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S. C. Han

Publications and source records attributed to S. C. Han.

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Exploring Hyperon Skyrme Forces in Multi-$\Lambda$ Hypernuclei and Neutron Star Matter

A major source of uncertainty in modeling the strangeness-rich interiors of neutron stars arises from the poorly constrained two-body and three-body interactions among hyperons and nucleons. We perform a comprehensive Bayesian analysis of the $\Lambda\Lambda$ and $\Lambda\Lambda N$ interaction parameters within the Skyrme Hartree-Fock framework, constrained by both hypernuclei experimental data and astrophysical observations. Our results show that the parameter space of the $\Lambda\Lambda$ interaction is tightly constrained by combining nuclear and astrophysical data, while the parameters of the $\Lambda\Lambda N$ three-body interaction remain sensitive to astrophysical inputs alone. Specifically, the local, momentum-independent two-body interaction parameter $\lambda_0$ is tightly constrained and predominantly attractive, while the momentum-dependent parameters $\lambda_1$ and $\lambda_2$ contribute repulsive effects at high densities. A key role is played by the $\Lambda\Lambda$ potential depth in pure $\Lambda$ matter, which effectively constrains the two-body $\Lambda\Lambda$ interaction and governs the balance between attraction at low densities and repulsion at high densities. The repulsive components of $\Lambda\Lambda$ interactions then decrease hyperon fractions and reconcile hyperon-rich equations of state with the observed $\sim2\,M_{\odot}$ neutron stars, increasing the maximum mass by up to 22\%. The inclusion of $\Lambda\Lambda N$ three-body forces further stiffens the EOS, raising the maximum mass by up to $\sim 0.1\,M_{\odot}$. Our study represents a promising step toward a complete, experimentally grounded description of dense matter across a wide range of densities and strangeness compositions.

nucl-th

A survey of joint intent detection and slot-filling models in natural language understanding

Intent classification and slot filling are two critical tasks for natural language understanding. Traditionally the two tasks have been deemed to proceed independently. However, more recently, joint models for intent classification and slot filling have achieved state-of-the-art performance, and have proved that there exists a strong relationship between the two tasks. This article is a compilation of past work in natural language understanding, especially joint intent classification and slot filling. We observe three milestones in this research so far: Intent detection to identify the speaker's intention, slot filling to label each word token in the speech/text, and finally, joint intent classification and slot filling tasks. In this article, we describe trends, approaches, issues, data sets, evaluation metrics in intent classification and slot filling. We also discuss representative performance values, describe shared tasks, and provide pointers to future work, as given in prior works. To interpret the state-of-the-art trends, we provide multiple tables that describe and summarise past research along different dimensions, including the types of features, base approaches, and dataset domain used.

cs.CL