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

Double active region index-guided semiconductor laser

A buried crescent InGaAsP/InP laser with a double active layer was fabricated. The laser showed very high characteristic temperature T0 and highly nonlinear light versus current characteristics. A theoretical model using a rate equation approach showed good agreement with the experimental results

Concentration Inequalities of Random Matrices and Solving Ptychography with a Convex Relaxation

Random matrix theory has seen rapid development in recent years. In particular, researchers have developed many non-asymptotic matrix concentration inequalities that parallel powerful scalar concentration inequalities. In this thesis, we focus on three topics: 1) estimating sparse covariance matrix using matrix concentration inequalities, 2) constructing the matrix phi-entropy to derive matrix concentration inequalities, 3) developing scalable algorithms to solve the phase recovery problem of ptychography based on low-rank matrix factorization. Estimation of covariance matrix is an important subject. In the setting of high dimensional statistics, the number of samples can be small in comparison to the dimension of the problem, thus estimating the complete covariance matrix is unfeasible. By assuming that the covariance matrix satisfies some sparsity assumptions, prior work has proved that it is feasible to estimate the sparse covariance matrix of Gaussian distribution using the masked sample covariance estimator. In this thesis, we use a new approach and apply non-asymptotic matrix concentration inequalities to obtain tight sample bounds for estimating the sparse covariance matrix of subgaussian distributions. The entropy method is a powerful approach in developing scalar concentration inequalities. The key ingredient is the subadditivity property that scalar entropy function exhibits. In this thesis, we construct a new concept of matrix phi-entropy and prove that matrix phi-entropy also satisfies a subadditivity property similar to the scalar form. We apply this new concept of matrix phi-entropy to derive non-asymptotic matrix concentration inequalities. Ptychography is a computational imaging technique which transforms low-resolution intensity-only images into a high-resolution complex recovery of the signal. Conventional algorithms are based on alternating projection, which lacks theoretical guarantees for their performance. In this thesis, we construct two new algorithms. The first algorithm relies on a convex formulation of the ptychography problem and on low-rank matrix recovery. This algorithm improves traditional approaches' performance but has high computational cost. The second algorithm achieves near-linear runtime and memory complexity by factorizing the objective matrix into its low-rank components and approximates the first algorithm's imaging quality.</p

A vertical monolithic combination of an InGaAsP/InP laser and a heterojunction bipolar transistor

A DH InGaAsP/InP mesa laser and a DH InGaAsP/InP mass-transport laser were successfully put together with an InGaAsP/InP heterojunction bipolar transistor in a vertical configuration. A laser threshold current as low as 17 mA and an output laser power of over 30 mW were achieved. Base injection current-controlled optical bistability and optical switching were demonstrated

Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks

Magnetic resonance image (MRI) in high spatial resolution provides detailed anatomical information and is often necessary for accurate quantitative analysis. However, high spatial resolution typically comes at the expense of longer scan time, less spatial coverage, and lower signal to noise ratio (SNR). Single Image Super-Resolution (SISR), a technique aimed to restore high-resolution (HR) details from one single low-resolution (LR) input image, has been improved dramatically by recent breakthroughs in deep learning. In this paper, we introduce a new neural network architecture, 3D Densely Connected Super-Resolution Networks (DCSRN) to restore HR features of structural brain MR images. Through experiments on a dataset with 1,113 subjects, we demonstrate that our network outperforms bicubic interpolation as well as other deep learning methods in restoring 4x resolution-reduced images.Comment: Accepted by ISBI'1