98,954 research outputs found
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 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 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
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