Primordial Black Holes from Sound Speed Resonance during Inflation

We report on a novel phenomenon of the resonance effect of primordial density perturbations arisen from a sound speed parameter with an oscillatory behavior, which can generically lead to the formation of primordial black holes in the early Universe. For a general inflaton field, it can seed primordial density fluctuations and their propagation is governed by a parameter of sound speed square. Once if this parameter achieves an oscillatory feature for a while during inflation, a significant non-perturbative resonance effect on the inflaton field fluctuations takes place around a critical length scale, which results in significant peaks in the primordial power spectrum. By virtue of this robust mechanism, primordial black holes with specific mass function can be produced with a sufficient abundance for dark matter in sizable parameter ranges.Comment: 6 pages, 4 figures; v2: figures replotted with corrections, analysis extended, version accepted by Phys.Rev.Let

Island Loss for Learning Discriminative Features in Facial Expression Recognition

Over the past few years, Convolutional Neural Networks (CNNs) have shown promise on facial expression recognition. However, the performance degrades dramatically under real-world settings due to variations introduced by subtle facial appearance changes, head pose variations, illumination changes, and occlusions. In this paper, a novel island loss is proposed to enhance the discriminative power of the deeply learned features. Specifically, the IL is designed to reduce the intra-class variations while enlarging the inter-class differences simultaneously. Experimental results on four benchmark expression databases have demonstrated that the CNN with the proposed island loss (IL-CNN) outperforms the baseline CNN models with either traditional softmax loss or the center loss and achieves comparable or better performance compared with the state-of-the-art methods for facial expression recognition.Comment: 8 pages, 3 figure

Optimizing Filter Size in Convolutional Neural Networks for Facial Action Unit Recognition

Recognizing facial action units (AUs) during spontaneous facial displays is a challenging problem. Most recently, Convolutional Neural Networks (CNNs) have shown promise for facial AU recognition, where predefined and fixed convolution filter sizes are employed. In order to achieve the best performance, the optimal filter size is often empirically found by conducting extensive experimental validation. Such a training process suffers from expensive training cost, especially as the network becomes deeper. This paper proposes a novel Optimized Filter Size CNN (OFS-CNN), where the filter sizes and weights of all convolutional layers are learned simultaneously from the training data along with learning convolution filters. Specifically, the filter size is defined as a continuous variable, which is optimized by minimizing the training loss. Experimental results on two AU-coded spontaneous databases have shown that the proposed OFS-CNN is capable of estimating optimal filter size for varying image resolution and outperforms traditional CNNs with the best filter size obtained by exhaustive search. The OFS-CNN also beats the CNN using multiple filter sizes and more importantly, is much more efficient during testing with the proposed forward-backward propagation algorithm

Biased diversity metrics revealed by bacterial 16S pyrotags derived from different primer sets.

published_or_final_versio

UAV-enabled optimal position selection for secure and precise wireless transmission

In this letter, two unmanned-aerial-vehicle (UAV) optimal position selection schemes are proposed. Based on the proposed schemes, the optimal UAV transmission positions for secure precise wireless transmission (SPWT) are given, where the maximum secrecy rate (SR) can be achieved without artificial noise (AN). In conventional SPWT schemes, the transmission location is not considered which impacts the SR a lot. The proposed schemes find the optimal transmission positions based on putting the eavesdropper at the null point. Thus, the received confidential message energy at the eavesdropper is zero, and the maximum SR achieves. Simulation results show that proposed schemes have improved the SR performance significantly

When primordial black holes from sound speed resonance meet a stochastic background of gravitational waves

Theoretical Physic