Associated production of the top-pions and single top at hadron colliders

In the context of topcolor assisted technicolor(TC2) models, we study the production of the top-pions $\pi_{t}^{0,\pm}$ with single top quark via the processes $p\bar{p} \to t\pi_{t}^{0}+X$ and $p\bar{p} \to t\pi_{t}^{\pm}+X$, and discuss the possibility of detecting these new particles at Tevatron and LHC. We find that it is very difficult to observe the signals of these particles via these processes at Tevatron, while the neutral and charged top-pions $\pi_{t}^{0}$ and $\pi_{t}^{\pm}$ can be detecting via considering the same sign top pair $tt\bar{c}$ event and the $tt\bar{b}$ (or $t\bar{t}b$) event at LHC, respectively.Comment: latex files,14 pages, 7 figures. Accepted for publication in Phys. Rev.

Weiqi games as a tree: Zipf's law of openings and beyond

Weiqi is one of the most complex board games played by two persons. The placement strategies adopted by Weiqi players are often used to analog the philosophy of human wars. Contrary to the western chess, Weiqi games are less studied by academics partially because Weiqi is popular only in East Asia, especially in China, Japan and Korea. Here, we propose to construct a directed tree using a database of extensive Weiqi games and perform a quantitative analysis of the Weiqi tree. We find that the popularity distribution of Weiqi openings with a same number of moves is distributed according to a power law and the tail exponent increases with the number of moves. Intriguingly, the superposition of the popularity distributions of Weiqi openings with the number of moves no more than a given number also has a power-law tail in which the tail exponent increases with the number of moves, and the superposed distribution approaches to the Zipf law. These findings are the same as for chess and support the conjecture that the popularity distribution of board game openings follows the Zipf law with a universal exponent. We also find that the distribution of out-degrees has a power-law form, the distribution of branching ratios has a very complicated pattern, and the distribution of uniqueness scores defined by the path lengths from the root vertex to the leaf vertices exhibits a unimodal shape. Our work provides a promising direction for the study of the decision making process of Weiqi playing from the angle of directed branching tree.Comment: 6 Latex pages including 6 figure

The next-to-next-to-leading order soft function for top quark pair production

We present the first calculation of the next-to-next-to-leading order threshold soft function for top quark pair production at hadron colliders, with full velocity dependence of the massive top quarks. Our results are fully analytic, and can be entirely written in terms of generalized polylogarithms. The scale-dependence of our result coincides with the well-known two-loop anomalous dimension matrix including the three-parton correlations, which at the two-loop order only appear when more than one massive partons are involved in the scattering process. In the boosted limit, our result exhibits the expected factorization property of mass logarithms, which leads to a consistent extraction of the soft fragmentation function. The next-to-next-to-leading order soft function obtained in this paper is an important ingredient for threshold resummation at the next-to-next-to-next-to-leading logarithmic accuracy.Comment: 34 pages, 9 figures; v2: added references, matches the published versio

ADMM Training Algorithms for Residual Networks: Convergence, Complexity and Parallel Training

We design a series of serial and parallel proximal point (gradient) ADMMs for the fully connected residual networks (FCResNets) training problem by introducing auxiliary variables. Convergence of the proximal point version is proven based on a Kurdyka-Lojasiewicz (KL) property analysis framework, and we can ensure a locally R-linear or sublinear convergence rate depending on the different ranges of the Kurdyka-Lojasiewicz (KL) exponent, in which a necessary auxiliary function is constructed to realize our goal. Moreover, the advantages of the parallel implementation in terms of lower time complexity and less (per-node) memory consumption are analyzed theoretically. To the best of our knowledge, this is the first work analyzing the convergence, convergence rate, time complexity and (per-node) runtime memory requirement of the ADMM applied in the FCResNets training problem theoretically. Experiments are reported to show the high speed, better performance, robustness and potential in the deep network training tasks. Finally, we present the advantage and potential of our parallel training in large-scale problems