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arXiv 2021年

作者： Gao, Guangwei Xu, Guoan Yu, Yi Xie, Jin Yang, Jian Yue, Dong Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210023 China Digital Content and Media Sciences Research Division National Institute of Informatics Tokyo101-8430 Japan Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China College of Automation and College of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing210023 China School of Computer Science and Technology Nanjing University of Science and Technology Nanjing210094 China

In recent years, how to strike a good trade-off between accuracy, inference speed, and model size has become the core issue for real-time semantic segmentation applications, which plays a vital role in real-world scenarios such as autonomous driving systems and drones. In this study, we devise a novel lightweight network using a multi-scale context fusion (MSCFNet) scheme, which explores an asymmetric encoderdecoder architecture to alleviate these problems. More specifically, the encoder adopts some developed efficient asymmetric residual (EAR) modules, which are composed of factorization depth-wise convolution and dilation convolution. Meanwhile, instead of complicated computation, simple deconvolution is applied in the decoder to further reduce the amount of parameters while still maintaining the high segmentation accuracy. Also, MSCFNet has branches with efficient attention modules from different stages of the network to well capture multiscale contextual information. Then we combine them before the final classification to enhance the expression of the features and improve the segmentation efficiency. Comprehensive experiments on challenging datasets have demonstrated that the proposed MSCFNet, which contains only 1.15M parameters, achieves 71.9% Mean IoU on the Cityscapes testing dataset and can run at over 50 FPS on a single Titan XP GPU configuration. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Make your choice for multimodal knowledge graph completion

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Knowledge-Based Systems 2025年 323卷

作者： Yifan Xue Shuoyan Ren Wanqiang Cai Yingyao Ma Lotfi Senhadji Huazhong Shu Jiasong Wu Laboratory of Image Science and Technology Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Nanjing 210096 China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast University Nanjing 210096 China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi 214122 China Univ Rennes INSERM LTSI - UMR 1099 F-35000 Rennes France Centre de Recherche en Information Biomédicale Sino-français (CRIBs) Univ Rennes INSERM F-35000 Rennes France

Knowledge graph completion (KGC) aims to predict missing entities in knowledge graphs by learning effective representations of entities and their relations. Recent advances have explored multimodal KGC by incorporating structural, textual, and visual information. However, two critical challenges remain unresolved: (1) modal heterogeneity, where significant differences in feature distributions across modalities hinder effective fusion; and (2) spatial heterogeneity, where embedding knowledge graphs in a single geometric space fails to capture their complex topological structures. To address these challenges, we propose ChoicE, a unified framework that leverages a mixture of experts (MoE) design for both encoding and decoding. In the encoder, the multimodal chooser preprocesses multiple modalities to derive embedding representations for each modality. These representations are then processed by distinct experts specialized for structural, textual, and visual features, facilitating effective fusion while preserving modality-specific information. In the decoder, the geometric chooser projects the unified multimodal embeddings into Euclidean, complex, or hyperbolic space, dynamically selecting the most appropriate space to model the inference patterns inherent to each query. Extensive experiments on multiple benchmark datasets demonstrate that ChoicE effectively overcomes these dilemmas and achieves state-of-the-art performance in multimodal KGC. The data and code are released at https://***/r/ChiocE-master/ .

关键词： Knowledge graph Knowledge graph completion Link prediction Multimodal Transformer

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Efficient spatialtemporal context modeling for action recognition?

arXiv

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arXiv 2021年

作者： Cao, Congqi Lu, Yue Zhang, Yifan Jiang, Dongmei Zhang, Yanning Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Lab on Speech and Image Information Processing School of Computer Science Northwestern Poly-technical University Xi'an710129 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing100190 China

Contextual information plays an important role in action recognition. Local operations have difficulty to model the relation between two elements with a long-distance interval. However, directly modeling the contextual information between any two points brings huge cost in computation and memory, especially for action recognition, where there is an additional temporal dimension. Inspired from 2D criss-cross attention used in segmentation task, we propose a recurrent 3D criss-cross attention (RCCA-3D) module to model the dense long-range spatiotemporal contextual information in video for action recognition. The global context is factorized into sparse relation maps. We model the relationship between points in the same line along the direction of horizon, vertical and depth at each time, which forms a 3D criss-cross structure, and duplicate the same operation with recurrent mechanism to transmit the relation between points in a line to a plane finally to the whole spatiotemporal space. Compared with the non-local method, the proposed RCCA-3D module reduces the number of parameters and FLOPs by 25% and 30% for video context modeling. We evaluate the performance of RCCA-3D with two latest action recognition networks on three datasets and make a thorough analysis of the architecture, obtaining the optimal way to factorize and fuse the relation maps. Comparisons with other state-of-the-art methods demonstrate the effectiveness and efficiency of our model. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Adaptive Variational Mode Time-frequency Analysis of Ship Radiated Noise

Adaptive Variational Mode Time-frequency Analysis of Ship Ra...

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International Conference on information Science and Control Engineering (ICISCE)

作者： Haoyi Zhang Naveed Ur Rehman Junejo Weitao Sun Hailan Chen Jiaquan Yan Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China Ilma University Karachi Pakistan School of Marine Science and Technology Northwestern Polytechnical University Xi’an China School of Science Jimei University Xiamen China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China

In this paper, we have proposed a novel time-frequency analysis method implementing adaptive variational mode decomposition (AVMD) and Wigner-Ville Distribution (WVD) to visualize the local features of ship radiated noise. Unlike the traditional methods are invalid under low signal to noise ratio (SNR), the proposed method is suitable for constructing time-frequency (TF) image with the capability of mining the local feature information accurately. Firstly, we employ complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) on the raw signal to determine the numbers of variational modes which is equal to intrinsic mode functions (IMFs) numbers. Secondly, we perform VMD algorithm to extract the local features absolutely by decomposing the raw signal into IMFs, where we set the IMFs gained by CEEMDAN as the input parameter of VMD. Finally, we adopt Wigner-Ville distribution (WVD) to draw TF signatures of all the IMFs, then superpose them to reconstruct the objected signal that can solve the cross-item problem. We have conducted the comparative experiments by the real oceanic data. Under the different SNRs, we have verified the effectiveness and robustness of the proposed method, also obtained better performance in terms of resolution than compared algorithm.

关键词： Time-frequency analysis Image resolution Sea measurements Feature extraction Robustness Time measurement Noise robustness

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Color space volume and superpixel based leukocyte image segmentation 10

Color space volume and superpixel based leukocyte image segm...

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10th International Conference on information technology in Medicine and Education, ITME 2019

作者： Zhou, Xiaogen Li, Zuoyong Wang, Chuansheng Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of Mathematics and Computer Science Fuzhou University Fuzhou China School of Computer Science and Technology Harbin University of Science and Technology Harbin China

ISBN: (纸本)9781728139173

In this paper, we propose a novel method based on color space volume and multimodal histogram thresholding (MHT) for leukocyte segmentation in human blood smear images under the rapid staining condition. The proposed method first defines a new color space volume to highlight the leukocyte's nucleus region, and it then uses the MHT method to extract the nucleus. Then, the proposed method takes the central region in the superpixel segmentation result as an initial leukocyte region, and it then uses the MHT method to extract the leukocyte region. Finally, the cytoplasm is obtained by subtracting the nucleus region from the leukocyte region. Experimental results on a dataset containing 300 leukocyte images under rapid staining condition show that the proposed method provides a higher segmentation accuracy than the counterparts. © 2019 IEEE.

关键词： Superpixels

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Using NSGA-Ⅲ for optimising biomedical ontology alignment

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CAAI Transactions on Intelligence technology 2019年第3期4卷 135-141页

作者： Xingsi Xue Jiawei Lu Junfeng Chen College of Information Science and Engineering Fujian University of TechnologyFuzhouFujianPeople’s Republic of China Intelligent Information Processing Research Center Fujian University of TechnologyFuzhouFujianPeople’s Republic of China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhouFujianPeople’s Republic of China Fujian Key Laboratory for Automotive Electronics and Electric Drive Fujian University of TechnologyFuzhouFujianPeople’s Republic of China College of IOT Engineering Hohai UniversityChangzhouJiangsuPeople’s Republic of China

To support semantic inter-operability between the biomedical information systems, it is necessary to determine the correspondences between the heterogeneous biomedical concepts, which is commonly known as biomedical ontology matching. Biomedical concepts are usually complex and ambiguous, which makes matching biomedical ontologies a challenge. Since none of the similarity measures can distinguish the heterogeneous biomedical concepts in any context independently, usually several similarity measures are applied together to determine the biomedical concepts mappings. However, the ignorance of the effects brought about by different biomedical concept mapping’s preference on the similarity measures significantly reduces the alignment’s quality. In this study, a non-dominated sorting genetic algorithm (NSGA)-III-based biomedical ontology matching technique is proposed to effectively match the biomedical ontologies, which first utilises an ontology partitioning technique to transform the large-scale biomedical ontology matching problem into several ontology segment-matching problems, and then uses NSGA-III to determine the optimal alignment without tuning the aggregating weights. The experiment is conducted on the anatomy track and large biomedic ontologies track which are provided by the Ontology Alignment Evaluation Initiative (OAEI), and the comparisons with OAEI’s participants show the effectiveness of the authors’ approach.

关键词： ontology ontologies transform together Ontology complex context effects

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Computational study the effects of venting pressure and ignition location on the fuel-air mixture explosion load in vented chamber 2nd

Computational study the effects of venting pressure and igni...

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2nd International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2018

作者： Liang, Xingxing Wang, Zhongqi Li, Pengyi Zhang, Fuquan State Key Laboratory of Explosion Science and Technology Beijing Institute of Technology Beijing100081 China Digital Performance and Simulation Technology Laboratory School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang Univeristy Fuzhou350121 China

ISBN: (纸本)9783030045845

Numerical analysis were performed to study venting explosion load of methane-air in vented chamber. The effects of venting pressure and ignition location on indoor gas explosion overpressure are discussed. The simulation results showed the expected trends are agreed with the experiments in some extent. Several peaks in overpressure were identified along the pressure–time curves. There are two typical peak overpressures in our study. The first peak is due to the vent failure. The second is caused by the external explosion. It’s not a monotonic relationship between the peak overpressure and venting explosion pressure. The influence of the venting pressure on the explosion overpressure is irregular when the venting pressure is less than 10 kPa. Then the explosion overpressure increases with the increase of the venting pressure. The results show that, the maximum peak value of overpressure is shear ignition. But the local overpressure on the pressure relief wall is stronger due to turbulence in central ignition case. Explosion venting process couples the turbulent flow and chemical reaction of combustible medium which is influenced by several parameters. © Springer Nature Switzerland AG 2019.

关键词： Computational fluid dynamics

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MR-APG: An improved model for swarm intelligence movement coordination 2nd

MR-APG: An improved model for swarm intelligence movement co...

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2nd International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2018

作者： Yang, Hui Ci, Linlin Zhang, Fuquan Yang, Minghua Mao, Yu Niu, Ke School of Computer Science and Technology Beijing Institute of Technology Beijing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China RocketMilitary Equipment Research Institute Beijing China Computer School Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9783030045845

Swarm intelligence based on the bionics have become an emerging research field, and researchers have proposed some kinds of intelligent models, such as ant colony, free group wolves, fish flock, insects group, bacteria and bacteria immune etc. The behavior of common features is: the individual behavior and function is simple, but a rally for the swarm after a complex cluster behavior ability. In mobile swarm group individuals within a certain space can keep a certain distance to avoid collision in order to group coordinated movement. In this paper, an attempt has been made to optimize the continuous and collaborative of the swarm, when the swarm nodes in the process of moving and turning. This paper puts forward the Multi-Restrained Artificial Pigeon Group (MR-APG) model based on multiple constraint factors, and introduce the concepts of quantitative coordination of nodes to optimize the nodes "over-dispersion" or "over-assembly" phenomenon. The experimental results show that, the higer α1 value will lead to average movement synchronization ratio μsync of MR-APG model will be lower. The results also show that, comparing with the traditional A/R model (μsync = 73%) and 3D-VFA model (μsync = 84%), the proposed MR-APG method can make μsync equal to 95%, moreover, its only takes half time of A/R model to make μsync equal to 80%. © Springer Nature Switzerland AG 2019.

关键词： Swarm intelligence

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Beamforming Design and Association Scheme for Multi-RIS Multi-User mmWave Systems Through Graph Neural Networks

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IEEE Transactions on Wireless Communications 2025年

作者： Liu, Mengbing Huang, Chongwen Alhammadi, Ahmed Renzo, Marco Di Debbah, Merouane Yuen, Chau Nanyang Technological University School of Electrical and Electronics Engineering 639798 Singapore Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China Technology Innovation Institute Masdar City Abu Dhabi United Arab Emirates Université Paris-Saclay CNRS Laboratoire des Signaux et Systèmes CentraleSupélec 3 Rue Joliot-Curie Gif-sur-Yvette91192 France King’s College London Centre for Telecommunications Research – Department of Engineering LondonWC2R 2LS United Kingdom Khalifa University KU6G. Research Center Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates University Paris-Saclay CentraleSupelec Gif-sur-Yvette91192 France

Reconfigurable intelligent surface (RIS) is emerging as a promising technology for next-generation wireless communication networks, offering a variety of merits such as the ability to tailor the communication environment. Moreover, deploying multiple RISs helps mitigate severe signal blocking between the base station (BS) and users, providing a practical and efficient solution to enhance the service coverage. However, fully reaping the potential of a multi-RIS aided communication system requires solving a non-convex optimization problem. This challenge motivates the adoption of learning-based methods for determining the optimal policy. In this paper, we introduce a novel heterogeneous graph neural network (GNN) to effectively leverage the graph topology of a wireless communication environment. Specifically, we design an association scheme that selects a suitable RIS for each user. Then, we maximize the weighted sum rate (WSR) of all the users by iteratively optimizing the RIS association scheme, and beamforming designs until the considered heterogeneous GNN converges. Based on the proposed approach, each user is associated with the best RIS, which is shown to significantly improve the system capacity in multi-RIS multi-user millimeter wave (mmWave) communications. Specifically, simulation results demonstrate that the proposed heterogeneous GNN closely approaches the performance of the high-complexity alternating optimization (AO) algorithm in the considered multi-RIS aided communication system, and it outperforms other benchmark schemes. Moreover, the performance improvement achieved through the RIS association scheme is shown to be of the order of 30%. © 2002-2012 IEEE.

关键词： Beamforming

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Understanding the phase separation characteristics of nucleocapsid protein provides a new therapeutic opportunity against SARS-CoV-2

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Protein & Cell 2021年第9期12卷 734-740页

作者： Dan Zhao Weifan Xu Xiaofan Zhang Xiaoting Wang Yiyue Ge Enming Yuan Yuanpeng Xiong Shenyang Wu Shuya Li Nian Wu Tingzhong Tian Xiaolong Feng Hantao Shu Peng Lang Jingxin Li Fengcai Zhu Xiaokun Shen Haitao Li Pilong Li Jianyang Zeng Institute for Interdisciplinary Information Sciences Tsinghua UniversityBeijing 100084China Beijing Advanced Innovation Center for Structural Biology Frontier Research Center for Biological Structure School of Life SciencesTsinghua UniversityBeijing 100084China Winghua-Peking Center for Life Sciences Tsinghua UniversityBeijing 100084China Silexon Al Technology Co. LtdNanjing 210033China NHC Key Laboratory of Enteric Pathogenic Microbiology Jiangsu Provincial Center for Diseases Control and PreventionNanjing 210009China Bioinformatics Division BNRIST/Department of Computer Science and TechnologyTsinghua UniversityBeijing 100084China Protein Preparation and Identification Facility Technology Center for Protein ScienceTsinghua UniversityBeijing 100084China Institute of Pathology Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430074China Center for Global Health Nanjing Medical UniversityNanjing 210009China Convalife(Shanghai)Co. LtdShanghai 201203China Ministry of Education Key Laboratory of Protein Sciences Beijing Advaneed Innovation Center for Structural BiologyBeijing Frontier Research Center for Biological StructureDepartment of Basic Medical SciencesSchool of MedicineTsinghua UniversityBeijing 100084China

Dear Editor To date,tens of millions of people have been infected with severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),causing the outbreak of the respiratory disease named the coronavirus disease 2019(COVID-19).As a newly emerged member of the coronavirus family,SARS-CoV-2 is an enveloped positive-strand RNA virus,which has probably the largest genome(approximately 30 kb)among all RNA *** nucleocapsid(N)protein of SARS-CoV-2 is mainly responsible for recognizing and wrapping viral RNA into helically symmetric structures(Malik,2020).

关键词： respiratory acute therapeutic

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