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