Hadronic B Decays to Charmless VT Final States

Charmless hadronic decays of B mesons to a vector meson (V) and a tensor meson (T) are analyzed in the frameworks of both flavor SU(3) symmetry and generalized factorization. We also make comments on B decays to two tensor mesons in the final states. Certain ways to test validity of the generalized factorization are proposed, using $B \to VT$ decays. We calculate the branching ratios and CP asymmetries using the full effective Hamiltonian including all the penguin operators and the form factors obtained in the non-relativistic quark model of Isgur, Scora, Grinstein and Wise.Comment: 27 pages, no figures, LaTe

Determinations of upper critical field in continuous Ginzburg-Landau model

Novel procedures to determine the upper critical field $B_{c2}$ have been proposed within a continuous Ginzburg-Landau model. Unlike conventional methods, where $B_{c2}$ is obtained through the determination of the smallest eigenvalue of an appropriate eigen equation, the square of the magnetic field is treated as eigenvalue problems so that the upper critical field can be directly deduced. The calculated $B_{c2}$ from the two procedures are consistent with each other and in reasonably good agreement with existing theories and experiments. The profile of the order parameter associated with $B_{c2}$ is found to be Gaussian-like, further validating the methodology proposed. The convergences of the two procedures are also studied.Comment: Revtex4, 8 pages, 4 figures, references modified, figures and table embedde

Learning Points and Routes to Recommend Trajectories

The problem of recommending tours to travellers is an important and broadly studied area. Suggested solutions include various approaches of points-of-interest (POI) recommendation and route planning. We consider the task of recommending a sequence of POIs, that simultaneously uses information about POIs and routes. Our approach unifies the treatment of various sources of information by representing them as features in machine learning algorithms, enabling us to learn from past behaviour. Information about POIs are used to learn a POI ranking model that accounts for the start and end points of tours. Data about previous trajectories are used for learning transition patterns between POIs that enable us to recommend probable routes. In addition, a probabilistic model is proposed to combine the results of POI ranking and the POI to POI transitions. We propose a new F$_1$ score on pairs of POIs that capture the order of visits. Empirical results show that our approach improves on recent methods, and demonstrate that combining points and routes enables better trajectory recommendations