78,824 research outputs found
Thermal entanglement in a two-spin-qutrit system under a nonuniform external magnetic field
The thermal entanglement in a two-spin-qutrit system with two spins coupled
by exchange interaction under a magnetic field in an arbitrary direction is
investigated. Negativity, the measurement of entanglement, is calculated. We
find that for any temperature the evolvement of negativity is symmetric with
respect to magnetic field. The behavior of negativity is presented for four
different cases. The results show that for different temperature, different
magnetic field give maximum entanglement. Both the parallel and antiparallel
magnetic field cases are investigated qualitatively (not quantitatively) in
detail, we find that the entanglement may be enhanced under an antiparallel
magnetic field.Comment: 2 eps figure
Possible S-wave Dibaryons in SU(3) Chiral Quark Model
In the framework of the SU(3) chiral quark model, the wave baryon-baryon
bound states are investigated. It is found that according to the symmetry
character of the system and the contributions from chiral fields, there are
three types of bound states. The states of the first type, such as
and are deeply bound
dibaryon with narrow widths. The second type states, ,,
and are also bound states, but with broad widths.
, , and are third type states. They, like {\em d}, are weakly bound
only if the chiral fields can provide attraction between baryons.Comment: Latex files, 1 figur
Correction of "Cloud Removal By Fusing Multi-Source and Multi-Temporal Images"
Remote sensing images often suffer from cloud cover. Cloud removal is
required in many applications of remote sensing images. Multitemporal-based
methods are popular and effective to cope with thick clouds. This paper
contributes to a summarization and experimental comparation of the existing
multitemporal-based methods. Furthermore, we propose a spatiotemporal-fusion
with poisson-adjustment method to fuse multi-sensor and multi-temporal images
for cloud removal. The experimental results show that the proposed method has
potential to address the problem of accuracy reduction of cloud removal in
multi-temporal images with significant changes.Comment: This is a correction version of the accepted IGARSS 2017 conference
pape
Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition
Recognizing irregular text in natural scene images is challenging due to the
large variance in text appearance, such as curvature, orientation and
distortion. Most existing approaches rely heavily on sophisticated model
designs and/or extra fine-grained annotations, which, to some extent, increase
the difficulty in algorithm implementation and data collection. In this work,
we propose an easy-to-implement strong baseline for irregular scene text
recognition, using off-the-shelf neural network components and only word-level
annotations. It is composed of a -layer ResNet, an LSTM-based
encoder-decoder framework and a 2-dimensional attention module. Despite its
simplicity, the proposed method is robust and achieves state-of-the-art
performance on both regular and irregular scene text recognition benchmarks.
Code is available at: https://tinyurl.com/ShowAttendReadComment: Accepted to Proc. AAAI Conference on Artificial Intelligence 201
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