Doppler effect of gamma-ray bursts in the fireball framework

The influence of the Doppler effect in the fireball framework on the spectrum of gamma-ray bursts is investigated. The study shows that the shape of the expected spectrum of an expanding fireball remains almost the same as that of the corresponding rest frame spectrum for constant radiations of the bremsstrahlung, Comptonized, and synchrotron mechanisms as well as for that of the GRB model. The peak flux spectrum and the peak frequency are obviously correlated. When the value of the Lorentz factor becomes 10 times larger, the flux of fireballs would be several orders of magnitude larger. The expansion speed of fireballs is a fundamental factor of the enhancement of the flux of gamma-ray bursts.Comment: 19 pages, 13 figure

Semi-Supervised Learning by Augmented Distribution Alignment

In this work, we propose a simple yet effective semi-supervised learning approach called Augmented Distribution Alignment. We reveal that an essential sampling bias exists in semi-supervised learning due to the limited number of labeled samples, which often leads to a considerable empirical distribution mismatch between labeled data and unlabeled data. To this end, we propose to align the empirical distributions of labeled and unlabeled data to alleviate the bias. On one hand, we adopt an adversarial training strategy to minimize the distribution distance between labeled and unlabeled data as inspired by domain adaptation works. On the other hand, to deal with the small sample size issue of labeled data, we also propose a simple interpolation strategy to generate pseudo training samples. Those two strategies can be easily implemented into existing deep neural networks. We demonstrate the effectiveness of our proposed approach on the benchmark SVHN and CIFAR10 datasets. Our code is available at \url{https://github.com/qinenergy/adanet}.Comment: To appear in ICCV 201

Fault-tolerant supervisory control of discrete-event systems

In this dissertation, I introduce my study on fault-tolerant supervisory control of discrete event systems. Given a plant, possessing both faulty and nonfaulty behavior, and a submodel for just the nonfaulty part, the goal of fault-tolerant supervisory control is to enforce a certain specifcation for the nonfaulty plant and another (perhaps more liberal) specifcation for the overall plant, and further to ensure that the plant recovers from any fault within a bounded delay so that following the recovery the system state is equivalent to a nonfaulty state (as if no fault ever happened). My research includes the formulation of the notations and the problem, existence conditions, synthesizing algorithms, and applications