32,699 research outputs found
The X-ray Nature of Nucleus in Seyfert 2 Galaxy NGC 7590
We present the result of the Chandra high-resolution observation of the
Seyfert~2 galaxy NGC 7590. This object was reported to show no X-ray absorption
in the low-spatial resolution ASCA data. The XMM observations show that the
X-ray emission of NGC 7590 is dominated by an off-nuclear ultra-luminous X-ray
source (ULX) and an extended emission from the host galaxy, and the nucleus is
rather weak, likely hosting a Compton-thick AGN. Our recent Chandra observation
of NGC 7590 enables to remove the X-ray contamination from the ULX and the
extended component effectively. The nuclear source remains undetected at
~4x10^{-15} erg/s/cm^-2 flux level. Although not detected, Chandra data gives a
2--10 keV flux upper limit of ~6.1x10^{-15} erg/s/cm^-2 (at 3 sigma level), a
factor of 3 less than the XMM value, strongly supporting the Compton-thick
nature of the nucleus. In addition, we detected five off-nuclear X-ray point
sources within the galaxy D25 ellipse, all with 2 -- 10 keV luminosity above
2x10^{38} erg/s (assuming the distance of NGC 7590). Particularly, the ULX
previously identified by ROSAT data was resolved by Chandra into two distinct
X-ray sources. Our analysis highlights the importance of high spatial
resolution images in discovering and studying ULXs.Comment: 8 pages, 5 figures, RAA accepte
Lifelong Learning CRF for Supervised Aspect Extraction
This paper makes a focused contribution to supervised aspect extraction. It
shows that if the system has performed aspect extraction from many past domains
and retained their results as knowledge, Conditional Random Fields (CRF) can
leverage this knowledge in a lifelong learning manner to extract in a new
domain markedly better than the traditional CRF without using this prior
knowledge. The key innovation is that even after CRF training, the model can
still improve its extraction with experiences in its applications.Comment: Accepted at ACL 2017. arXiv admin note: text overlap with
arXiv:1612.0794
DOC: Deep Open Classification of Text Documents
Traditional supervised learning makes the closed-world assumption that the
classes appeared in the test data must have appeared in training. This also
applies to text learning or text classification. As learning is used
increasingly in dynamic open environments where some new/test documents may not
belong to any of the training classes, identifying these novel documents during
classification presents an important problem. This problem is called open-world
classification or open classification. This paper proposes a novel deep
learning based approach. It outperforms existing state-of-the-art techniques
dramatically.Comment: accepted at EMNLP 201
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