ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes

Exploiting synthetic data to learn deep models has attracted increasing attention in recent years. However, the intrinsic domain difference between synthetic and real images usually causes a significant performance drop when applying the learned model to real world scenarios. This is mainly due to two reasons: 1) the model overfits to synthetic images, making the convolutional filters incompetent to extract informative representation for real images; 2) there is a distribution difference between synthetic and real data, which is also known as the domain adaptation problem. To this end, we propose a new reality oriented adaptation approach for urban scene semantic segmentation by learning from synthetic data. First, we propose a target guided distillation approach to learn the real image style, which is achieved by training the segmentation model to imitate a pretrained real style model using real images. Second, we further take advantage of the intrinsic spatial structure presented in urban scene images, and propose a spatial-aware adaptation scheme to effectively align the distribution of two domains. These two modules can be readily integrated with existing state-of-the-art semantic segmentation networks to improve their generalizability when adapting from synthetic to real urban scenes. We evaluate the proposed method on Cityscapes dataset by adapting from GTAV and SYNTHIA datasets, where the results demonstrate the effectiveness of our method.Comment: Add experiments on SYNTHIA, CVPR 2018 camera-ready versio

All Optical Regeneration

All optical regeneration methods and systems can be realized through an exponential amplifier and a limiting amplifier, which could be two independent devices (one piece of fiber with parametric amplification and a semiconductor optical amplifier operating at saturation state) or one single device (one piece of fiber). The signal quality and the extinction ratio after regeneration are significantly improved compared with the degraded incoming data using a parametric amplifier with the data signal to be regenerated as the pump. The regenerated data has an extinction ratio as high as 14 dB, an extinction ratio enhancement of approximately 5 dB and an approximately 5 dB negative power penalty. This regeneration schemes are format transparent (RZ and NRZ), and provide noise reduction both for bit 1s and bit 0s of the data sequence. The regeneration method and apparatus that just utilizes fibers has the additional capability of ultrafast response speed (several femtoseconds due to the Ker

Normalized solutions for some quasilinear elliptic equation with critical Sobolev exponent

Consider the equation \begin{equation*} -\Delta_p u =\lambda |u|^{p-2}u+\mu|u|^{q-2}u+|u|^{p^\ast-2}u\ \ {\rm in}\ \R^N \end{equation*} under the normalized constraint $$\int_{ \R^N}|u|^p=c^p,$$ where $-\Delta_pu={\rm div} (|\nabla u|^{p-2}\nabla u)$, $1<p<N$, $p0$ and $\lambda\in\R$. In the purely $L^p$-subcritical case, we obtain the existence of ground state solution by virtue of truncation technique, and obtain multiplicity of normalized solutions. In the purely $L^p$-critical and supercritical case, we drive the existence of positive ground state solution, respectively. Finally, we investigate the asymptotic behavior of ground state solutions obtained above as $\mu\to0^+$