Presupernova strongly screening electron capture of iron group nuclei

Liu, Jing-Jing; Hao, Liang; Li, Dongmei

doi:10.1142/s0217732322500158

Cited by 1 publication

(4 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, we have studied the masses of light pseudoscalar mesons by a new method based on the previous study [10,11]. The integral formula (5) for the multi-valued complex function is applied to perform the integrals arise in the amplitudes of triangle diagrams associated with the axial vector current, the integration results allows to cancel the ABJ anomaly term, therefore eliminate the anomalous problem.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…For meson at the ground state the integer takes value n = 1, for excited state it takes value of larger integers. The calculation of various light meson masses is presented in section 5, comparing with our previous work [11], the formula (25) gives more accurate meson masses.…”

Section: Introductionmentioning

confidence: 86%

“…Concerning the nature of baryons in this picture, one approach is to view baryons as soliton solutions of the effective theory of meson fields. In this paper, we study the mass of some light mesons by a method developed in the previous works by the first author [10,11]. The particle content of the pseudoscalar mesons are shown in Figure . 1.…”

Section: Introductionmentioning

confidence: 99%

“…In spite of success of the effective field theories, these theories always have the U (1) problem [18][19][20]. In previous works by the first author, a new anomaly free condition for chiral anomaly is used to study the masses of some neutral pseudoscalar mesons [10,11]. In this paper, we will generalize this method to other pseudoscalar mesons.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Geography of Technology Transfer in China

Liu

2023

East China Normal University Scientific Reports

View full text Add to dashboard Cite

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.

show abstract

Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%