Towards a warped inflationary brane scanning

We present a detailed systematics for comparing warped brane inflation with the observations, incorporating the effects of both moduli stabilization and ultraviolet bulk physics. We explicitly construct an example of the inflaton potential governing the motion of a mobile D3 brane in the entire warped deformed conifold. This allows us to precisely identify the corresponding scales of the cosmic microwave background. The effects due to bulk fluxes or localized sources are parametrized using gauge/string duality. We next perform some sample scannings to explore the parameter space of the complete potential, and first demonstrate that without the bulk effects there can be large degenerate sets of parameters with observationally consistent predictions. When the bulk perturbations are included, however, the observational predictions are generally spoiled. For them to remain consistent, the magnitudes of the bulk effects need to be highly suppressed via fine tuning.Comment: (v1) 11 pages, 2 figures, 2 tables; (v2) more clarifications and references added; (v3) 12 pages, more discussions, to appear in Physical Review

Breaking scale invariance from a singular inflaton potential

In this paper we break the scale invariance of the primordial power spectrum of curvature perturbations of inflation. Introducing a singular behaviour due to spontaneous symmetry breaking in the inflaton potential, we obtain fully analytic expressions of scale dependent oscillation and a modulation in power on small scale in the primordial spectrum. And we give the associated cosmic microwave background and matter power spectra which we can observe now and discuss the signature of the scale dependence. We also address the possibility of whether some inflationary model with featured potential might mimic the predictions of the scale invariant power spectrum. We present some examples which illustrate such degeneracies.Comment: 20 pages, 9 figures; Discussion expanded and references added; Miscellaneous typos correcte

Inflation in minimal left-right symmetric model with spontaneous D-parity breaking

We present a simplest inflationary scenario in the minimal left-right symmetric model with spontaneous D-parity breaking, which is a well motivated particle physics model for neutrino masses. This leads us to connect the observed anisotropies in the cosmic microwave background to the sub-eV neutrino masses. The baryon asymmetry via the leptogenesis route is also discussed briefly.Comment: (v1) 4 pages, 1 figure; (v2) typos corrected; (v3) title and abstract changed, numerical estimates given, minor changes; (v4) 5 pages, relations between the neutrino masses and the CMB fluctuations become more explicit, miscellaneous changes, to appear in Physical Review

Inflationary Hubble Parameter from the Gravitational Wave Spectrum in the General Slow-roll Approximation

Improved general slow-roll formulae giving the primordial gravitational wave spectrum are derived in the present work. Also the first and second order general slow-roll inverse formulae giving the Hubble parameter $H$ in terms of the gravitational wave spectrum are derived. Moreover, the general slow-roll consistency condition relating the scalar and tensor spectra is obtained

When is Quantum Decoherence Dynamics Classical?

A direct classical analog of quantum decoherence is introduced. Similarities and differences between decoherence dynamics examined quantum mechanically and classically are exposed via a second-order perturbative treatment and via a strong decoherence theory, showing a strong dependence on the nature of the system-environment coupling. For example, for the traditionally assumed linear coupling, the classical and quantum results are shown to be in exact agreement.Comment: 5 pages, no figures, to appear in Physical Review Letter

Variability of Contact Process in Complex Networks

We study numerically how the structures of distinct networks influence the epidemic dynamics in contact process. We first find that the variability difference between homogeneous and heterogeneous networks is very narrow, although the heterogeneous structures can induce the lighter prevalence. Contrary to non-community networks, strong community structures can cause the secondary outbreak of prevalence and two peaks of variability appeared. Especially in the local community, the extraordinarily large variability in early stage of the outbreak makes the prediction of epidemic spreading hard. Importantly, the bridgeness plays a significant role in the predictability, meaning the further distance of the initial seed to the bridgeness, the less accurate the predictability is. Also, we investigate the effect of different disease reaction mechanisms on variability, and find that the different reaction mechanisms will result in the distinct variabilities at the end of epidemic spreading.Comment: 6 pages, 4 figure

Non-Gaussianity from false vacuum inflation: Old curvaton scenario

We calculate the three-point correlation function of the comoving curvature perturbation generated during an inflationary epoch driven by false vacuum energy. We get a novel false vacuum shape bispectrum, which peaks in the equilateral limit. Using this result, we propose a scenario which we call "old curvaton". The shape of the resulting bispectrum lies between the local and the false vacuum shapes. In addition we have a large running of the spectral index.Comment: 13 pages, 3 figures; v2 with minor revison; v3 final version to appear on JCA

Deep Discrete Hashing with Self-supervised Pairwise Labels

Hashing methods have been widely used for applications of large-scale image retrieval and classification. Non-deep hashing methods using handcrafted features have been significantly outperformed by deep hashing methods due to their better feature representation and end-to-end learning framework. However, the most striking successes in deep hashing have mostly involved discriminative models, which require labels. In this paper, we propose a novel unsupervised deep hashing method, named Deep Discrete Hashing (DDH), for large-scale image retrieval and classification. In the proposed framework, we address two main problems: 1) how to directly learn discrete binary codes? 2) how to equip the binary representation with the ability of accurate image retrieval and classification in an unsupervised way? We resolve these problems by introducing an intermediate variable and a loss function steering the learning process, which is based on the neighborhood structure in the original space. Experimental results on standard datasets (CIFAR-10, NUS-WIDE, and Oxford-17) demonstrate that our DDH significantly outperforms existing hashing methods by large margin in terms of~mAP for image retrieval and object recognition. Code is available at \url{https://github.com/htconquer/ddh}