Spontaneous leptonic CP violation and nonzero theta(13)

We consider a simple extension of the Standard Model by adding two Higgs triplets and a complex scalar singlet to its particle content. In this framework, the CP symmetry is spontaneously broken at high energies by the complex vacuum expectation value of the scalar singlet. Such a breaking leads to leptonic CP violation at low energies. The model also exhibits an A(4) X Z(4) flavor symmetry which, after being spontaneously broken at a high-energy scale, yields a tribimaximal pattern in the lepton sector. We consider small perturbations around the tribimaximal vacuum alignment condition in order to generate nonzero values of theta(13), as required by the latest neutrino oscillation data. It is shown that the value of theta(13) recently measured by the Daya Bay Reactor Neutrino Experiment can be accommodated in our framework together with large Dirac-type CP violation. We also address the viability of leptogenesis in our model through the out-of-equilibrium decays of the Higgs triplets. In particular, the CP asymmetries in the triplet decays into two leptons are computed and it is shown that the effective leptogenesis and low-energy CP-violating phases are directly linked.

Stability of the tree-level vacuum in two Higgs doublet models against charge or CP spontaneous violation

We show that in two Higgs doublet models at tree-level the potential minimum preserving electric charge and CP symmetries, when it exists, is the global one. Furthermore, we derived a very simple condition, involving only the coefficients of the quartic terms of the potential, that guarantees spontaneous CP breaking. (C) 2004 Elsevier B.V. All rights reserved.

Enhancing Multimodal Silent Speech Interfaces With Feature Selection

In research on Silent Speech Interfaces (SSI), different sources of information (modalities) have been combined, aiming at obtaining better performance than the individual modalities. However, when combining these modalities, the dimensionality of the feature space rapidly increases, yielding the well-known "curse of dimensionality". As a consequence, in order to extract useful information from this data, one has to resort to feature selection (FS) techniques to lower the dimensionality of the learning space. In this paper, we assess the impact of FS techniques for silent speech data, in a dataset with 4 non-invasive and promising modalities, namely: video, depth, ultrasonic Doppler sensing, and surface electromyography. We consider two supervised (mutual information and Fisher's ratio) and two unsupervised (meanmedian and arithmetic mean geometric mean) FS filters. The evaluation was made by assessing the classification accuracy (word recognition error) of three well-known classifiers (knearest neighbors, support vector machines, and dynamic time warping). The key results of this study show that both unsupervised and supervised FS techniques improve on the classification accuracy on both individual and combined modalities. For instance, on the video component, we attain relative performance gains of 36.2% in error rates. FS is also useful as pre-processing for feature fusion. Copyright © 2014 ISCA.