M-estimation in high-dimensional linear model

We mainly study the M-estimation method for the high-dimensional linear regression model, and discuss the properties of M-estimator when the penalty term is the local linear approximation. In fact, M-estimation method is a framework, which covers the methods of the least absolute deviation, the quantile regression, least squares regression and Huber regression. We show that the proposed estimator possesses the good properties by applying certain assumptions. In the part of numerical simulation, we select the appropriate algorithm to show the good robustness of this methodComment: 16 pages,3 table

Can Up FCNC solve the ΔACP\Delta A_{CP} puzzle?

We investigate the attempt using flavor violation gauge interaction in the up sector to explain the LHCb recently observed large $\Delta A_{CP}$ ($A_{CP}(D^{0}\to K^{+}K^{-})-A_{CP}(D^{0}\to \pi^{+}\pi^{-})$). We study an Abelian model that only right-handed up quarks is charged under it and the 1-3 coupling is maximized. The simultaneous 1-3 2-3 mixing is realized by a quark mixing of 1-2 generation. Given the easy identification of top quark, the model can be directly tested by $\Delta F=1$ and $\Delta F=2$ processes at the hadron colliders as associated top production $g c \to t Z^{\prime}$ or same-sign top scattering $u u\to t t$. The direct search bounds are still consistent with the assumption that $ut$ and $ct$ couplings are equal but the same-sign top scattering bound is expected to be reached very soon. However, since there is no CKM-like suppression, the corresponding parameter space for generating $\Delta A_{CP}$ is completely excluded by the $D^{0}-\bar{D}^{0}$ mixing. We conclude that the up FCNC type models cannot explain the $\Delta A_{CP}$ while to be consistent with the $D^{0}-\bar{D}^{0}$ mixing constraint at the same time. On the other hand, a model as SM with fourth family extension has better chance to explain the large $\Delta A_{CP}$ consistently.Comment: 10 pages, 2 figure

A Robust Information Source Estimator with Sparse Observations

In this paper, we consider the problem of locating the information source with sparse observations. We assume that a piece of information spreads in a network following a heterogeneous susceptible-infected-recovered (SIR) model and that a small subset of infected nodes are reported, from which we need to find the source of the information. We adopt the sample path based estimator developed in [1], and prove that on infinite trees, the sample path based estimator is a Jordan infection center with respect to the set of observed infected nodes. In other words, the sample path based estimator minimizes the maximum distance to observed infected nodes. We further prove that the distance between the estimator and the actual source is upper bounded by a constant independent of the number of infected nodes with a high probability on infinite trees. Our simulations on tree networks and real world networks show that the sample path based estimator is closer to the actual source than several other algorithms

A unification of RDE model and XCDM model

In this Letter, we propose a new generalized Ricci dark energy (NGR) model to unify Ricci dark energy (RDE) and XCDM. Our model can distinguish between RDE and XCDM by introducing a parameter $\beta$ called weight factor. When $\beta=1$, NGR model becomes the usual RDE model. The XCDM model is corresponding to $\beta=0$. Moreover, NGR model permits the situation where neither $\beta=1$ nor $\beta=0$. We then perform a statefinder analysis on NGR model to see how $\beta$ effects the trajectory on the $r-s$ plane. In order to know the value of $\beta$, we constrain NGR model with latest observations including type Ia supernovae (SNe Ia) from Union2 set (557 data), baryonic acoustic oscillation (BAO) observation from the spectroscopic Sloan Digital Sky Survey (SDSS) data release 7 (DR7) galaxy sample and cosmic microwave background (CMB) observation from the 7-year Wilkinson Microwave Anisotropy Probe (WMAP7) results. With Markov Chain Monte Carlo (MCMC) method, the constraint result is $\beta$=$0.08_{-0.21}^{+0.30}(1\sigma)_{-0.28}^{+0.43}(2\sigma)$, which manifests the observations prefer a XCDM universe rather than RDE model. It seems RDE model is ruled out in NGR scenario within $2\sigma$ regions. Furthermore, we compare it with some of successful cosmological models using AIC information criterion. NGR model seems to be a good choice for describing the universe.Comment: 12 pages, 7 figures, 2 tables. Accepted for publication in PL

Constraints on f(R) cosmologies from strong gravitational lensing systems

f(R) gravity is thought to be an alternative to dark energy which can explain the acceleration of the universe. It has been tested by different observations including type Ia supernovae (SNIa), the cosmic microwave background (CMB), the baryon acoustic oscillations (BAO) and so on. In this Letter, we use the Hubble constant independent ratio between two angular diameter distances $D=D_{ls}/D_s$ to constrain f(R) model in Palatini approach $f(R)=R-\alpha H^2_0(-\frac{R}{H^2_0})^\beta$. These data are from various large systematic lensing surveys and lensing by galaxy clusters combined with X-ray observations. We also combine the lensing data with CMB and BAO, which gives a stringent constraint. The best-fit results are $(\alpha,\beta)=(-1.50,0.696)$ or $(\Omega_m,\beta)=(0.0734,0.696)$ using lensing data only. When combined with CMB and BAO, the best-fit results are $(\alpha,\beta)=(-3.75,0.0651)$ or $(\Omega_m,\beta)=(0.286,0.0651)$. If we further fix $\beta=0$ (corresponding to $\Lambda$CDM), the best-fit value for $\alpha$ is $\alpha$=$-4.84_{-0.68}^{+0.91}(1\sigma)_{-0.98}^{+1.63}(2\sigma)$ for the lensing analysis and $\alpha$=$-4.35_{-0.16}^{+0.18}(1\sigma)_{-0.25}^{+0.3}(2\sigma)$ for the combined data, respectively. Our results show that $\Lambda$CDM model is within 1$\sigma$ range.Comment: 9 pages, 2 figures, 2 table