Outflow and hot dust emission in high redshift quasars

Correlations of hot dust emission with outflow properties are investigated, based on a large z~2 non-broad absorption lines quasar sample built from the Wide-field Infrared Survey and the Sloan Digital Sky Survey data releases. We use the near infrared slope and the infrared to UV luminosity ratio to indicate the hot dust emission relative to the emission from the accretion disk. In our luminous quasars, these hot dust emission indicators are almost independent of the fundamental parameters, such as luminosity, Eddington ratio and black hole mass, but moderately dependent on the blueshift and asymmetry index (BAI) and full width at half-maximum (FWHM) of CIV lines. Interestingly, the latter two correlations dramatically strengthen with increasing Eddington ratio. We suggest that, in high Eddington ratio quasars, CIV regions are dominated by outflows so the BAI and FWHM(CIV) can reliably reflect the general properties and velocity of outflows, respectively. While in low Eddington ratio quasars, CIV lines are primarily emitted by virialized gas so the BAI and FWHM(CIV) become less sensitive to outflows. Therefore, the correlations for the highest Eddington ratio quasars are more likely to represent the true dependence of hot dust emission on outflows and the correlations for the entire sample are significantly diluted by the low Eddington ratio quasars. Our results show that an outflow with a large BAI or velocity can double the hot dust emission on average. We suggest that outflows either contain hot dust in themselves or interact with the dusty interstellar medium or torus.Comment: 14 page, 4 figures, accepted for publication in ApJ

Outflow and hot dust emission in broad absorption line quasars

We have investigated a sample of 2099 broad absorption line (BAL) quasars with z=1.7-2.2 built from the Sloan Digital Sky Survey Data Release Seven and the Wide-field Infrared Survey. This sample is collected from two BAL quasar samples in the literature, and refined by our new algorithm. Correlations of outflow velocity and strength with hot dust indicator (beta_NIR) and other quasar physical parameters, such as Eddington ratio, luminosity and UV continuum slope, are explored in order to figure out which parameters drive outflows. Here beta_NIR is the near-infrared continuum slope, a good indicator of the amount of hot dust emission relative to accretion disk emission. We confirm previous findings that outflow properties moderately or weakly depends on Eddington ratio, UV slope and luminosity. For the first time, we report moderate and significant correlations of outflow strength and velocity with beta_NIR in BAL quasars. It is consistent with the behavior of blueshifted broad emission lines in non-BAL quasars. The statistical analysis and composite spectra study both reveal that outflow strength and velocity are more strongly correlated with beta_NIR than Eddington ratio, luminosity and UV slope. In particular, the composites show that the entire C IV absorption profile shifts blueward and broadens as beta_NIR increases, while Eddington ratio and UV slope only affect the high and low velocity part of outflows, respectively. We discuss several potential processes and suggest that dusty outflow scenario, i.e. dust is intrinsic to outflows and may contribute to the outflow acceleration, is most likely. The BAL quasar catalog is available from the authors upon request.Comment: 16 pages, 10 figures, 2 tables; Accepted for publication in The Astrophysical Journa

Identity-adaptive Facial Expression Recognition Through Expression Regeneration Using Conditional Generative Adversarial Networks

Subject variation is a challenging issue for facial expression recognition, especially when handling unseen subjects with small-scale labeled facial expression databases. Although transfer learning has been widely used to tackle the problem, the performance degrades on new data. In this paper, we present a novel approach (so-called IA-gen) to alleviate the issue of subject variations by regenerating expressions from any input facial images. First of all, we train conditional generative models to generate six prototypic facial expressions from any given query face image while keeping the identity related information unchanged. Generative Adversarial Networks are employed to train the conditional generative models, and each of them is designed to generate one of the prototypic facial expression images. Second, a regular CNN (FER-Net) is fine- tuned for expression classification. After the corresponding prototypic facial expressions are regenerated from each facial image, we output the last FC layer of FER-Net as features for both the input image and the generated images. Based on the minimum distance between the input image and the generated expression images in the feature space, the input image is classified as one of the prototypic expressions consequently. Our proposed method can not only alleviate the influence of inter-subject variations but will also be flexible enough to integrate with any other FER CNNs for person-independent facial expression recognition. Our method has been evaluated on CK+, Oulu-CASIA, BU-3DFE and BU-4DFE databases, and the results demonstrate the effectiveness of our proposed method

CARE: Large Precision Matrix Estimation for Compositional Data

High-dimensional compositional data are prevalent in many applications. The simplex constraint poses intrinsic challenges to inferring the conditional dependence relationships among the components forming a composition, as encoded by a large precision matrix. We introduce a precise specification of the compositional precision matrix and relate it to its basis counterpart, which is shown to be asymptotically identifiable under suitable sparsity assumptions. By exploiting this connection, we propose a composition adaptive regularized estimation (CARE) method for estimating the sparse basis precision matrix. We derive rates of convergence for the estimator and provide theoretical guarantees on support recovery and data-driven parameter tuning. Our theory reveals an intriguing trade-off between identification and estimation, thereby highlighting the blessing of dimensionality in compositional data analysis. In particular, in sufficiently high dimensions, the CARE estimator achieves minimax optimality and performs as well as if the basis were observed. We further discuss how our framework can be extended to handle data containing zeros, including sampling zeros and structural zeros. The advantages of CARE over existing methods are illustrated by simulation studies and an application to inferring microbial ecological networks in the human gut.Comment: 67 pages, 7 figure

Ball Prolate Spheroidal Wave Functions In Arbitrary Dimensions

In this paper, we introduce the prolate spheroidal wave functions (PSWFs) of real order $\alpha>-1$ on the unit ball in arbitrary dimension, termed as ball PSWFs. They are eigenfunctions of both a weighted concentration integral operator, and a Sturm-Liouville differential operator. Different from existing works on multi-dimensional PSWFs, the ball PSWFs are defined as a generalisation of orthogonal {\em ball polynomials} in primitive variables with a tuning parameter $c>0$, through a "perturbation" of the Sturm-Liouville equation of the ball polynomials. From this perspective, we can explore some interesting intrinsic connections between the ball PSWFs and the finite Fourier and Hankel transforms. We provide an efficient and accurate algorithm for computing the ball PSWFs and the associated eigenvalues, and present various numerical results to illustrate the efficiency of the method. Under this uniform framework, we can recover the existing PSWFs by suitable variable substitutions.Comment: 25 page

Alignments of galaxies within cosmic filaments from SDSS DR7

Using a sample of galaxy groups selected from the Sloan Digital Sky Survey Data Release 7 (SDSS DR7), we examine the alignment between the orientation of galaxies and their surrounding large scale structure in the context of the cosmic web. The latter is quantified using the large-scale tidal field, reconstructed from the data using galaxy groups above a certain mass threshold. We find that the major axes of galaxies in filaments tend to be preferentially aligned with the directions of the filaments, while galaxies in sheets have their major axes preferentially aligned parallel to the plane of the sheets. The strength of this alignment signal is strongest for red, central galaxies, and in good agreement with that of dark matter halos in N-body simulations. This suggests that red, central galaxies are well aligned with their host halos, in quantitative agreement with previous studies based on the spatial distribution of satellite galaxies. There is a luminosity and mass dependence that brighter and more massive galaxies in filaments and sheets have stronger alignment signals. We also find that the orientation of galaxies is aligned with the eigenvector associated with the smallest eigenvalue of the tidal tensor. These observational results indicate that galaxy formation is affected by large-scale environments, and strongly suggests that galaxies are aligned with each other over scales comparable to those of sheets and filaments in the cosmic web.Comment: 11 pages, 10 figures, accepted for publication in Ap