Controllable Image-to-Video Translation: A Case Study on Facial Expression Generation

The recent advances in deep learning have made it possible to generate photo-realistic images by using neural networks and even to extrapolate video frames from an input video clip. In this paper, for the sake of both furthering this exploration and our own interest in a realistic application, we study image-to-video translation and particularly focus on the videos of facial expressions. This problem challenges the deep neural networks by another temporal dimension comparing to the image-to-image translation. Moreover, its single input image fails most existing video generation methods that rely on recurrent models. We propose a user-controllable approach so as to generate video clips of various lengths from a single face image. The lengths and types of the expressions are controlled by users. To this end, we design a novel neural network architecture that can incorporate the user input into its skip connections and propose several improvements to the adversarial training method for the neural network. Experiments and user studies verify the effectiveness of our approach. Especially, we would like to highlight that even for the face images in the wild (downloaded from the Web and the authors' own photos), our model can generate high-quality facial expression videos of which about 50\% are labeled as real by Amazon Mechanical Turk workers.Comment: 10 page

Handling Reject Cause on Roaming Network

A wireless multiple-access communications system may provide various types of wireless communication content such as voice, video, messaging, etc. The wireless multiple-access communications system may be a public land mobile network (PLMN). The PLMN may support various radio access technologies (RATs), for example, fourth generation (4G), such as Long-Term Evolution (LTE), LTE-Advanced (LTE-A) or LTE-A Pro system. The PLMN may also include network devices, such as a mobility management entity (MME), a serving gateway, a packet data network, etc. Each of these network devices may support communication for a communication device, which may be otherwise known as a user equipment (UE). The UE may be registered with a PLMN to receive the various types of wireless communication content. The UE may, in some examples, roam out of the PLMN and attempt to register with a roaming network (e.g., another PLMN) via a registration procedure (e.g., an attach procedure), to continue receiving the various types of wireless communication content

Enhancement of Spectral Response in μ

The hydrogenated amorphous silicon (a-Si:H)/hydrogenated microcrystalline silicon (μc-Si:H) double p-type window layer has been developed and applied for improving microcrystalline silicon-germanium p-i-n single-junction thin-film solar cells deposited on textured SnO2:F-coated glass substrates. The substrates of SnO2:F, SnO2:F/μc-Si:H(p), and SnO2:F/a-Si:H(p) were exposed to H2 plasma to investigate the property change. Our results showed that capping a thin layer of a-Si:H(p) on SnO2:F can minimize the Sn reduction during the deposition process which had H2-containing plasma. Optical measurement has also revealed that a-Si:H(p) capped SnO2:F glass had a higher optical transmittance. When the 20 nm μc-Si:H(p) layer was replaced by a 3 nm a-Si:H(p)/17 nm μc-Si:H(p) double window layer in the cell, the conversion efficiency (η) and the short-circuit current density (JSC) were increased by 16.6% and 16.4%, respectively. Compared to the standard cell with the 20 nm μc-Si:H(p) window layer, an improved conversion efficiency of 6.19% can be obtained for the cell having a-Si:H(p)/μc-Si:H(p) window layer, with VOC = 490 mV, JSC = 19.50 mA/cm2, and FF = 64.83%

Bond relaxation, electronic and magnetic behavior of 2D metals structures Y on Li(110) surface

We investigated the bond, electronic and magnetic behavior of adsorption Yttrium atoms on Lithium (110) surface using a combination of Bond-order-length-strength(BOLS) correlation and density-functional theory(DFT). We found that adsorption Y atoms on Li(110) surfaces form two-dimensional (2D) geometric structures of hexagon, nonagon, solid hexagonal, quadrangle and triangle. The consistent with the magnetic moment are 6.66{\mu}B, 5.54{\mu}B, 0.28{\mu}B, 1.04{\mu}B, 2.81{\mu}B, respectively. In addition, this work could pave the way for design new 2D metals electronic and magnetic properties