Evidence for Photoionization Driven Broad Absorption Line Variability

We present a qualitative analysis of the variability of quasar broad absorption lines using the large multi-epoch spectroscopic dataset of the Sloan Digital Sky Survey Data Release 10. We confirm that variations of absorption lines are highly coordinated among different components of the same ion or the same absorption component of different ions for C IV, Si IV and N V. Furthermore, we show that the equivalent widths of the lines decrease or increase statistically when the continuum brightens or dims. This is further supported by the synchronized variations of emission and absorption line equivalent width, when the well established intrinsic Baldwin effect for emission lines is taken into account. We find that the emergence of an absorption component is usually accompanying with dimming of the continuum while the disappearance of an absorption line component with brightening of the continuum. This suggests that the emergence or disappearance of a C IV absorption component is only the extreme case, when the ionic column density is very sensitive to continuum variations or the continuum variability amplitude is larger. These results support the idea that absorption line variability is driven mainly by changes in the gas ionization in response to continuum variations, that the line-absorbing gas is highly ionized, and in some extreme cases, too highly ionized to be detected in UV absorption lines. Due to uncertainties in the spectroscopic flux calibration, we cannot quantify the fraction of quasars with asynchronized continuum and absorption line variations.Comment: 41 pages, 15 figures, accepted to Ap

CNN Based 3D Facial Expression Recognition Using Masking And Landmark Features

Automatically recognizing facial expression is an important part for human-machine interaction. In this paper, we first review the previous studies on both 2D and 3D facial expression recognition, and then summarize the key research questions to solve in the future. Finally, we propose a 3D facial expression recognition (FER) algorithm using convolutional neural networks (CNNs) and landmark features/masks, which is invariant to pose and illumination variations due to the solely use of 3D geometric facial models without any texture information. The proposed method has been tested on two public 3D facial expression databases: BU-4DFE and BU-3DFE. The results show that the CNN model benefits from the masking, and the combination of landmark and CNN features can further improve the 3D FER accuracy

Reconstructing the Initial Density Field of the Local Universe: Method and Test with Mock Catalogs

Our research objective in this paper is to reconstruct an initial linear density field, which follows the multivariate Gaussian distribution with variances given by the linear power spectrum of the current CDM model and evolves through gravitational instability to the present-day density field in the local Universe. For this purpose, we develop a Hamiltonian Markov Chain Monte Carlo method to obtain the linear density field from a posterior probability function that consists of two components: a prior of a Gaussian density field with a given linear spectrum, and a likelihood term that is given by the current density field. The present-day density field can be reconstructed from galaxy groups using the method developed in Wang et al. (2009a). Using a realistic mock SDSS DR7, obtained by populating dark matter haloes in the Millennium simulation with galaxies, we show that our method can effectively and accurately recover both the amplitudes and phases of the initial, linear density field. To examine the accuracy of our method, we use $N$-body simulations to evolve these reconstructed initial conditions to the present day. The resimulated density field thus obtained accurately matches the original density field of the Millennium simulation in the density range 0.3 <= rho/rho_mean <= 20 without any significant bias. Especially, the Fourier phases of the resimulated density fields are tightly correlated with those of the original simulation down to a scale corresponding to a wavenumber of ~ 1 h/Mpc, much smaller than the translinear scale, which corresponds to a wavenumber of ~ 0.15 h\Mpc.Comment: 43 pages, 15 figures, accepted for publication in Ap

Set Operation Aided Network For Action Units Detection

As a large number of parameters exist in deep model-based methods, training such models usually requires many fully AU-annotated facial images. This is true with regard to the number of frames in two widely used datasets: BP4D [31] and DISFA [18], while those frames were captured from a small number of subjects (41, 27 respectively). This is problematic, as subjects produce highly consistent facial muscle movements, adding more frames per subject would only adds more close points in the feature space, and thus the classifier does not benefit from those extra frames. Data augmentation methods can be applied to alleviate the problem to a certain degree, but they fail to augment new subjects. We propose a novel Set Operation Aided Network (SO-Net) for action units\u27 detection. Specifically, new features and the corresponding labels are generated by adding set operations to both the feature and label spaces. The generated new features can be treated as a representation of a hypothetical image. As a result, we can implicitly obtain training examples beyond what was originally observed in the dataset. Therefore, the deep model is forced to learn subject-independent features and is generalizable to unseen subjects. SO-Net is end-to-end trainable and can be flexibly plugged in any CNN model during training. We evaluate the proposed method on two public datasets, BP4D and DISFA. The experiment shows a state-of-the-art performance, demonstrating the effectiveness of the proposed method