High precision predictions for exclusive VHVH production at the LHC

We present a resummation-improved prediction for $VH$ + 0 jets production at the Large Hadron Collider. We focus on highly-boosted final states in the presence of jet veto to suppress the $t{\bar t}$ background. In this case, conventional fixed-order calculations are plagued by the existence of large Sudakov logarithms $\alpha_s^n \log^m (p_T^{veto}/Q)$ for $Q\sim m_V + m_H$ which lead to unreliable predictions as well as large theoretical uncertainties, and thus limit the accuracy when comparing experimental measurements to the Standard Model. In this work, we show that the resummation of Sudakov logarithms beyond the next-to-next-to-leading-log accuracy, combined with the next-to-next-to-leading order calculation, reduces the scale uncertainty and stabilizes the perturbative expansion in the region where the vector bosons carry large transverse momentum. Our result improves the precision with which Higgs properties can be determined from LHC measurements using boosted Higgs techniques.Comment: 24 pages, 8 figure

A General Theory for Direct Quantitative Analysis of Antigen

A theory for direct quantitative analysis of an antigen is proposed. It is based on a potential homogenous immunoreaction system. It establishes an equation to describe the concentration change of the antigen and antibody complex. A maximum point is found in the concentration profile of the complex which can be used to calculate the concentration of the antigen. An experimental scheme was designed for a commercial time-resolved fluoroimmunoassay kit for HBsAg, which is based heterogeneous immunoreaction. The results showed that the theory is practically applicable.Comment: 7pages, 2 figure

Split Bregman Method for Sparse Inverse Covariance Estimation with Matrix Iteration Acceleration

We consider the problem of estimating the inverse covariance matrix by maximizing the likelihood function with a penalty added to encourage the sparsity of the resulting matrix. We propose a new approach based on the split Bregman method to solve the regularized maximum likelihood estimation problem. We show that our method is significantly faster than the widely used graphical lasso method, which is based on blockwise coordinate descent, on both artificial and real-world data. More importantly, different from the graphical lasso, the split Bregman based method is much more general, and can be applied to a class of regularization terms other than the $\ell_1$ nor

Numerical Simulation Based Targeting of the Magushan Skarn Cu-Mo Deposit, Middle-Lower Yangtze Metallogenic Belt, China

The Magushan Cu–Mo deposit is a skarn deposit within the Nanling–Xuancheng mining district of the Middle-Lower Yangtze River Metallogenic Belt (MLYRMB), China. This study presents the results of a new numerical simulation that models the ore-forming processes that generated the Magushan deposit and enables the identification of unexplored areas that have significant exploration potential under areas covered by thick sedimentary sequences that cannot be easily explored using traditional methods. This study outlines the practical value of numerical simulation in determining the processes that operate during mineral deposit formation and how this knowledge can be used to enhance exploration targeting in areas of known mineralization. Our simulation also links multiple subdisciplines such as heat transfer, pressure, fluid flow, chemical reactions, and material migration. Our simulation allows the modeling of the formation and distribution of garnet, a gangue mineral commonly found within skarn deposits (including within the Magushan deposit). The modeled distribution of garnet matches the distribution of known mineralization as well as delineating areas that may well contain high garnet abundances within and around a concealed intrusion, indicating this area should be considered a prospective target during future mineral exploration. Overall, our study indicates that this type of numerical simulation-based approach to prospectivity modeling is both effective and economical and should be considered an additional tool for future mineral exploration to reduce exploration risks when targeting mineralization in areas with thick and unprospective sedimentary cover sequences