Charmless hadronic decays B→VVB \to VV in the Topcolor-assisted Technicolor model

Based on the effective Hamiltonian with the generalized factorization approach, we calculate the branching ratios and CP asymmetries of $B \to VV$ decays in the Topcolor-assisted Technicolor (TC2) model. Within the considered parameter space we find that: (a) for the penguin-dominated $B \to K^{*+}\phi$ and $K^{*0}\phi$ decays, the new physics enhancements to the branching ratios are around 40%; (b) the measured branching ratios of $B \to K^{*+} \phi$ and $K^{*0} \phi$ decays prefer the range of 3 \lesssim \nceff \lesssim 5; (c) the SM and TC2 model predictions for the branching ratio ${\cal B}(B^+ \to \rho^+ \rho^0)$ are only about half of the Belle's measurement; and (d) for most $B \to VV$ decays, the new physics corrections on their CP asymmetries are generally small or moderate in magnitude and insensitive to the variation of \mpcc and \nceff.Comment: 16 pages, Revtex, 4 EPS figure

Cross Language Text Classification via Subspace Co-Regularized Multi-View Learning

In many multilingual text classification problems, the documents in different languages often share the same set of categories. To reduce the labeling cost of training a classification model for each individual language, it is important to transfer the label knowledge gained from one language to another language by conducting cross language classification. In this paper we develop a novel subspace co-regularized multi-view learning method for cross language text classification. This method is built on parallel corpora produced by machine translation. It jointly minimizes the training error of each classifier in each language while penalizing the distance between the subspace representations of parallel documents. Our empirical study on a large set of cross language text classification tasks shows the proposed method consistently outperforms a number of inductive methods, domain adaptation methods, and multi-view learning methods.Comment: Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012