Expectile Matrix Factorization for Skewed Data Analysis

Matrix factorization is a popular approach to solving matrix estimation problems based on partial observations. Existing matrix factorization is based on least squares and aims to yield a low-rank matrix to interpret the conditional sample means given the observations. However, in many real applications with skewed and extreme data, least squares cannot explain their central tendency or tail distributions, yielding undesired estimates. In this paper, we propose \emph{expectile matrix factorization} by introducing asymmetric least squares, a key concept in expectile regression analysis, into the matrix factorization framework. We propose an efficient algorithm to solve the new problem based on alternating minimization and quadratic programming. We prove that our algorithm converges to a global optimum and exactly recovers the true underlying low-rank matrices when noise is zero. For synthetic data with skewed noise and a real-world dataset containing web service response times, the proposed scheme achieves lower recovery errors than the existing matrix factorization method based on least squares in a wide range of settings.Comment: 8 page main text with 5 page supplementary documents, published in AAAI 201

Long-term X-ray Variability Study of IC342 from XMM-Newton Observations

We presented the results of an analysis of four XMM-Newton observations of the starburst galaxy IC342 taken over a four-year span from 2001 to 2005, with an emphasis on investigating the long-term flux and spectral variability of the X-ray point sources. We detected a total of 61 X-ray sources within 35' $\times$ 30' of the galaxy down to a luminosity of (1-2)$\times$1037 erg s-1 depending on the local background. We found that 39 of the 61 detected sources showed long-term variability, in which 26 of them were classified as X-ray transients. We also found 19 sources exhibiting variations in hardness ratios or undergoing spectral transitions among observations, and were identified as spectral variables. In particular, 8 of the identified X-ray transients showed spectral variability in addition to flux variability. The diverse patterns of variability observed is indicative of a population of X-ray binaries. We used X-ray colors, flux and spectral variability, and in some cases the optical or radio counterparts to classify the detected X-ray sources into several stellar populations. We identified a total of 11 foreground stars, 1 supersoft sources (SSS), 3 quasisoft sources (QSS), and 2 supernova remnants (SNR). The identified SSS/QSS are located near or on the spiral arms, associate with young stellar populations; the 2 SNR are very close to the starburst nucleus where current star formation activities are dominated. We also discovered a spectral change in the nuclear source of IC342 for the first time by a series of X-ray spectrum analysis.Comment: 45 pages, 6 figures accepted by Ap

The Progenitors of Type Ia Supernovae: Are They Supersoft Sources?

In a canonical model, the progenitors of Type Ia supernovae (SNe Ia) are accreting, nuclear-burning white dwarfs (NBWDs), which explode when the white dwarf reaches the Chandrasekhar mass, M_C. Such massive NBWDs are hot (kT ~100 eV), luminous (L ~ 10^{38} erg/s), and are potentially observable as luminous supersoft X-ray sources (SSSs). During the past several years, surveys for soft X-ray sources in external galaxies have been conducted. This paper shows that the results falsify the hypothesis that a large fraction of progenitors are NBWDs which are presently observable as SSSs. The data also place limits on sub-M_C models. While Type Ia supernova progenitors may pass through one or more phases of SSS activity, these phases are far shorter than the time needed to accrete most of the matter that brings them close to M_C.Comment: submitted to ApJ 18 November 2009; 17 pages, 2 figure

Populations of Supersoft X-ray Sources: Novae, tidal disruption, Type Ia supernovae, accretion-induced collapse, ionization, and intermediate-mass black holes?

Observations of hundreds of supersoft x-ray sources (SSSs) in external galaxies have shed light on the diversity of the class and on the natures of the sources. SSSs are linked to the physics of Type Ia supernovae and accretion-induced collapse, ultraluminous x-ray sources and black holes, the ionization of the interstellar medium, and tidal disruption by supermassive black holes. The class of SSSs has an extension to higher luminosities: ultraluminous SSSs have luminosities above 10^39 erg/s. There is also an extension to higher energies: quasisoft x-ray sources (QSSs) emit photons with energies above 1 eV, but few or none with energies above 2 keV. Finally, a significant fraction of the SSSs found in external galaxies switch states between observations, becoming either quasisoft or hard. For many systems ``supersoft'' refers to a temporary state; SSSs are sources, possibly including a variety of fundamentally different system types, that pass through such a state. We review those results derived from extragalactic data and related theoretical work that are most surprising and that suggest directions for future research.Comment: submitted to Astron.Nachr.; latex, 6 figure

Growing Story Forest Online from Massive Breaking News

We describe our experience of implementing a news content organization system at Tencent that discovers events from vast streams of breaking news and evolves news story structures in an online fashion. Our real-world system has distinct requirements in contrast to previous studies on topic detection and tracking (TDT) and event timeline or graph generation, in that we 1) need to accurately and quickly extract distinguishable events from massive streams of long text documents that cover diverse topics and contain highly redundant information, and 2) must develop the structures of event stories in an online manner, without repeatedly restructuring previously formed stories, in order to guarantee a consistent user viewing experience. In solving these challenges, we propose Story Forest, a set of online schemes that automatically clusters streaming documents into events, while connecting related events in growing trees to tell evolving stories. We conducted extensive evaluation based on 60 GB of real-world Chinese news data, although our ideas are not language-dependent and can easily be extended to other languages, through detailed pilot user experience studies. The results demonstrate the superior capability of Story Forest to accurately identify events and organize news text into a logical structure that is appealing to human readers, compared to multiple existing algorithm frameworks.Comment: Accepted by CIKM 2017, 9 page

The Discovery of Quasisoft and Supersoft Sources in External Galaxies

We apply a uniform procedure to select very soft sources from point sources observed by Chandra in 4 galaxies. This sample includes one elliptical galaxy (NGC 4967), 2 face-on spirals (M101 and M83), and an interacting galaxy (M51). We have found very soft X-ray sources (VSSs) in every galaxy. Some of these fit the criteria for canonical supersoft sources (SSSs), while others are somewhat harder. These latter have characteristic values of kT < 300 eV; we refer to them as quasisoft sources (QSSs). We found a combined total of 149 VSSs in the 4 galaxies we considered; 77 were SSSs and 72 were QSSs. (See the paper for the original long abstract)Comment: 20 pages, 6 figures. Accepted for publication in Ap