Bar-induced central star formation as revealed by integral field spectroscopy from CALIFA

We investigate the recent star formation history (SFH) in the inner region of 57 nearly face-on spiral galaxies selected from the Calar Alto Legacy Integral Field Area (CALIFA) survey. For each galaxy we use the integral field spectroscopy from CALIFA to obtain two-dimensional maps and radial profiles of three parameters that are sensitive indicators of the recent SFH: the 4000\AA\ break (D$_n$(4000)), and the equivalent width of H$\delta$ absorption (EW(H$\delta_A$)) and H$\alpha$ emission (EW(H$\alpha$)). We have also performed photometric decomposition of bulge/bar/disk components based on SDSS optical image. We identify a class of 17 "turnover" galaxies whose central region present significant drop in D$_n$(4000), and most of them correspondingly show a central upturn in EW(H$\delta_A$) and EW(H$\alpha$). This indicates that the central region of the turnover galaxies has experienced star formation in the past 1-2 Gyr, which makes the bulge younger and more star-forming than surrounding regions. We find almost all (15/17) the turnover galaxies are barred, while only half of the barred galaxies in our sample (15/32) are classified as a turnover galaxy. This finding provides strong evidence in support of the theoretical expectation that the bar may drive gas from the disc inward to trigger star formation in galaxy center, an important channel for the growth/rejuvenation of pseudobulges in disc galaxies.Comment: 19 pages, 10 figures, ApJ accepte

Recurrent 3D Pose Sequence Machines

3D human articulated pose recovery from monocular image sequences is very challenging due to the diverse appearances, viewpoints, occlusions, and also the human 3D pose is inherently ambiguous from the monocular imagery. It is thus critical to exploit rich spatial and temporal long-range dependencies among body joints for accurate 3D pose sequence prediction. Existing approaches usually manually design some elaborate prior terms and human body kinematic constraints for capturing structures, which are often insufficient to exploit all intrinsic structures and not scalable for all scenarios. In contrast, this paper presents a Recurrent 3D Pose Sequence Machine(RPSM) to automatically learn the image-dependent structural constraint and sequence-dependent temporal context by using a multi-stage sequential refinement. At each stage, our RPSM is composed of three modules to predict the 3D pose sequences based on the previously learned 2D pose representations and 3D poses: (i) a 2D pose module extracting the image-dependent pose representations, (ii) a 3D pose recurrent module regressing 3D poses and (iii) a feature adaption module serving as a bridge between module (i) and (ii) to enable the representation transformation from 2D to 3D domain. These three modules are then assembled into a sequential prediction framework to refine the predicted poses with multiple recurrent stages. Extensive evaluations on the Human3.6M dataset and HumanEva-I dataset show that our RPSM outperforms all state-of-the-art approaches for 3D pose estimation.Comment: Published in CVPR 201

Production of high stellar-mass primordial black holes in trapped inflation

Trapped inflation has been proposed to provide a successful inflation with a steep potential. We discuss the formation of primordial black holes in the trapped inflationary scenario. We show that primordial black holes are naturally produced during inflation with a steep trapping potential. In particular, we have given a recipe for an inflaton potential with which particle production can induce large non-Gaussian curvature perturbation that leads to the formation of high stellar-mass primordial black holes. These primordial black holes could be dark matter observed by the LIGO detectors through a binary black-hole merger. At the end, we have given an attempt to realize the required inflaton potential in the axion monodromy inflation, and discussed the gravitational waves sourced by the particle production.Comment: 6 pages, 5 figures, match the version accepted by JHE

Hete-CF : Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations

The work described here was funded by the National Natural Science Foundation of China (NSFC) under Grant No. 61373051; the National Science and Technology Pillar Program (Grant No.2013BAH07F05), the Key Laboratory for Symbolic Computation and Knowledge Engineering, Ministry of Education, China, and the UK Economic & Social Research Council (ESRC); award reference: ES/M001628/1.Preprin