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 $S-$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 $[\Omega\Omega]_{(0,0)}$ and $[\Xi^{*}\Omega]_{(0,1/2)}$ are deeply bound dibaryon with narrow widths. The second type states, $[\Sigma^{*} \Delta]_{(0,5/2)}$,$[\Sigma^{*} \Delta]_{(3,1/2)}$, $[\Delta\Delta]_{(0,3)}$ and $[\Delta\Delta]_{(3,0)}$ are also bound states, but with broad widths. $[\Xi\Omega - \Xi^{*}\Omega]_{(1,1/2)}$, $[\Xi\Xi]_{(0,1)}$, and $[N \Omega]_{(2,1/2)}$ 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 $31$-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