14,946 research outputs found

    Inflation and Alternatives with Blue Tensor Spectra

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    We study the tilt of the primordial gravitational waves spectrum. A hint of blue tilt is shown from analyzing the BICEP2 and POLARBEAR data. Motivated by this, we explore the possibilities of blue tensor spectra from the very early universe cosmology models, including null energy condition violating inflation, inflation with general initial conditions, and string gas cosmology, etc. For the simplest G-inflation, blue tensor spectrum also implies blue scalar spectrum. In general, the inflation models with blue tensor spectra indicate large non-Gaussianities. On the other hand, string gas cosmology predicts blue tensor spectrum with highly Gaussian fluctuations. If further experiments do confirm the blue tensor spectrum, non-Gaussianity becomes a distinguishing test between inflation and alternatives.Comment: 13 pages, 10 figures. v2: references and minor improvements added. v3: version to appear on JCA

    Connectivity of Direct Products of Graphs

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    Let κ(G)\kappa(G) be the connectivity of GG and G×HG\times H the direct product of GG and HH. We prove that for any graphs GG and KnK_n with n3n\ge 3, κ(G×Kn)=min{nκ(G),(n1)δ(G)}\kappa(G\times K_n)=min\{n\kappa(G),(n-1)\delta(G)\}, which was conjectured by Guji and Vumar.Comment: 5 pages, accepted by Ars Com

    Inflationary NonGaussianity from Thermal Fluctuations

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    We calculate the contribution of the fluctuations with the thermal origin to the inflationary nonGaussianity. We find that even a small component of radiation can lead to a large nonGaussianity. We show that this thermal nonGaussianity always has positive fNLf_{\rm NL}. We illustrate our result in the chain inflation model and the very weakly dissipative warm inflation model. We show that fNLO(1)f_{NL}\sim {\cal O}(1) is general in such models. If we allow modified equation of state, or some decoupling effects, the large thermal nonGaussianity of order fNL>5f_{\rm NL}>5 or even fNL100f_{\rm NL}\sim 100 can be produced. We also show that the power spectrum of chain inflation should have a thermal origin. In the Appendix A, we made a clarification on the different conventions used in the literature related to the calculation of fNLf_{\rm NL}.Comment: 20 pages, 1 figure. v2, v3: references and acknowledgments update

    Generalized Space-time Noncommutative Inflation

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    We study the noncommutative inflation with a time-dependent noncommutativity between space and time. From the numerical analysis of power law inflation, there are clues that the CMB spectrum indicates a nonconstant noncommutative inflation. Then we extend our treatment to the inflation models with more general noncommutativity and find that the scalar perturbation power spectrum depends sensitively on the time varying of the spacetime noncommutativity. This stringy effect may be probed in the future cosmological observations.Comment: 15 pages, 2 figure

    One-Shot Image Classification by Learning to Restore Prototypes

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    One-shot image classification aims to train image classifiers over the dataset with only one image per category. It is challenging for modern deep neural networks that typically require hundreds or thousands of images per class. In this paper, we adopt metric learning for this problem, which has been applied for few- and many-shot image classification by comparing the distance between the test image and the center of each class in the feature space. However, for one-shot learning, the existing metric learning approaches would suffer poor performance because the single training image may not be representative of the class. For example, if the image is far away from the class center in the feature space, the metric-learning based algorithms are unlikely to make correct predictions for the test images because the decision boundary is shifted by this noisy image. To address this issue, we propose a simple yet effective regression model, denoted by RestoreNet, which learns a class agnostic transformation on the image feature to move the image closer to the class center in the feature space. Experiments demonstrate that RestoreNet obtains superior performance over the state-of-the-art methods on a broad range of datasets. Moreover, RestoreNet can be easily combined with other methods to achieve further improvement.Comment: Published as a conference paper in AAAI 202

    Learning user-specific latent influence and susceptibility from information cascades

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    Predicting cascade dynamics has important implications for understanding information propagation and launching viral marketing. Previous works mainly adopt a pair-wise manner, modeling the propagation probability between pairs of users using n^2 independent parameters for n users. Consequently, these models suffer from severe overfitting problem, specially for pairs of users without direct interactions, limiting their prediction accuracy. Here we propose to model the cascade dynamics by learning two low-dimensional user-specific vectors from observed cascades, capturing their influence and susceptibility respectively. This model requires much less parameters and thus could combat overfitting problem. Moreover, this model could naturally model context-dependent factors like cumulative effect in information propagation. Extensive experiments on synthetic dataset and a large-scale microblogging dataset demonstrate that this model outperforms the existing pair-wise models at predicting cascade dynamics, cascade size, and "who will be retweeted".Comment: from The 29th AAAI Conference on Artificial Intelligence (AAAI-2015

    1-[(Z)-2-Cyano-2-(2-pyrid­yl)vin­yl]­ferrocene

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    In the title compound, [Fe(C5H5)(C13H9N2)], the dihedral angle between the substituted cyclo­penta­dienyl plane and the plane of the pyridine ring is 8.43 (14)°. The double bond adopts a Z configuration. In the crystal structure, weak C—H⋯N inter­actions link the molecules into a zigzag chain. A weak intramolecular C—H⋯N hydrogen bond is also present
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