Nonparametric Independence Screening in Sparse Ultra-High Dimensional Additive Models

A variable screening procedure via correlation learning was proposed Fan and Lv (2008) to reduce dimensionality in sparse ultra-high dimensional models. Even when the true model is linear, the marginal regression can be highly nonlinear. To address this issue, we further extend the correlation learning to marginal nonparametric learning. Our nonparametric independence screening is called NIS, a specific member of the sure independence screening. Several closely related variable screening procedures are proposed. Under the nonparametric additive models, it is shown that under some mild technical conditions, the proposed independence screening methods enjoy a sure screening property. The extent to which the dimensionality can be reduced by independence screening is also explicitly quantified. As a methodological extension, an iterative nonparametric independence screening (INIS) is also proposed to enhance the finite sample performance for fitting sparse additive models. The simulation results and a real data analysis demonstrate that the proposed procedure works well with moderate sample size and large dimension and performs better than competing methods.Comment: 48 page

Estimation of Semileptonic Decays of BcB_c Meson to S-wave Charmonia with NRQCD

We study the semileptonic differential decay rates of $B_c$ meson to S-wave charmonia, $\eta_c$ and $J/\Psi$, at the next-to-leading order accuracy in the framework of NRQCD. In the heavy quark limit, $m_b \to \infty$, we obtain analytically the asymptotic expression for the ratio of NLO form factor to LO form factor. Numerical results show that the convergence of the ratio is perfect. At the maximum recoil region, we analyze the differential decay rates in detail with various input parameters and polarizations of $J/\psi$, which can now be checked in the LHCb experiment. Phenomenologically, the form factors are extrapolated to the minimal recoil region, and then the $B_c$ to charmonium semileptonic decay rates are estimated.Comment: 9 pages, 2 figure and 4 table

Chandra Survey of Nearby Galaxies: The Catalog

We searched in the public archive of the Chandra X-ray Observatory as of March 2016 and assembled a sample of 719 galaxies within 50 Mpc with ACIS observations available. By cross-correlation with the optical or near-infrared nuclei of these galaxies, 314 of them are identified to have an X-ray active galactic nucleus (AGN). The majority of them are low-luminosity AGNs and are unlikely X-ray binaries based upon their spatial distribution and luminosity functions. The AGN fraction is around 60% for elliptical galaxies and early-type spirals, but drops to roughly 20% for Sc and later types, consistent with previous findings in the optical. However, the X-ray survey is more powerful in finding weak AGNs, especially from regions with active star formation that may mask the optical AGN signature. For example, 31% of the H II nuclei are found to harbor an X-ray AGN. For most objects, a single power-law model subject to interstellar absorption is adequate to fit the spectrum, and the typical photon index is found to be around 1.8. For galaxies with a non-detection, their stacked Chandra image shows an X-ray excess with a luminosity of a few times 10^37 erg/s on average around the nuclear region, possibly composed of faint X-ray binaries. This paper reports on the technique and results of the survey; in-depth analysis and discussion of the results will be reported in forthcoming papers.Comment: Accepted for publication in the Astrophysical Journa

Neural network-based arithmetic coding of intra prediction modes in HEVC

In both H.264 and HEVC, context-adaptive binary arithmetic coding (CABAC) is adopted as the entropy coding method. CABAC relies on manually designed binarization processes as well as handcrafted context models, which may restrict the compression efficiency. In this paper, we propose an arithmetic coding strategy by training neural networks, and make preliminary studies on coding of the intra prediction modes in HEVC. Instead of binarization, we propose to directly estimate the probability distribution of the 35 intra prediction modes with the adoption of a multi-level arithmetic codec. Instead of handcrafted context models, we utilize convolutional neural network (CNN) to perform the probability estimation. Simulation results show that our proposed arithmetic coding leads to as high as 9.9% bits saving compared with CABAC.Comment: VCIP 201

Study of the quasi-two-body decays B^{0}_{s} \rightarrow \psi(3770)(\psi(3686))\pi^+\pi^- with perturbative QCD approach

In this note, we study the contributions from the S-wave resonances, f_{0}(980) and f_{0}(1500), to the B^{0}_{s}\rightarrow \psi(3770)\pi^ {+}\pi^{-} decay by introducing the S-wave \pi\pi distribution amplitudes within the framework of the perturbative QCD approach. Both resonant and nonresonant contributions are contained in the scalar form factor in the S-wave distribution amplitude \Phi^S_{\pi\pi}. Since the vector charmonium meson \psi(3770) is a S-D wave mixed state, we calculated the branching ratios of S-wave and D-wave respectively, and the results indicate that f_{0}(980) is the main contribution of the considered decay, and the branching ratio of the \psi(2S) mode is in good agreement with the experimental data. We also take the S-D mixed effect into the B^{0}_{s}\rightarrow \psi(3686)\pi^ {+}\pi^{-} decay. Our calculations show that the branching ratio of B^{0}_{s}\rightarrow \psi(3770)(\psi(3686))\pi^ {+}\pi^{-} can be at the order of 10^{-5}, which can be tested by the running LHC-b experiments.Comment: 10 pages, 3 figure