As an important model of machine learning, Bayesian networks are widely applied into medical diagnosis and achieve good performances in practical applications. Compared to black-box models, Bayesian networks can clear...
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Ontology matching plays a crucial role in the realm of ontology engineering, specifically in ontology development and integration. It serves the purpose of resolving the issue of heterogeneous semantics between ontolo...
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Link prediction is used to complete the knowledge graph. Convolutional neural network models are commonly used for link prediction tasks, but they only consider the direct relations between entity pairs, ignoring the ...
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This paper introduces a novel approach to reduce computational latencies in cloud platforms by integrating data compression with Non-Orthogonal Multiple Access (NOMA) technologies. We propose a computational task offl...
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Medical reports play an important role in diagnosing a patient’s illness. However, writing medical reports is time-consuming and labor-intensive, and writing high-quality medical reports often requires extensive clin...
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In a crowd-sensing system, effective transmission of sensing data is an important step in improving task distribution efficiency. In real-life scenarios, when resources are limited or geographic conditions are harsh, ...
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State-of-the-art recommender systems are increasingly focused on optimizing implementation efficiency, such as enabling on-device recommendations under memory constraints. Current methods commonly use lightweight embe...
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State-of-the-art recommender systems are increasingly focused on optimizing implementation efficiency, such as enabling on-device recommendations under memory constraints. Current methods commonly use lightweight embeddings for users and items or employ compact embeddings to enhance reusability and reduce memory usage. However, these approaches consider only the coarse-grained aspects of embeddings, overlooking subtle semantic nuances. This limitation results in an adversarial degradation of meta-embedding performance, impeding the system's ability to capture intricate relationships between users and items, leading to suboptimal recommendations. To address this, we propose a novel approach to efficiently learn meta-embeddings with varying grained and apply fine-grained meta-embeddings to strengthen the representation of their coarse-grained counterparts. Specifically, we introduce a recommender system based on a graph neural network, where each user and item is represented as a node. These nodes are directly connected to coarse-grained virtual nodes and indirectly linked to fine-grained virtual nodes, facilitating learning of multi-grained semantics. Fine-grained semantics are captured through sparse meta-embeddings, which dynamically balance embedding uniqueness and memory constraints. To ensure their sparseness, we rely on initialization methods such as sparse principal component analysis combined with a soft thresholding activation function. Moreover, we propose a weight-bridging update strategy that aligns coarse-grained meta-embedding with several fine-grained meta-embeddings based on the underlying semantic properties of users and items. Comprehensive experiments demonstrate that our method outperforms existing baselines. The code of our proposal is available at https://***/htyjers/C2F-MetaEmbed.
Similar to air reverberation chambers, non-anechoic water tanks are important acoustic measurement devices that can be used to measure the sound power radiated from complex underwater sound sources using diffusion fie...
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Similar to air reverberation chambers, non-anechoic water tanks are important acoustic measurement devices that can be used to measure the sound power radiated from complex underwater sound sources using diffusion field theory. However,the problem of the poor applicability of low-frequency measurements in these tanks has not yet been solved. Therefore,we propose a low-frequency acoustic measurement method based on sound-field correction(SFC) in an enclosed space that effectively solves the problem of measuring the sound power from complex sound sources below the Schroeder cutoff frequency in a non-anechoic tank. Using normal mode theory, the transfer relationship between the mean-square sound pressure in an underwater enclosed space and the free-field sound power of the sound source is established, and this is regarded as a correction term for the sound field between this enclosed space and the free field. This correction term can be obtained based on previous measurements of a known sound source. This term can then be used to correct the mean-square sound pressure excited by any sound source to be tested in this enclosed space and equivalently obtain its free-field sound power. Experiments were carried out in a non-anechoic water tank(9.0 m × 3.1 m × 1.7 m) to confirm the validity of the SFC method. Through measurements with a spherical sound source(whose free-field radiation characteristics are known),the correction term of the sound field between this water tank and the free field was obtained. On this basis, the sound power radiated from a cylindrical shell model under the action of mechanical excitation was measured. The measurement results were found to have a maximum deviation of 2.9 d B from the free-field results. These results show that the SFC method has good applicability in the frequency band above the first-order resonant frequency in a non-anechoic tank. This greatly expands the potential low-frequency applications of non-anechoic tanks.
In a crowdsensing system, effective transmission of sensing data is an important step in improving task distribution efficiency. In real-life scenarios, when resources are limited or geographic conditions are harsh, U...
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With the development of the miniaturization of electronic equipment and lightweight weapon equipment,there are new requirements for electromagnetic wave absorption material(EMWAM).EMWAM has outstanding electromagnetic...
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With the development of the miniaturization of electronic equipment and lightweight weapon equipment,there are new requirements for electromagnetic wave absorption material(EMWAM).EMWAM has outstanding electromagnetic wave absorption properties and lightweight characteristics become an important direction of *** this study,graphene/g-C_(3)N_(4)(GGCN)EMWAM was first synthesized in situ by simple heat treatment,in which the g-C_(3)N_(4) had a porous structure and dispersed on the surface of *** impedance matching of the GGCN was well adjusted by decreasing the dielectric constant and attenuation constant due to the g-C_(3)N_(4) semiconductor property and the graphite-like *** EMW loss mechanism of GGCN was also analyzed by simulating GGCN’s electric field mode distribution and resistance loss power *** analysis result shows that the distribution of g-C_(3)N_(4) among GGCN sheets can produce more polarization effects and relaxation effects by increasing the lamellar ***,the polarization loss of GGCN could be increased successfully by porous g-C_(3)N_(4).Ultimately,the EMW absorption property of GGCN is optimized significantly,and GGCN exhibits excellent EMW absorption *** the thickness is 2 mm,the effective absorption bandwidth(EAB)can reach 4.6 GHz,and when the thickness is 4.5 mm,the minimum reflection loss(RLmin)at 4.56 GHz can reach-34.69 ***,the practical application of EMWAM was studied by radar cross-section(RCS)simulation,showing that GGCN has a good application prospect.
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