Accurate property assessment is essential in the dynamic and ever-changing world of real estate so that both buyers and sellers can make well-informed choices. We are aware that setting the appropriate asking price fo...
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The application of contrastive learning (CL) to collaborative filtering (CF) in recommender systems has achieved remarkable success. CL-based recommendation models mainly focus on creating multiple augmented views by ...
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This research study presents an initiative that seeks to revolutionize the process of tree enumeration and categorization, traditionally carried out manually. It harnesses the power of image analytics, the system aims...
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Breast cancer forms one of the most common types of cancer among women worldwide;therefore, it highlights the importance of having rapid and timely diagnosis methods. Here, we will survey the use of convolutional neur...
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Metal active gas(MAG)welding is one of the widely applied welding techniques using argon and carbon dioxide as shielding *** response to the problem of welding halo and drag shadow during the image acquisition process...
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Metal active gas(MAG)welding is one of the widely applied welding techniques using argon and carbon dioxide as shielding *** response to the problem of welding halo and drag shadow during the image acquisition process of it,which makes it difficult to accurately extract the contour of the molten pool,this paper proposes a molten pool edge detection method that combines dark channel prior dehazing(DCPD)and improved single scale Retinex image enhancement *** method overcomes the problem of excessive edge noise in the original molten pool image and the difficulty in feature extraction caused by the dark part of the molten pool after DCPD *** comparative experiments and ablation experiments,it has been shown that the algorithm proposed in this paper has significantly improved the enhancement effect and feature extraction effect,extracting accurate and complete molten pool contours.
The mechanism of magnetic nanoparticles(MNPs)affecting magnetic field uniformity is studied in this *** spatial distribution of MNPs in liquid is simulated based on Monte Carlo *** induced field of the single MNP is c...
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The mechanism of magnetic nanoparticles(MNPs)affecting magnetic field uniformity is studied in this *** spatial distribution of MNPs in liquid is simulated based on Monte Carlo *** induced field of the single MNP is combined with the magnetic field distribution of *** the simulation,magnetic field uniformity is described by a statistical *** the chemical shift(CS)and full width at half maximum(FWHM)of magnetic resonance(MR)spectrum can reflect the uniformity of magnetic field,the simulation is verified by spectrum *** and measurement results prove that the CS and FWHM of the MR spectrum are basically positively correlated with the concentration of MNPs and negatively correlated with the *** research results can explain how MNPs play a role in MR by affecting the uniform magnetic field,which is of great significance in improving the temperature measurement accuracy of magnetic nanothermometers and the spatial resolution of magnetic particle imaging.
Dear editor,In recent decades, the six-DOF(degree-of-freedom) spacecraft(i.e., motions of the attitude and the orbit) control has received considerable attention owing to its broad applications in aerospace missions l...
Dear editor,In recent decades, the six-DOF(degree-of-freedom) spacecraft(i.e., motions of the attitude and the orbit) control has received considerable attention owing to its broad applications in aerospace missions like space station construction [1, 2].
In this paper, a discrete-time projection neural network with an adaptive step size (DPNN) is proposed for distributed global optimization. The DPNN is proven to be convergent to a Karush-Kuhn-Tucker point. Several DP...
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Modern software development has moved toward agile growth and rapid delivery,where developers must meet the changing needs of users *** such a situation,plug-and-play Third-Party Libraries(TPLs)introduce a considerabl...
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Modern software development has moved toward agile growth and rapid delivery,where developers must meet the changing needs of users *** such a situation,plug-and-play Third-Party Libraries(TPLs)introduce a considerable amount of convenience to ***,selecting the exact candidate that meets the project requirements from the countless TPLs is challenging for *** works have considered setting up a personalized recommender system to suggest TPLs for ***,these approaches rarely consider the complex relationships between applications and TPLs,and are unsatisfactory in accuracy,training speed,and convergence *** this paper,we propose a new end-to-end recommendation model called Neighbor Library-Aware Graph Neural Network(NLA-GNN).Unlike previous works,we only initialize one type of node embedding,and construct and update all types of node representations using Graph Neural Networks(GNN).We use a simplified graph convolution operation to alternate the information propagation process to increase the training efficiency and eliminate the heterogeneity of the app-library bipartite graph,thus efficiently modeling the complex high-order relationships between the app and the *** experiments on large-scale real-world datasets demonstrate that NLA-GNN achieves consistent and remarkable improvements over state-of-the-art baselines for TPL recommendation tasks.
Emotion recognition from physiological signals(ERPS)has drawn tremendous attention and can be potentially applied to numerous *** physiological signals are nonstationary time series with high sampling frequency,it is ...
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Emotion recognition from physiological signals(ERPS)has drawn tremendous attention and can be potentially applied to numerous *** physiological signals are nonstationary time series with high sampling frequency,it is challenging to directly extract features from ***,there are 2 major challenges in ERPS:(a)how to adequately capture the correlations between physiological signals at different times and between different types of physiological signals and(b)how to effectively minimize the negative effect caused by temporal covariate shift(TCS).To tackle these problems,we propose a domain generalization and residual network-based approach for emotion recognition from physiological signals(DGR-ERPS).We first pre-extract time-and frequency-domain features from the original time series to compose a new time ***,in order to fully extract the correlation information of different physiological signals,these time series are converted into 3D image data to serve as input for a residual-based feature encoder(RBFE).In addition,we introduce a domain generalization-based technique to mitigate the issue posed by *** have conducted extensive experiments on 2 real-world datasets,and the results indicate that our DGR-ERPS achieves superior performance under both TCS and non-TCS scenarios.
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