Abundance of moderate-redshift clusters in the Cold + Hot dark matter model

Using a set of \pppm simulation which accurately treats the density evolution of two components of dark matter, we study the evolution of clusters in the Cold + Hot dark matter (CHDM) model. The mass function, the velocity dispersion function and the temperature function of clusters are calculated for four different epochs of $z\le 0.5$. We also use the simulation data to test the Press-Schechter expression of the halo abundance as a function of the velocity dispersion $\sigma_v$. The model predictions are in good agreement with the observational data of local cluster abundances ($z=0$). We also tentatively compare the model with the Gunn and his collaborators' observation of rich clusters at $z\approx 0.8$ and with the x-ray luminous clusters at $z\approx 0.5$ of the {\it Einstein} Extended Medium Sensitivity Survey. The important feature of the model is the rapid formation of clusters in the near past: the abundances of clusters of \sigma_v\ge 700\kms and of \sigma_v\ge 1200 \kms at $z=0.5$ are only 1/4 and 1/10 respectively of the present values ($z=0$). Ongoing ROSAT and AXAF surveys of distant clusters will provide sensitive tests to the model. The abundance of clusters at $z\approx 0.5$ would also be a good discriminator between the CHDM model and a low-density flat CDM model both of which show very similar clustering properties at $z=0$.Comment: 21 pages + 6 figures (uuencoded version of the PS files), Steward Preprints No. 118

Video Saliency Detection by 3D Convolutional Neural Networks

Different from salient object detection methods for still images, a key challenging for video saliency detection is how to extract and combine spatial and temporal features. In this paper, we present a novel and effective approach for salient object detection for video sequences based on 3D convolutional neural networks. First, we design a 3D convolutional network (Conv3DNet) with the input as three video frame to learn the spatiotemporal features for video sequences. Then, we design a 3D deconvolutional network (Deconv3DNet) to combine the spatiotemporal features to predict the final saliency map for video sequences. Experimental results show that the proposed saliency detection model performs better in video saliency prediction compared with the state-of-the-art video saliency detection methods

First Principles Studies on 3-Dimentional Strong Topological Insulators: Bi2Te3, Bi2Se3 and Sb2Te3

Bi2Se3, Bi2Te3 and Sb2Te3 compounds are recently predicted to be 3-dimentional (3D) strong topological insulators. In this paper, based on ab-initio calculations, we study in detail the topological nature and the surface states of this family compounds. The penetration depth and the spin-resolved Fermi surfaces of the surface states will be analyzed. We will also present an procedure, from which highly accurate effective Hamiltonian can be constructed, based on projected atomic Wannier functions (which keep the symmetries of the systems). Such Hamiltonian can be used to study the semi-infinite systems or slab type supercells efficiently. Finally, we discuss the 3D topological phase transition in Sb2(Te1-xSex)3 alloy system.Comment: 8 pages,17 figure