7,568 research outputs found
Polarizing primordial gravitational waves by parity violation
We study primordial gravitational waves (PGWs) in the Horava-Lifshitz (HL)
theory of quantum gravity, in which high-order spatial derivative operators,
including the ones violating parity, generically appear in order for the theory
to be power-counting renormalizable and ultraviolet (UV) complete. Because of
both parity violation and non-adiabatic evolution of the modes due to a
modified dispersion relationship, a large polarization of PGWs becomes
possible, and it could be well within the range of detection of the BB, TB and
EB power spectra of the forthcoming cosmic microwave background (CMB)
observations.Comment: revtex4, 3 figures. Phys. Rev. D87, 103512 (2013
Beamformer Based on Quaternion Processes
In this chapter, the problem of quaternion beamformer based on linear and widely linear hypercomplex processing is investigated in scenarios, where there exist one signal and one interference that are uncorrelated. First, we introduce brief information about the quaternion algebra and a quaternion model of linear symmetric array with two-component electromagnetic (EM) vector-sensors is presented. Based on array’s quaternion model, a quaternion MVDR (QMVDR) beamformer is derived and its performance is analysed. Second, we propose the general expression of a quaternion semi-widely linear (QSWL) beamformer and derive its useful implementation and the array’s gain expression. Finally, we give the main results of Monte Carlo simulation
Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks
Prediction of popularity has profound impact for social media, since it
offers opportunities to reveal individual preference and public attention from
evolutionary social systems. Previous research, although achieves promising
results, neglects one distinctive characteristic of social data, i.e.,
sequentiality. For example, the popularity of online content is generated over
time with sequential post streams of social media. To investigate the
sequential prediction of popularity, we propose a novel prediction framework
called Deep Temporal Context Networks (DTCN) by incorporating both temporal
context and temporal attention into account. Our DTCN contains three main
components, from embedding, learning to predicting. With a joint embedding
network, we obtain a unified deep representation of multi-modal user-post data
in a common embedding space. Then, based on the embedded data sequence over
time, temporal context learning attempts to recurrently learn two adaptive
temporal contexts for sequential popularity. Finally, a novel temporal
attention is designed to predict new popularity (the popularity of a new
user-post pair) with temporal coherence across multiple time-scales.
Experiments on our released image dataset with about 600K Flickr photos
demonstrate that DTCN outperforms state-of-the-art deep prediction algorithms,
with an average of 21.51% relative performance improvement in the popularity
prediction (Spearman Ranking Correlation).Comment: accepted in IJCAI-1
- …