Unified TeV Scale Picture of Baryogenesis and Dark Matter

We present a simple extension of MSSM which provides a unified picture of cosmological baryon asymmetry and dark matter. Our model introduces a gauge singlet field $N$ and a color triplet field $X$ which couple to the right--handed quark fields. The out--of equilibrium decay of the Majorana fermion $N$ mediated by the exchange of the scalar field $X$ generates adequate baryon asymmetry for $M_N \sim 100$ GeV and $M_X \sim$ TeV. The scalar partner of $N$ (denoted $\tilde{N}_1$) is naturally the lightest SUSY particle as it has no gauge interactions and plays the role of dark matter. $\tilde{N}_1$ annihilates into quarks efficiently in the early universe via the exchange of the fermionic $\tilde{X}$ field. The model is experimentally testable in (i) neutron--antineutron oscillations with a transition time estimated to be around $10^{10}$ sec, (ii) discovery of colored particles $X$ at LHC with mass of order TeV, and (iii) direct dark matter detection with a predicted cross section in the observable range.Comment: 10 pages, one reference updated. Version to appear in Phys. Rev. Let

DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs

We present a novel deep learning architecture for fusing static multi-exposure images. Current multi-exposure fusion (MEF) approaches use hand-crafted features to fuse input sequence. However, the weak hand-crafted representations are not robust to varying input conditions. Moreover, they perform poorly for extreme exposure image pairs. Thus, it is highly desirable to have a method that is robust to varying input conditions and capable of handling extreme exposure without artifacts. Deep representations have known to be robust to input conditions and have shown phenomenal performance in a supervised setting. However, the stumbling block in using deep learning for MEF was the lack of sufficient training data and an oracle to provide the ground-truth for supervision. To address the above issues, we have gathered a large dataset of multi-exposure image stacks for training and to circumvent the need for ground truth images, we propose an unsupervised deep learning framework for MEF utilizing a no-reference quality metric as loss function. The proposed approach uses a novel CNN architecture trained to learn the fusion operation without reference ground truth image. The model fuses a set of common low level features extracted from each image to generate artifact-free perceptually pleasing results. We perform extensive quantitative and qualitative evaluation and show that the proposed technique outperforms existing state-of-the-art approaches for a variety of natural images.Comment: ICCV 201

Neutrino Masses and Mixings in a Minimal SO(10) Model

We consider a minimal formulation of SO(10) Grand Unified Theory wherein all the fermion masses arise from Yukawa couplings involving one 126 and one 10 of Higgs multiplets. It has recently been recognized that such theories can explain, via the type-II seesaw mechanism, the large \nu_\mu - \nu_\tau mixing as a consequence of b-tau unification at the GUT scale. In this picture, however, the CKM phase \delta lies preferentially in the second quadrant, in contradiction with experimental measurements. We revisit this minimal model and show that the conventional type-I seesaw mechanism generates phenomenologically viable neutrino masses and mixings, while being consistent with CKM CP violation. We also present improved fits in the type-II seesaw scenario and suggest fully consistent fits in a mixed scenario.Comment: 27 pages, 13 eps figures, revtex4; references added, some minor correction