Model-independent traversable wormholes from baryon acoustic oscillations

In this paper, we investigate the model-independent traversable wormholes from baryon acoustic oscillations. Firstly, we place the statistical constraints on the average dark energy equation of state $\omega_{av}$ by only using BAO data. Subsequently, two specific wormhole solutions are obtained, i.e, the cases of the constant redshift function and a special choice for the shape function. For the first case, we analyze the traversabilities of the wormhole configuration, and for the second case, we find that one can construct theoretically a traversable wormhole with infinitesimal amounts of average null energy condition violating phantom fluid. Furthermore, we perform the stability analysis for the first case, and find that the stable equilibrium configurations may increase for increasing values of the throat radius of the wormhole in the cases of a positive and a negative surface energy density. It is worth noting that the obtained wormhole solutions are static and spherically symmetrical metric, and that we assume $\omega_{av}$ to be a constant between different redshifts when placing constraints, hence, these wormhole solutions can be interpreted as stable and static phantom wormholes configurations at some certain redshift which lies in the range [0.32, 2.34].Comment: Minor revision. Published in Physics of the Dark Univers

Rapid Enhancement of Sheared Evershed Flow Along the Neutral Line Associated with an X6.5 Flare Observed by Hinode

We present G-band and Ca II H observations of NOAA AR 10930 obtained by Hinode/SOT on 2006 December 6 covering an X6.5 flare. Local Correlation Tracking (LCT) technique was applied to the foreshortening-corrected G-band image series to acquire horizontal proper motions in this complex beta-gamma-delta active region. With the continuous high quality, spatial and temporal resolution G-band data, we not only confirm the rapid decay of outer penumbrae and darkening of the central structure near the flaring neutral line, but also unambiguously detect for the first time the enhancement of the sheared Evershed flow (average horizontal flow speed increased from 330+-3.1 to 403+-4.6 m/s) along the neutral line right after the eruptive white-light flare. Post-flare Ca II H images indicate that the originally fanning out field lines at the two sides of the neutral line get connected. Since penumbral structure and Evershed flow are closely related to photospheric magnetic inclination or horizontal field strength, we interpret the rapid changes of sunspot structure and surface flow as the result of flare-induced magnetic restructuring down to the photosphere. The magnetic fields turn from fanning out to inward connection causing outer penumbrae decay, meanwhile those near the flaring neutral line become more horizontal leading to stronger Evershed flow there. The inferred enhancement of horizontal magnetic field near the neutral line is consistent with recent magnetic observations and theoretical predictions of flare-invoked photospheric magnetic field change.Comment: 6 pages, 5 figures, accepted by the Astrophysical Journal Letter

Generative Model with Coordinate Metric Learning for Object Recognition Based on 3D Models

Given large amount of real photos for training, Convolutional neural network shows excellent performance on object recognition tasks. However, the process of collecting data is so tedious and the background are also limited which makes it hard to establish a perfect database. In this paper, our generative model trained with synthetic images rendered from 3D models reduces the workload of data collection and limitation of conditions. Our structure is composed of two sub-networks: semantic foreground object reconstruction network based on Bayesian inference and classification network based on multi-triplet cost function for avoiding over-fitting problem on monotone surface and fully utilizing pose information by establishing sphere-like distribution of descriptors in each category which is helpful for recognition on regular photos according to poses, lighting condition, background and category information of rendered images. Firstly, our conjugate structure called generative model with metric learning utilizing additional foreground object channels generated from Bayesian rendering as the joint of two sub-networks. Multi-triplet cost function based on poses for object recognition are used for metric learning which makes it possible training a category classifier purely based on synthetic data. Secondly, we design a coordinate training strategy with the help of adaptive noises acting as corruption on input images to help both sub-networks benefit from each other and avoid inharmonious parameter tuning due to different convergence speed of two sub-networks. Our structure achieves the state of the art accuracy of over 50\% on ShapeNet database with data migration obstacle from synthetic images to real photos. This pipeline makes it applicable to do recognition on real images only based on 3D models.Comment: 14 page