The electromagnetic and gravitational-wave radiations of X-ray transient CDF-S XT2

Binary neutron star (NS) mergers may result in remnants of supra-massive or even stable NS, which have been supported indirectly by observed X-ray plateau of some gamma-ray bursts (GRBs) afterglow. Recently, Xue et al. (2019) discovered a X-ray transient CDF-S XT2 that is powered by a magnetar from merger of double NS via X-ray plateau and following stepper phase. However, the decay slope after the plateau emission is a little bit larger than the theoretical value of spin-down in electromagnetic (EM) dominated by losing its rotation energy. In this paper, we assume that the feature of X-ray emission is caused by a supra-massive magnetar central engine for surviving thousands of seconds to collapse black hole. Within this scenario, we present the comparisons of the X-ray plateau luminosity, break time, and the parameters of magnetar between CDF-S XT2 and other short GRBs with internal plateau samples. By adopting the collapse time to constrain the equation of state (EOS), we find that three EOSs (GM1, DD2, and DDME2) are consistent with the observational data. On the other hand, if the most released rotation energy of magnetar is dominated by GW radiation, we also constrain the upper limit of ellipticity of NS for given EOS, and it is range in $[0.32-1.3]\times 10^{-3}$. Its GW signal can not be detected by aLIGO or even for more sensitive Einstein Telescope in the future.Comment: 13 pages, 5 figures,1 table. Accepted for publication by Research in Astronomy and Astrophysic

Observation on the effect of 200mL/L alcohol pretreatment on the pterygium operation

AIM: To observe the effect and clinical significance of alcohol pretreatment during the pterygium surgery.<p>METHODS: Totally 300 eyes with pterygium were randomly divided into two groups. Control group: 142 eyes with pterygium were peeled under local anethesia and their degenerative organization of pterygium was cleaned up followed by a transplantation of corneal limbus with an autologous conjunctival flap. Experimental group: 158 eyes with pterygium were placed with a special metal ring used in LASEK on the head of pterygium to isolate the treatment area under local anesthesis, then, the treatment area within the ring was filled with the alcohol with a concentration of 200mL/L for 40-60s, followed by an adequate flushing with saline. Subsequent surgical procedure was the same as control group. <p>RESULTS: Follow-up for all patients ranged from 1 month to 3 years. Postoperatively, 158 eyes of experimental group had better operative effect than control group. Experimental group had better would healing, complete tissue construction, and improved visual quality. The break-up time of tear film for experimental group was significantly prolonged than that for control group. The average corneal astigmatism and total higher-order aberrations of experimental group were significantly lower than that of control group. The recurrence rate of experimental group was also significantly lower than control group. There's no significant difference in the incidence of complications.<p>CONCLUSION: Alcohol pretreatment during the pterygium surgery is a safe and effective method

Disentangled Contrastive Image Translation for Nighttime Surveillance

Nighttime surveillance suffers from degradation due to poor illumination and arduous human annotations. It is challengable and remains a security risk at night. Existing methods rely on multi-spectral images to perceive objects in the dark, which are troubled by low resolution and color absence. We argue that the ultimate solution for nighttime surveillance is night-to-day translation, or Night2Day, which aims to translate a surveillance scene from nighttime to the daytime while maintaining semantic consistency. To achieve this, this paper presents a Disentangled Contrastive (DiCo) learning method. Specifically, to address the poor and complex illumination in the nighttime scenes, we propose a learnable physical prior, i.e., the color invariant, which provides a stable perception of a highly dynamic night environment and can be incorporated into the learning pipeline of neural networks. Targeting the surveillance scenes, we develop a disentangled representation, which is an auxiliary pretext task that separates surveillance scenes into the foreground and background with contrastive learning. Such a strategy can extract the semantics without supervision and boost our model to achieve instance-aware translation. Finally, we incorporate all the modules above into generative adversarial networks and achieve high-fidelity translation. This paper also contributes a new surveillance dataset called NightSuR. It includes six scenes to support the study on nighttime surveillance. This dataset collects nighttime images with different properties of nighttime environments, such as flare and extreme darkness. Extensive experiments demonstrate that our method outperforms existing works significantly. The dataset and source code will be released on GitHub soon.Comment: Submitted to TI