14,946 research outputs found
Inflation and Alternatives with Blue Tensor Spectra
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
Let be the connectivity of and the direct product
of and . We prove that for any graphs and with ,
, which was conjectured
by Guji and Vumar.Comment: 5 pages, accepted by Ars Com
Inflationary NonGaussianity from Thermal Fluctuations
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 . We illustrate our result in
the chain inflation model and the very weakly dissipative warm inflation model.
We show that is general in such models. If we allow
modified equation of state, or some decoupling effects, the large thermal
nonGaussianity of order or even 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 .Comment: 20 pages, 1 figure. v2, v3: references and acknowledgments update
Generalized Space-time Noncommutative Inflation
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
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
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
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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