A universal average spectral energy distribution for quasars from optical to extreme ultraviolet

The well-known anti-correlation between the optical/ultraviolet (UV) emission line equivalent widths of active galactic nuclei and the continuum luminosity (the so-called Baldwin effect) is a long-standing puzzle. One common hypothesis is that more luminous sources have softer spectral energy distribution (SED) in the extreme UV (EUV), as revealed by some observational studies. In this work we revisit this issue through cross-matching SDSS quasars with GALEX far-UV/near-UV catalogs and correcting the effect of a severe observational bias of significant UV detection incompleteness, i.e., the more luminous in observed-frame optical, the more likely detected in observed-frame UV. We find that, for GALEX detected quasars at 1.8 < z < 2.2, the rest-frame mean UV SED (~ 500 -- 3000 Angstrom) bewilderingly shows no luminosity dependence at log(\nu L_\nu(2200 Angstrom)) > 45 (up to 47.3), contrary to the standard thin disc model predictions and the observed Baldwin effect in this luminosity range. Probably, the universal mean UV SED is the result of a local atomic-originated process, and in fainter quasars stronger disk turbulence launching more clouds is the main origin of the Baldwin effect. After correcting for the absorption of the intergalactic medium, a rest-frame intrinsic mean EUV SED is derived from a sub-sample of bright quasars and is found to be much redder in the EUV than all previous quasar composite spectra, highlighting the significance of properly accounting for the sample incompleteness. Interestingly, the global consistence between our extremely red mean EUV SED and the line-driven wind model again supports an origin of a local physical process.Comment: 27 pages, 15 figures, author's initial version submitted to Nature Astronom

Decision Model for COTS Component Procurement Based on Case-based Retrieval and Goal Programming

Compared with traditional information system development methodology, COTS-based information system has the following advantages: Avoid expensive development and maintenance; frequent upgrades often anticipate organization’s need; rich functionality; mature technologies; tracks technology trends, etc. However, how to select appropriate COTS components is a complex problem. For improving the accuracy of decision-making in COTS component procurement, a two-period model is put forward. In the first period, the procurement requirement of each COTS component is compared with a COTS component case base by case-based retrieval (CBR) and the initial candidates are selected. In the second period, a (0-1) integer goal programming model is created to optimize cost and time of the whole COTS-based system, and help decision makers to decide the final candidates. Case shows that the two-period method declines the complexity of computation and increases the rationality of decisio

Cold Dark Matter Isocurvature Perturbations: Cosmological Constraints and Applications

In this paper we present the constraints on cold dark matter (CDM) isocurvature contributions to the cosmological perturbations. By employing Markov Chain Monte Carlo method (MCMC), we perform a global analysis for cosmological parameters using the latest astronomical data, such as 7-year Wilkinson Microwave Anisotropy Probe (WMAP7) observations, matter power spectrum from the Sloan Digital Sky Survey (SDSS) luminous red galaxies (LRG), and "Union2" type Ia Supernovae (SNIa) sample. We find that the correlated mixture of adiabatic and isocurvature modes are mildly better fitting to the current data than the pure adiabatic ones, with the minimal $\chi^2$ given by the likelihood analysis being reduced by 3.5. We also obtain a tight limit on the fraction of the CDM isocurvature contributions, which should be less than 14.6% at 95% confidence level. With the presence of the isocurvature modes, the adiabatic spectral index becomes slightly bigger, n_s^{\rm adi}=0.972\pm0.014~(1\,\sigma), and the tilt for isocurvature spectrum could be large, namely, the best fit value is n_s^{\rm iso}=3.020. Finally, we discuss the effect on WMAP normalization priors, shift parameter R, acoustic scale l_A and z_{*}, from the CDM isocurvaure perturbation. By fitting the mixed initial condition to the combined data, we find the mean values of R, l_A and z_{*} can be changed about 2.9\sigma, 2.8\sigma and 1.5\sigma respectively, comparing with those obtained in the pure adiabatic condition.Comment: 9 pages, 5 figures, 3 tables, references adde

(4-Bromo-2-{[2-(morpholin-4-yl)ethyl­imino]­meth­yl}phenolato)dioxido­vanadium(V)

In the title mononuclear dioxidovanadium(V) complex, [V(C13H16BrN2O2)O2], the VV atom is five-coordinated by one phenolate O, one imine N and one morpholine N atom of the Schiff base ligand, and by two oxide O atoms, forming a distorted square-pyramidal geometry. In the crystal, weak C—H⋯O inter­actions and a short Br⋯Br contact [3.4597 (12) Å] are observed

GTC: Guided Training of CTC Towards Efficient and Accurate Scene Text Recognition

Connectionist Temporal Classification (CTC) and attention mechanism are two main approaches used in recent scene text recognition works. Compared with attention-based methods, CTC decoder has a much shorter inference time, yet a lower accuracy. To design an efficient and effective model, we propose the guided training of CTC (GTC), where CTC model learns a better alignment and feature representations from a more powerful attentional guidance. With the benefit of guided training, CTC model achieves robust and accurate prediction for both regular and irregular scene text while maintaining a fast inference speed. Moreover, to further leverage the potential of CTC decoder, a graph convolutional network (GCN) is proposed to learn the local correlations of extracted features. Extensive experiments on standard benchmarks demonstrate that our end-to-end model achieves a new state-of-the-art for regular and irregular scene text recognition and needs 6 times shorter inference time than attentionbased methods.Comment: Accepted by AAAI 202