13,003 research outputs found
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
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