Perturbative Bottom-up Approach for Neutrino Mass Matrix in Light of Large \theta_{13} and Role of Lightest Neutrino Mass

We discuss the role of lightest neutrino mass (m_0) in the neutrino mass matrix, defined in a flavor basis, through a bottom-up approach using the current neutrino oscillation data. We find that if m_0 < 10^{-3} eV, then the deviation \delta M_\nu in the neutrino mass matrix from a tree-level, say tribimaximal neutrino mass matrix, does not depend on m_0. As a result \delta M_\nu's are exactly predicted in terms of the experimentally determined quantities such as solar and atmospheric mass squared differences and the mixing angles. On the other hand for m_0 \gsim 10^{-3} eV, \delta M_\nu strongly depends on m_0 and hence can not be determined within the knowledge of oscillation parameters alone. In this limit, we provide an exponential parameterization for \delta M_\nu for all values of m_0 such that it can factorize the m_0 dependency of \delta M_\nu from rest of the oscillation parameters. This helps us in finding \delta M_\nu as a function of the solar and atmospheric mass squared differences and the mixing angles for all values of m_0. We use this information to build up a model of neutrino masses and mixings in a top-down scenario which can predict large \theta_{13} perturbatively.Comment: 26 pages, 42 eps figures, revtex (references are added, more discussions are added in section-III

Automatic CP Invariance and Flavor Symmetry

The approximate conservation of $CP$ can be naturally understood if it arises as an automatic symmetry of the renormalizable Lagrangian. We present a specific realistic example with this feature. In this example, the global Peccei-Quinn symmetry and gauge symmetries of the model make the renormalizable Lagrangian $CP$ invariant but allow non zero hierarchical masses and mixing among the three generations. The left-right and a horizontal $U(1)_H$ symmetry is imposed to achieve this. The non-renormalizable interactions invariant under these symmetries violate $CP$ whose magnitude can be in the experimentally required range if $U(1)_H$ is broken at very high, typically, near the grand unification scale

Doodle to Search: Practical Zero-Shot Sketch-based Image Retrieval

In this paper, we investigate the problem of zero-shot sketch-based image retrieval (ZS-SBIR), where human sketches are used as queries to conduct retrieval of photos from unseen categories. We importantly advance prior arts by proposing a novel ZS-SBIR scenario that represents a firm step forward in its practical application. The new setting uniquely recognizes two important yet often neglected challenges of practical ZS-SBIR, (i) the large domain gap between amateur sketch and photo, and (ii) the necessity for moving towards large-scale retrieval. We first contribute to the community a novel ZS-SBIR dataset, QuickDraw-Extended, that consists of 330,000 sketches and 204,000 photos spanning across 110 categories. Highly abstract amateur human sketches are purposefully sourced to maximize the domain gap, instead of ones included in existing datasets that can often be semi-photorealistic. We then formulate a ZS-SBIR framework to jointly model sketches and photos into a common embedding space. A novel strategy to mine the mutual information among domains is specifically engineered to alleviate the domain gap. External semantic knowledge is further embedded to aid semantic transfer. We show that, rather surprisingly, retrieval performance significantly outperforms that of state-of-the-art on existing datasets that can already be achieved using a reduced version of our model. We further demonstrate the superior performance of our full model by comparing with a number of alternatives on the newly proposed dataset. The new dataset, plus all training and testing code of our model, will be publicly released to facilitate future researchComment: Oral paper in CVPR 201