Proton fraction in neutron stars

The proton fraction in {\sl \beta}-stable neutron stars is investigated within the framework of the Skyrme-Hartree-Fock theory using the extended Skyrme effective interaction for the first time. The calculated results show that the proton fraction disappears at high density, which implies that the pure neutron matter may exist in the interior of neutron stars. The incompressibility of the nuclear equation of state is shown to be more important to determine the proton fraction. Meanwhile, it is indicated that the addition of muons in neutron stars will change the proton fraction. It is also found that the higher-order terms of the nuclear symmetry energy have obvious effects on the proton fraction and the parabolic law of the nuclear symmetry energy is not enough to determine the proton fraction.Comment: 4 Pgaes in REVTex, 2 Figures, 1 Tabl

Contributions of hyperon-hyperon scattering to subthreshold cascade production in heavy ion collisions

Using a gauged flavor SU(3)-invariant hadronic Lagrangian, we calculate the cross sections for the strangeness-exchange reactions YY to N\Xi (Y=\Lambda, \Sigma) in the Born approximation. These cross sections are then used in the Relativistic Vlasov-Uehling-Uhlenbeck (RVUU) transport model to study \Xi production in Ar+KCl collisions at incident energy of 1.76A GeV and impact parameter b=3.5 fm. We find that including the contributions of hyperon-hyperon scattering channels strongly enhances the yield of \Xi, leading to the abundance ratio \Xi^{-}/(\Lambda+\Sigma^{0})=3.38E-3, which is essentially consistent with the recently measured value of $(5.6 \pm 1.2_{-1.7}^{+1.8})\times 10^{-3}$ by the HADES collaboration at GSI.Comment: 8 pages, 5 figure

Neyman-pearson classiffication under high-dimensional settings

Most existing binary classification methods target on the optimization of the overall classification risk and may fail to serve some real-world applications such as cancer diagnosis, where users are more concerned with the risk of misclassifying one specific class than the other. Neyman-Pearson (NP) paradigm was introduced in this context as a novel statistical framework for handling asymmetric type I/II error priorities. It seeks classifiers with a minimal type II error and a constrained type I error under a user specified level. This article is the first attempt to construct classifiers with guaranteed theoretical performance under the NP paradigm in high-dimensional settings. Based on the fundamental Neyman-Pearson Lemma, we used a plug-in approach to construct NP-Type classifiers for Naive Bayes models. The proposed classifiers satisfy the NP oracle inequalities, which are natural NP paradigm counterparts of the oracle inequalities in classical binary classification. Besides their desirable theoretical properties, we also demonstrated their numerical advantages in prioritized error control via both simulation and real data studies

Towards Novel Class Discovery: A Study in Novel Skin Lesions Clustering

Existing deep learning models have achieved promising performance in recognizing skin diseases from dermoscopic images. However, these models can only recognize samples from predefined categories, when they are deployed in the clinic, data from new unknown categories are constantly emerging. Therefore, it is crucial to automatically discover and identify new semantic categories from new data. In this paper, we propose a new novel class discovery framework for automatically discovering new semantic classes from dermoscopy image datasets based on the knowledge of known classes. Specifically, we first use contrastive learning to learn a robust and unbiased feature representation based on all data from known and unknown categories. We then propose an uncertainty-aware multi-view cross pseudo-supervision strategy, which is trained jointly on all categories of data using pseudo labels generated by a self-labeling strategy. Finally, we further refine the pseudo label by aggregating neighborhood information through local sample similarity to improve the clustering performance of the model for unknown categories. We conducted extensive experiments on the dermatology dataset ISIC 2019, and the experimental results show that our approach can effectively leverage knowledge from known categories to discover new semantic categories. We also further validated the effectiveness of the different modules through extensive ablation experiments. Our code will be released soon.Comment: 10 pages, 1 figure,Accepted by miccai 202