Canonical interpretation of Y(10750)Y(10750) and Υ(10860)\Upsilon(10860) in the Υ\Upsilon family

Inspired by the new resonance $Y(10750)$, we calculate the masses and two-body OZI-allowed strong decays of the higher vector bottomonium sates within both screened and linear potential models. We discuss the possibilities of $\Upsilon(10860)$ and $Y(10750)$ as mixed states via the $S-D$ mixing. Our results suggest that $Y(10750)$ and $\Upsilon(10860)$ might be explained as mixed states between $5S$- and $4D$-wave vector $b\bar{b}$ states. The $Y(10750)$ and $\Upsilon(10860)$ resonances may correspond to the mixed states dominated by the $4D$- and $5S$-wave components, respectively. The mass and the strong decay behaviors of the $\Upsilon(11020)$ resonance are consistent with the assignment of the $\Upsilon(6S)$ state in the potential models.Comment: 9 pages, 4 figures. More discussions are adde

Offline-Online Associated Camera-Aware Proxies for Unsupervised Person Re-identification

Recently, unsupervised person re-identification (Re-ID) has received increasing research attention due to its potential for label-free applications. A promising way to address unsupervised Re-ID is clustering-based, which generates pseudo labels by clustering and uses the pseudo labels to train a Re-ID model iteratively. However, most clustering-based methods take each cluster as a pseudo identity class, neglecting the intra-cluster variance mainly caused by the change of cameras. To address this issue, we propose to split each single cluster into multiple proxies according to camera views. The camera-aware proxies explicitly capture local structures within clusters, by which the intra-ID variance and inter-ID similarity can be better tackled. Assisted with the camera-aware proxies, we design two proxy-level contrastive learning losses that are, respectively, based on offline and online association results. The offline association directly associates proxies according to the clustering and splitting results, while the online strategy dynamically associates proxies in terms of up-to-date features to reduce the noise caused by the delayed update of pseudo labels. The combination of two losses enables us to train a desirable Re-ID model. Extensive experiments on three person Re-ID datasets and one vehicle Re-ID dataset show that our proposed approach demonstrates competitive performance with state-of-the-art methods. Code will be available at: https://github.com/Terminator8758/O2CAP.Comment: Accepted to TI