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We develop a new heavy quark transport model, QLBT, to simulate the dynamical propagation of heavy quarks inside the quark-gluon plasma (QGP) created in relativistic heavy-ion collisions. Our QLBT model is based on the linear Boltzmann transport (LBT) model with the ideal QGP replaced by a collection of quasi-particles to account for the non-perturbative interactions among quarks and gluons of the hot QGP. The thermal masses of quasi-particles are fitted to the equation of state from lattice QCD simulations using the Bayesian statistical analysis method. Combining QLBT with our advanced hybrid fragmentation-coalescence hadronization approach, we calculate the nuclear modification factor$$R_\mathrm {AA}$$$R_{\mathrm{AA}}$and the elliptic flow$$v_2$$$v_2$ofDmesons at the Relativistic Heavy-Ion Collider and the Large Hadron Collider. By comparing our QLBT calculation to the experimental data on theDmeson$$R_\mathrm {AA}$$$R_{\mathrm{AA}}$and$$v_2$$$v_2$, we extract the heavy quark transport parameter$$\hat{q}$$$\hat{q}$and diffusion coefficient$$D_\mathrm {s}$$$D_s$in the temperature range of$$1-4~T_\mathrm {c}$$$1-4T_c$, and compare them with the lattice QCD results and other phenomenological studies.

 

Under the linear regression framework, we study the variable selection problem when the underlying model is assumed to have a small number of nonzero coefficients. Non-convex penalties in specic forms are well-studied in the literature for sparse estimation. A recent work, Ahn, Pang, and Xin (2017), has pointed out that nearly all existing non-convex penalties can be represented as difference-of-convex (DC) functions, which are the difference of two convex functions, while itself may not be convex. There is a large existing literature on optimization problems when their objectives and/or constraints involve DC functions. Efficient numerical solutions have been proposed. Under the DC framework, directional-stationary (d-stationary) solutions are considered, and they are usually not unique. In this paper, we show that under some mild conditions, a certain subset of d-stationary solutions in an optimization problem (with a DC objective) has some ideal statistical properties: namely, asymptotic estimation consistency, asymptotic model selection consistency, asymptotic efficiency. Our assumptions are either weaker than or comparable with those conditions that have been adopted in other existing works. This work shows that DC is a nice framework to offer a unied approach to these existing works where non-convex penalties are involved. Our work bridges the communities of optimization and statistics. 

In image detection, one problem is to test whether the set, though mainly consisting of uniformly scattered points, also contains a small fraction of points sampled from some (a priori unknown) curve, for example, a curve with $C^\alpha$-norm bounded by $\beta$. One approach is to analyze the data by counting membership in multiscale multianisotropic strips, which involves an algorithm that delves into the length of the path connecting many consecutive “significant” nodes. In this paper, we develop the mathematical formalism of this algorithm and analyze the statistical property of the length of the longest significant run. The rate of convergence is derived. Using percolation theory and random graph theory, we present a novel probabilistic model named, pseudo-tree model. Based on the asymptotic results for the pseudo-tree model, we further study the length of the longest significant run in an “inflating” Bernoulli net. We find that the probability parameter $p$ of significant node plays an important role: there is a threshold $p_c$, such that in the cases of $p < p_c$ and $p > p_c$, very different asymptotic behaviors of the length of the significant runs are observed. We apply our results to the detection of an underlying curvilinear feature and prove that the test based on our proposed longest run theory is asymptotically powerful.