Deformed charmed hypernuclei

Liu, Yi-Xiu; Chen, C. F.; Chen, Q. B.; Xue, Huai-Tong; Schulze, H.-J.; Zhou, Xian-Rong

doi:10.1103/physrevc.108.064312

Cited by 1 publication

(1 citation statement)

References 66 publications

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“…In the hypernucleus 17 Λ F with the adjusted force, an s-state Λ is bound by B Λ = 13.8 MeV and a p-state Λ by B Λ = 3.2 MeV ([101]) or B Λ = 3.9 MeV ([110]). In the latter case both nuclear core and hyperon orbital are prolately deformed with β 2 ≈ 0.2, which causes an important gain in binding energy [20,28,53,56,57]. This is also decisive for the effect of the hyperon on the proton separation energy, which can better be seen by the precise values listed in Table II.…”

Section: We Begin In Tablementioning

confidence: 92%

Geography of Technology Transfer in China

Liu

2023

East China Normal University Scientific Reports

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Graphs can model complex relationships between objects, enabling a myriad of Web applications such as online page/article classification and social recommendation. While graph neural networks (GNNs) have emerged as a powerful tool for graph representation learning, in an end-to-end supervised setting, their performance heavily relies on a large amount of task-specific supervision. To reduce labeling requirement, the "pre-train, fine-tune" and "pre-train, prompt" paradigms have become increasingly common. In particular, prompting is a popular alternative to fine-tuning in natural language processing, which is designed to narrow the gap between pre-training and downstream objectives in a task-specific manner. However, existing study of prompting on graphs is still limited, lacking a universal treatment to appeal to different downstream tasks. In this paper, we propose GraphPrompt, a novel pre-training and prompting framework on graphs. GraphPrompt not only unifies pre-training and downstream tasks into a common task template, but also employs a learnable prompt to assist a downstream task in locating the most relevant knowledge from the pre-trained model in a task-specific manner. Finally, we conduct extensive experiments on five public datasets to evaluate and analyze GraphPrompt. CCS CONCEPTS• Computing methodologies → Learning latent representations; • Information systems → Data mining.

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Section: We Begin In Tablementioning

confidence: 92%

Geography of Technology Transfer in China

Liu

2023

East China Normal University Scientific Reports

View full text Add to dashboard Cite

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Deformed charmed hypernuclei

Cited by 1 publication

References 66 publications

Geography of Technology Transfer in China

Geography of Technology Transfer in China

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