Weidong Wang School of Information and Software Engineering University of Electronic Science and Technology of China National Key Laboratory of Automotive Chassis Integration and Bionics Jilin University

This paper focuses on the problems of point cloud deep neural networks in classification and segmentation tasks, including losing important information during down-sampling, ignoring relationships among points when extracting features, and network performance being susceptible to the sparsity of point cloud. To begin with, this paper proposes a farthest point sampling-important points sampling method for down-sampling, which can preserve important information of point clouds and maintain the geometry of input data. Then, the local feature relation aggregating method is proposed for feature extraction, improving the network's ability to learn contextual information and extract rich local region features. Based on these methods, the important points feature aggregating net(IPFA-Net) is designed for point cloud classification and segmentation tasks. Furthermore, this paper proposes the multi-scale multi-density feature connecting method to reduce the negative impact of point cloud data sparsity on network performance. Finally, the effectiveness of IPFA-Net is demonstrated through experiments on ModelNet40, ShapeNet part, and ScanNet v2 datasets. IPFA-Net is robust to reducing the number of point clouds, with only a 3.3% decrease in accuracy under a 16-fold reduction of point number. In the part segmentation experiments, our method achieves the best segmentation performance for five objects.

关键词： Point cloud compression Representation learning Geometry Fuses Semantics Artificial neural networks Feature extraction Sampling methods Robustness Data mining

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Modulation of inactive Li2O via iodinated MOF nanocapsules interfacial transformation engineering for high-performance solid electrolyte interphase

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Journal of Energy Chemistry 2025年第5期104卷 482-493页

作者： Xingxing Zhang Qingmei Su Gaohui Du Bingshe Xu Xuehan Hou Xiaowei Yang Weihao Shi Zhuo Chen Yang Shi Yujie Lv Wenhuan Huang School of Materials Science & Engineering Shaanxi University of Science and Technology Key Laboratory of Chemical Additives for China National Light Industry College of Chemistry and Chemical Engineering Shaanxi University of Science and Technology Materials Institute of Atomic and Molecular Science School of Physics & Information Science Shaanxi University of Science and Technology School of Chemistry and Chemical Engineering Shanghai Jiaotong University

Lithium （Li） deposition and nucleation at solid electrolyte interphase （SEI） is the main origin for the capacity decay in Li metal batteries （LMBs）.SEI conversion with enhanced electrochemical and mechanical properties is an effective approach to achieve uniform nucleation of Li+and stabilize the lithium metal ***,complex interfacial reaction mechanisms and interface compatibility issues hinder the development of SEI conversion strategies for stabilizing lithium metal ***,we presented the release of I3-in–NH2-modified metal–organic frameworks for a Li metal surface SEI phase conversion ***–NH2group in MOF pores induced the formation of I3-from I2,which was further spontaneously reacted with inactive Li2O transforming into high-performance LiI and ***,theoretical calculation provided deeply insight into the unique reconstructed interfacial formation and electrochemical mechanism of rich LiI and *** a result,the Li+deposition and nucleation were improved,facilitating the transport kinetics of Li+and inhibiting the growth of lithium *** assembled solid-state Li||LiFePO4full cells exhibited superior long-term stability of 800 cycles and high Coulombic efficiency （>99%）,Li||LiNi0.8Co0.1Mn0.1O2pouch cell also displayed superior practical performance over 200 cycles at 2 C,high loading of 5 mg cm-2and safety *** innovative SEI design strategy promotes the development of high-performance solid-state Li metal batteries.

关键词： Li metal battery MOF Nano-capsule SEI phase conversion Solid electrolyte interphase (SEI) Solid-state electrolytes

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Event-Triggered Robust Parallel Optimal Consensus Control for Multiagent systems

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 40-53页

作者： Qinglai Wei Shanshan Jiao Qi Dong Fei-Yue Wang IEEE the Institute of Systems Engineering Macau University of Science and Technology the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences the School of Artificial Intelligence University of Chinese Academy of Sciences China Academy of Electronics and Information Technology the State Key Laboratory of Multimodal Artificial Intelligence Systems the School of Artificial Intelligence University of Chinese Academy of Sciences

This paper highlights the utilization of parallel control and adaptive dynamic programming(ADP) for event-triggered robust parallel optimal consensus control(ETRPOC) of uncertain nonlinear continuous-time multiagent systems(MASs).First, the parallel control system, which consists of a virtual control variable and a specific auxiliary variable obtained from the coupled Hamiltonian, allows general systems to be transformed into affine systems. Of interest is the fact that the parallel control technique's introduction provides an unprecedented perspective on eliminating the negative effects of disturbance. Then, an eventtriggered mechanism is adopted to save communication resources while ensuring the system's stability. The coupled HamiltonJacobi(HJ) equation's solution is approximated using a critic neural network(NN), whose weights are updated in response to events. Furthermore, theoretical analysis reveals that the weight estimation error is uniformly ultimately bounded(UUB). Finally,numerical simulations demonstrate the effectiveness of the developed ETRPOC method.

关键词： Adaptive dynamic programming (ADP) critic neural network (NN) event-triggered control optimal consensus control robust control

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EPAFusion: A Novel Fusion Network Based on Enhancement and Progressive Aware for Infrared-Visible Images in Low-Light

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IEEE Sensors Journal 2025年第9期25卷 15378-15390页

作者： Qi, Jianhuan Ni, Bo Yu, Qida Ni, Haibin Zhou, Xiaoyan Chang, Jianhua Nanjing University of Information Science and Technology School of Electronics and Information Engineering Nanjing210044 China Nanjing University of Information Science and Technology Jiangsu Provincial Key Laboratory of Meteorological Observation and Information Processing Nanjing210044 China

Most infrared and visible image fusion algorithms demonstrate satisfactory performance under normal lighting conditions, but often perform poorly in low-light environments because the texture details in visible images are barely visible in the dark. To resolve this issue, we have proposed a novel fusion network based on enhancement and progressive aware for infrared-visible images in low-light (EPAFusion). The EPAFusion network efficiently brightens low-light environments while fusing complementary information from the two modalities. Specifically, we design a degraded illumination disentangled network (DIDNet), which is specially used to eliminate the degraded illumination effect in the visible image and enhance the feature information of the source image. Then, a progressive complementary-aware fusion network (PCAFNet), which is used to generate fused images with rich texture details and high contrast by integrating the complementary information of the dual modal through a progressive approach. Extensive comparative experiments have demonstrated that EPAFusion exhibits superior to state-of-the-art fusion algorithms in terms of subjective and objective evaluation metrics. Particularly, low-light enhancement and dual-modal-aware fusion provide more effective information to the fused image. © 2001-2012 IEEE.

关键词： Image fusion

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Cat Swarm Algorithm Generated Based on Genetic Programming Framework Applied in Digital Watermarking

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Computers, Materials & Continua 2025年第5期83卷 3135-3163页

作者： Shu-Chuan Chu Libin Fu Jeng-Shyang Pan Xingsi Xue Min Liu School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210000China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266000China Department of Information Management Chaoyang University of TechnologyTaichung40601TaiwanChina School of Information Science and Engineering Fujian University of TechnologyFuzhou350000China Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resource Nanjing University of Information Science and TechnologyNanjing210000China

Evolutionary algorithms have been extensively utilized in practical ***,manually designed population updating formulas are inherently prone to the subjective influence of the *** programming(GP),characterized by its tree-based solution structure,is a widely adopted technique for optimizing the structure of mathematical models tailored to real-world *** paper introduces a GP-based framework(GPEAs)for the autonomous generation of update formulas,aiming to reduce human *** modifications to tree-based GP have been instigated,encompassing adjustments to its initialization process and fundamental update operations such as crossover and mutation within the *** designing suitable function sets and terminal sets tailored to the selected evolutionary algorithm,and ultimately derive an improved update *** Cat Swarm Optimization Algorithm(CSO)is chosen as a case study,and the GP-EAs is employed to regenerate the speed update formulas of the *** validate the feasibility of the GP-EAs,the comprehensive performance of the enhanced algorithm(GP-CSO)was evaluated on the CEC2017 benchmark ***,GP-CSO is applied to deduce suitable embedding factors,thereby improving the robustness of the digital watermarking *** experimental results indicate that the update formulas generated through training with GP-EAs possess excellent performance scalability and practical application proficiency.

关键词： Cat swarm algorithm genetic programming digital watermarking update mode mode generation framework

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Quantum public-key crypto via EPR pairs

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science China(Physics,Mechanics & Astronomy) 2025年第1期68卷 54-59页

作者： Xiang-Qun Fu Hong-Wei Li Jian-Hong Shi Tan Li Wan-Su Bao Henan Key Laboratory of Quantum Information and Cryptography Information Engineering UniversityZhengzhou 450004China Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of ChinaHefei 230026China

With the development of quantum computing technology,quantum public-key cryptography is gradually becoming an alternative to the existing classical public-key *** paper designs a quantum trapdoor one-way function via EPR pairs and quantum *** on this,a new quantum public-key cryptosystem is presented,which offers forward security,and can resist the chosen-plaintext attack and chosen-ciphertext *** with the existing quantum public-key cryptos,eavesdropping can be automatically detected in this new quantum public-key cryptosystem under a necessary condition,which is also detailed in the paper.

关键词： quantum public-key crypto trapdoor one-way function EPR pairs

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