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Yin Fan

Publications and source records attributed to Yin Fan.

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Proton radioactivity in deformed nuclei with microscopic optical potential: A novel angular-dependent emission mechanism in the nanosecond-lived $^{149}$Lu

We present a theoretical description of proton radioactivity in 149Lu, the most oblate deformed proton emitter known, by combining a deformed microscopic optical potential derived from ab initio nuclear matter calculations with the Wentzel-Kramers-Brillouin penetration probabilities and the assault frequency of the emitted proton estimated through a new harmonic-oscillator-inspired scheme. We predict a novel angular-dependent phenomenon unprecedented in spherical proton emitters: the disappearance of classically allowed regions at small polar angles $(\theta\leq 21^\circ)$. Our framework yields a half-life $T_{1/2}=467^{+143}_{-108}$ ns for 149Lu, in excellent agreement within uncertainties with the experimental value $450^{+170}_{-100}$ ns. Deformation analysis rigorously excludes configurations with $|\beta_2|\geq 0.32$. Extensions to 150, 151Lu and their isomers also achieve excellent agreement with experimental half-life data. We further predict 148Lu as another highly oblate $(\beta_2 = -0.166)$ proton emitter with a half-life $T_{1/2}=4.42$ ns. This work validates deformed microscopic optical potentials as a robust predictive tool for drip-line proton emitters and provides quantitative evidence for deformation effects in exotic decays.

nucl-th

Unknown Identity Rejection Loss: Utilizing Unlabeled Data for Face Recognition

Face recognition has advanced considerably with the availability of large-scale labeled datasets. However, how to further improve the performance with the easily accessible unlabeled dataset remains a challenge. In this paper, we propose the novel Unknown Identity Rejection (UIR) loss to utilize the unlabeled data. We categorize identities in unconstrained environment into the known set and the unknown set. The former corresponds to the identities that appear in the labeled training dataset while the latter is its complementary set. Besides training the model to accurately classify the known identities, we also force the model to reject unknown identities provided by the unlabeled dataset via our proposed UIR loss. In order to 'reject' faces of unknown identities, centers of the known identities are forced to keep enough margin from centers of unknown identities which are assumed to be approximated by the features of their samples. By this means, the discriminativeness of the face representations can be enhanced. Experimental results demonstrate that our approach can provide obvious performance improvement by utilizing the unlabeled data.

cs.CV

iQIYI-VID: A Large Dataset for Multi-modal Person Identification

Person identification in the wild is very challenging due to great variation in poses, face quality, clothes, makeup and so on. Traditional research, such as face recognition, person re-identification, and speaker recognition, often focuses on a single modal of information, which is inadequate to handle all the situations in practice. Multi-modal person identification is a more promising way that we can jointly utilize face, head, body, audio features, and so on. In this paper, we introduce iQIYI-VID, the largest video dataset for multi-modal person identification. It is composed of 600K video clips of 5,000 celebrities. These video clips are extracted from 400K hours of online videos of various types, ranging from movies, variety shows, TV series, to news broadcasting. All video clips pass through a careful human annotation process, and the error rate of labels is lower than 0.2\%. We evaluated the state-of-art models of face recognition, person re-identification, and speaker recognition on the iQIYI-VID dataset. Experimental results show that these models are still far from being perfect for the task of person identification in the wild. We proposed a Multi-modal Attention module to fuse multi-modal features that can improve person identification considerably. We have released the dataset online to promote multi-modal person identification research.

cs.CV