Gauge Theory Model of the Neutrino and New Physics Beyond the Standard Model

Majorana features of neutrinos and SO(3) gauge symmetry of three families enable us to construct a gauge model of neutrino for understanding naturally the observed smallness of neutrino masses and the nearly tri-bimaximal neutrino mixing when combining together with the mechanism of approximate global U(1) family symmetry. The vacuum structure of SO(3) symmetry breaking is found to play an important role. The mixing angle $\theta_{13}$ and CP-violating phases governed by the vacuum of spontaneous symmetry breaking are in general non-zero and testable experimentally at the allowed sensitivity. The model predicts the existence of vector-like SO(3) triplet charged leptons and vector-like SO(3) triplet Majorana neutrinos as well as SO(3) tri-triplet Higgs bosons, some of them can be light and explored at the colliders LHC and ILC.Comment: 15 pages, only typos in table 1 corrected in this replaced versio

The Lepton-Number-Violating Decays of B+,D+B^+, D^+ and Ds+D_s^+ Mesons Induced by the Doubly Charged Higgs Boson

The lepton-number-violating decays of $B^+, D^+$ and $D_s^+$ mesons induced by the doubly charged Higgs boson have been studied. It is found that although the yielded results of the branch ratio are much smaller than the present limits from the data they are consistent with the previous conclusions calculated in the framwork of relativistic quark model where the processes happened via the light Majorana neutrinos.Comment: version to appear in PR

Nuclear mass predictions based on Bayesian neural network approach with pairing and shell effects

Bayesian neural network (BNN) approach is employed to improve the nuclear mass predictions of various models. It is found that the noise error in the likelihood function plays an important role in the predictive performance of the BNN approach. By including a distribution for the noise error, an appropriate value can be found automatically in the sampling process, which optimizes the nuclear mass predictions. Furthermore, two quantities related to nuclear pairing and shell effects are added to the input layer in addition to the proton and mass numbers. As a result, the theoretical accuracies are significantly improved not only for nuclear masses but also for single-nucleon separation energies. Due to the inclusion of the shell effect, in the unknown region, the BNN approach predicts a similar shell-correction structure to that in the known region, e.g., the predictions of underestimation of nuclear mass around the magic numbers in the relativistic mean-field model. This manifests that better predictive performance can be achieved if more physical features are included in the BNN approach.Comment: 15 pages, 4 figures, and 3 table

Iteration-Complexity of a Generalized Forward Backward Splitting Algorithm

In this paper, we analyze the iteration-complexity of Generalized Forward--Backward (GFB) splitting algorithm, as proposed in \cite{gfb2011}, for minimizing a large class of composite objectives $f + \sum_{i=1}^n h_i$ on a Hilbert space, where $f$ has a Lipschitz-continuous gradient and the $h_i$'s are simple (\ie their proximity operators are easy to compute). We derive iteration-complexity bounds (pointwise and ergodic) for the inexact version of GFB to obtain an approximate solution based on an easily verifiable termination criterion. Along the way, we prove complexity bounds for relaxed and inexact fixed point iterations built from composition of nonexpansive averaged operators. These results apply more generally to GFB when used to find a zero of a sum of $n > 0$ maximal monotone operators and a co-coercive operator on a Hilbert space. The theoretical findings are exemplified with experiments on video processing.Comment: 5 pages, 2 figure

Identifiability and Unmixing of Latent Parse Trees

This paper explores unsupervised learning of parsing models along two directions. First, which models are identifiable from infinite data? We use a general technique for numerically checking identifiability based on the rank of a Jacobian matrix, and apply it to several standard constituency and dependency parsing models. Second, for identifiable models, how do we estimate the parameters efficiently? EM suffers from local optima, while recent work using spectral methods cannot be directly applied since the topology of the parse tree varies across sentences. We develop a strategy, unmixing, which deals with this additional complexity for restricted classes of parsing models

An X-ray and radio study of the cluster A 2717

We present an X-ray, radio and optical study of the cluster A 2717. The central D galaxy is associated with aWide- Angled-Tailed (WAT) radio source. A Rosat PSPC observation of the cluster shows that the cluster has a well constrained temperature of 1.9+0.3 −0.2 × 107 K. The pressure of the intracluster medium was found to be comparable to the mininum pressure of the radio source suggesting that the tails may in fact be in equipartition with the surrounding hot gas