36,274 research outputs found
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 . We also use the simulation data to test
the Press-Schechter expression of the halo abundance as a function of the
velocity dispersion . The model predictions are in good agreement
with the observational data of local cluster abundances (). We also
tentatively compare the model with the Gunn and his collaborators' observation
of rich clusters at and with the x-ray luminous clusters at
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 are only 1/4 and 1/10 respectively of the present values
(). Ongoing ROSAT and AXAF surveys of distant clusters will provide
sensitive tests to the model. The abundance of clusters at 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 .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
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