The possible members of the 51S05^1S_0 meson nonet

The strong decays of the $5^1S_0$ $q\bar{q}$ states are evaluated in the $^3P_0$ model with two types of space wave functions. Comparing the model expectations with the experimental data for the $\pi(2360)$, $\eta(2320)$, $X(2370)$, and $X(2500)$, we suggest that the $\pi(2360)$, $\eta(2320)$, and $X(2500)$ can be assigned as the members of the $5^1S_0$ meson nonet, while the $5^1S_0$ assignment for the $X(2370)$ is not favored by its width. The $5^1S_0$ kaon is predicted to have a mass of about 2418 MeV and a width of about 163 MeV or 225 MeV.Comment: 10 pages, 5 figures, version accepted by Eur. Phys. J.

Experimental high-intensity three-photon entangled source

We experimentally realize a high-intensity three-photon Greenberger-Horne-Zeilinger (GHZ) entanglement source directly following the proposal by Rarity and Tapster [J. G. Rarity and P. R. Tapster, Phys. Rev. A 59, R35 (1999)]. The threefold coincidence rate can be more than 200 Hz with a fidelity of 0.811, and the intensity can be further improved with moderate fidelity degradation. The GHZ entanglement is characterized by testing the Bell-Mermin inequality and using an entanglement witness operator. To optimize the polarization-entangled source, we theoretically analyze the relationship between the mean photon number of the single-photon source and the probability of parametric down-conversion.Comment: 4 pages, 4 figure

Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation

While representation learning aims to derive interpretable features for describing visual data, representation disentanglement further results in such features so that particular image attributes can be identified and manipulated. However, one cannot easily address this task without observing ground truth annotation for the training data. To address this problem, we propose a novel deep learning model of Cross-Domain Representation Disentangler (CDRD). By observing fully annotated source-domain data and unlabeled target-domain data of interest, our model bridges the information across data domains and transfers the attribute information accordingly. Thus, cross-domain joint feature disentanglement and adaptation can be jointly performed. In the experiments, we provide qualitative results to verify our disentanglement capability. Moreover, we further confirm that our model can be applied for solving classification tasks of unsupervised domain adaptation, and performs favorably against state-of-the-art image disentanglement and translation methods.Comment: CVPR 2018 Spotligh

Correlation of Plasma MMP-1 and TIMP-1 Levels and the Colonic Mucosa Expressions in Patients with Ulcerative Colitis

Background. Both plasma and mucosal levels of matrix metalloproteinase-1 (MMP-1) and tissue inhibitor of metalloproteinase-1 (TIMP-1) have been shown to be independently correlated with ulcerative colitis (UC), but their relationship with each other and to disease severity remains unclear. This study aims to evaluate the relationship between colonic mucosal and plasma levels of MMP-1 and TIMP-1 with each other and with the severity of ulcerative colitis (UC). Methods. Colonic mucosal lesions and venous blood samples were collected from 30 patients with UC and 15 normal subjects. Real-time reverse transcription-PCR and immunohistochemistry were used to determine colonic mucosal MMP-1 and TIMP-1 expression; ELISA was used to measure plasma levels of MMP-1 and TIMP-1. Results. Expression of colonic mucosal and plasma MMP-1 and TIMP-1 in patients with UC was significantly higher than that of controls (P < .05), and was positively correlated with disease severity (P < .05). Plasma MMP-1 and TIMP-1 levels were well correlated with their corresponding expression in colonic mucosa (P < .05, r = 0.805 and 0.908). Conclusion. Plasma MMP-1 and TIMP-1 levels reflect their colonic mucosal expression to some extent in patients with UC. Plasma MMP-1 and TIMP-1, in particular, demonstrate the potential to become biomarkers to clinically diagnose UC, predict its severity, and guide further therapy