Multi-modal entity alignment (MMEA) aims to identify equivalent entities across different multi-modal knowledge graphs. In these graphs, entities are enriched with information from various modalities, such as text, im...
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ISBN:
(数字)9798331508821
ISBN:
(纸本)9798331508838
Multi-modal entity alignment (MMEA) aims to identify equivalent entities across different multi-modal knowledge graphs. In these graphs, entities are enriched with information from various modalities, such as text, images, and numerical data, making the alignment task both challenging and crucial for improving knowledge graph quality. Current MMEA algorithms typically encode entity information separately for each modality using corresponding encoders, and then integrate these representations through various modal fusion strategies. However, these methods often fail to fully exploit the multi-modal information of entities. To address this issue, we propose a feature-enhanced multi-modal entity alignment transformer (FEMEAT). FEMEAT enhances entity attribute information by incorporating modal distribution data, which captures the inherent distribution of different modalities for each entity. This inclusion allows the model to have a richer understanding of entity characteristics across modalities. Additionally, FEMEAT utilizes an Optical Character Recognition (OCR) model to extract and incorporate textual information from images. By integrating this text extracted from images, the model can better utilize the visual modality, enhancing its ability to understand and process multi-modal information. Furthermore, FEMEAT employs a multi-head cross-modal attention (MHCA) mechanism for modal fusion to achieve comprehensive multi-modal entity representation. This mechanism enables the model to attend to different modalities simultaneously and learn a detailed representation of entities by considering the interactions between modalities. The multi-head cross-modal attention mechanism facilitates a nuanced understanding and integration of multi-modal data. Experimental results demonstrate that our model achieves state-of-the-art (SOTA) performance across various training scenarios. The code and datasets used in this study can be accessed at https://***/zewenD/FEMEAT.
Graph pattern matching is a technique widely used in various fields such as protein structure analysis, social group querying, and expert localization. This technique involves finding matching subgraphs in large socia...
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ISBN:
(数字)9798350373554
ISBN:
(纸本)9798350373561
Graph pattern matching is a technique widely used in various fields such as protein structure analysis, social group querying, and expert localization. This technique involves finding matching subgraphs in large social networks that align with the patterns specified in the pattern graph. In this paper, we focus on a specific sub-problem in social group querying, known as the cooperative team query, which arises from practical applications, where the nodes in the pattern graph and the data graph represent team member entities, while the edges represent their social relationships. We note that the requirements of many teams in the real world are dynamic, necessitating iterative computation for graph pattern matching using traditional methods. To address this challenge in highly dynamic systems, we propose a graph pattern matching method based on core pattern graph matching cache. This approach involves extracting the core pattern graph, and comprising core team members based on the characteristics of cooperative teams. The core graph-based matching cache enables the second half of the algorithm to operate on an order-of-magnitude smaller graph, significantly improving efficiency. Additionally, the multi-threaded approach fully leverages hardware resources, synchronizing multiple matching result of the core pattern graph to reduce matching time. Experimental results on three real social network datasets demonstrate that our proposed algorithm, Core Pattern Graph Matching Cache-based Multi-threaded Exploration (CCMTE), significantly outperforms existing methods in terms of efficiency.
In this paper, we propose a fault location of distribution network based on narrowband communication technology, which belong to the field fault monitoring and location of distribution network. The whole system consis...
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When solving the problems of data scarcity and cross-domain adaptability in bearing fault diagnosis, traditional methods often find it difficult to obtain a large amount of fault data, which limits the application and...
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This paper presents a Scientific Literature Management Platform (SLMP, demo link1 ) based on large language models (LLMs). The platform consists of four modules: literature management, literature extraction, literatur...
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Internet of Things (IoT) applications have recently been widely used in safety-critical scenarios. To prevent sensitive information leaks, IoT device vendors provide hardware-assisted protections, called Trusted Execu...
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To address challenges in steel surface defect detection, such as low accuracy and slow processing speed, an enhanced algorithm is proposed. The C3 module is replaced with GSConv (multi-channel shuffle convolution) to ...
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Accurate control of slab temperature and heating rate is an important significance to improve product performance and reduce carbon emissions for steel rolling reheating furnace(SRRF).Firstly,a spatial temporal distri...
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Accurate control of slab temperature and heating rate is an important significance to improve product performance and reduce carbon emissions for steel rolling reheating furnace(SRRF).Firstly,a spatial temporal distributed-nonlinear autoregressive with exogenous inputs correlation model(STD-NARXCM)to spatial temporal distributed-autoregressive with exogenous inputs correlation model(STD-ARXCM)in working point is ***,a new coordinated time-sharing control architecture in different time periods is proposed,which is along the length of the SRRF to improve the control ***,a hybrid control algorithm of expert-fuzzy is proposed to improve the dynamic of the temperature and the heating rate during time period 0 to t_(1).A hybrid control algorithm of expert-fuzzy-PID is proposed to enhance the control accuracy and the heating rate during time period t_(1) to t_(2).A hybrid control algorithm of expert-active disturbance rejection control(ADRC)is proposed to boost the anti-interference and the heating rate during time period t_(2) to t_(3).Finally,the experimental results show that the coordinated time-sharing algorithm can meet the process requirements,the maximum deviation of temperature value is 8-13.5℃.
We experimentally demonstrate a Yb-doped all-fiber mode-locked laser based on the Mamyshev *** entire experimental setup operates only by injecting pump powers and adjusting polarization controllers(PCs),which realize...
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We experimentally demonstrate a Yb-doped all-fiber mode-locked laser based on the Mamyshev *** entire experimental setup operates only by injecting pump powers and adjusting polarization controllers(PCs),which realizes *** types of pulse patterns are observed at different pump powers and polarization states,including single pulses and up to eight-pulse bound-state *** operating wavelength of single-pulse mode-locking switching between 1072.3and 1043.1 nm can be realized by increasing the pump power while keeping the PCs in a fixed *** design can provide an attractive experimental model for all-fiber and self-starting Mamyshev oscillators.
Mean-field variational inference (VI) is computationally scalable, but its highly-demanding independence requirement hinders it from being applied to wider scenarios. Although many VI methods that take correlation int...
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