Titlebook: Observational Astrophysics; Pierre Léna,François Lebrun,François Mignard Textbook 19982nd edition Springer-Verlag Berlin Heidelberg 1998 a

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0941-7834 ehind space missions aimed for the next decades. Avoiding particulars, it covers the whole of the electromagnetic spectrum, and touches upon the "new astronomies" becoming possible with gravitational waves and neutrinos.978-3-642-08336-5978-3-662-03685-3Series ISSN 0941-7834 Series E-ISSN 2196-9698

Cholagogue

oach based on Contrastive Learning, named CLCDDA. This approach combines the Multiple Kernel Maximum Mean Discrepancy and enhanced contrastive learning to obtain high-quality target domain data with pseudo labels, which is used to improve the performance of the generative model. In addition, contras

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香料

Pierre Léna,François Lebrun,François Mignardoach based on Contrastive Learning, named CLCDDA. This approach combines the Multiple Kernel Maximum Mean Discrepancy and enhanced contrastive learning to obtain high-quality target domain data with pseudo labels, which is used to improve the performance of the generative model. In addition, contras

步兵

Pierre Léna,François Lebrun,François Mignardc skillfully captures the subtle patterns in student-concept interactions by utilizing the synergistic impacts of these techniques, yielding a more nuanced understanding that drives the recommendation process. Extensive experiments conducted on a real-world MOOC dataset show that MMPDRec outperforms

fibula

Pierre Léna,François Lebrun,François Mignardoach based on Contrastive Learning, named CLCDDA. This approach combines the Multiple Kernel Maximum Mean Discrepancy and enhanced contrastive learning to obtain high-quality target domain data with pseudo labels, which is used to improve the performance of the generative model. In addition, contras

Oversee

Pierre Léna,François Lebrun,François Mignardhat the model achieves an F1 score of 71.11% on the OntoNotes-5.0 Chinese dataset for the CoNLL metric, an improvement of 0.95% compared to the baseline model. On the self-constructed "Tibet News Traffic" dataset, the F1 score reaches 73.62%, an increase of 2.58% compared to the baseline model. Thes

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Pierre Léna,François Lebrun,François Mignardcoder in the UIE model, sequence labeling decoder CRF is introduced. Compared with the baseline model, UIE-ERNIE-CRF performs well on the precision rate, recall rate and F1 value. And the ablation experiment shows that introducing the ERNIE and CRF is effective for entity relation extraction of lega

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Astronomy and Astrophysics Libraryhttp://image.papertrans.cn/o/image/700356.jpg

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