Effects of a kaonic meson on the ground-state properties of nuclei *

Guo, Jing; Chen, DengHui; Zhou, Xian-Rong; Chen, QiBo; Schulze, H.-J.

doi:10.1088/1674-1137/ac5601

Cited by 2 publications

(3 citation statements)

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“…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. Namely, the proton separation energies for s[000], p[101], p[110] hypernuclei SLy4 (s t2 = 1.000, s b4 = 1.000) 19 Na(+L) 18 Ne(+L)…”

Section: We Begin In Tablementioning

confidence: 99%

“…In nuclei with added strangeness, strange particles might also affect the drip lines. Previous studies have shown that the weakly-bound nuclear cores of drip line nuclei might be stabilized by the addition of further strongly-bound particles, and the drip line might even be extended by added hyperons [12][13][14][15][16][17] or kaons [18].…”

Section: Introductionmentioning

confidence: 99%

“…FIG.2. Same as Fig.1, but (a) for19 Na and18 Ne with energies E( 19 Na) = −131.82 MeV and E( 18 Ne) = −132.14 MeV, (b) for16 Ne and14 O with energies E( 16 Ne) = −97 33. MeV and E( 14 O) = −98 73.…”

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confidence: 98%

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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: 99%