9,108 research outputs found

    A framework of rapid regional tsunami damage recognition from post-event TerraSAR-X imagery using deep neural networks

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    Near real-time building damage mapping is an indispensable prerequisite for governments to make decisions for disaster relief. With high-resolution synthetic aperture radar (SAR) systems, such as TerraSAR-X, the provision of such products in a fast and effective way becomes possible. In this letter, a deep learning-based framework for rapid regional tsunami damage recognition using post-event SAR imagery is proposed. To perform such a rapid damage mapping, a series of tile-based image split analysis is employed to generate the data set. Next, a selection algorithm with the SqueezeNet network is developed to swiftly distinguish between built-up (BU) and nonbuilt-up regions. Finally, a recognition algorithm with a modified wide residual network is developed to classify the BU regions into wash away, collapsed, and slightly damaged regions. Experiments performed on the TerraSAR-X data from the 2011 Tohoku earthquake and tsunami in Japan show a BU region extraction accuracy of 80.4% and a damage-level recognition accuracy of 74.8%, respectively. Our framework takes around 2 h to train on a new region, and only several minutes for prediction.This work was supported in part by JST CREST, Japan, under Grant JPMJCR1411 and in part by the China Scholarship Council. (JPMJCR1411 - JST CREST, Japan; China Scholarship Council

    Bayesian detection of embryonic gene expression onset in C. elegans

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    To study how a zygote develops into an embryo with different tissues, large-scale 4D confocal movies of C. elegans embryos have been produced recently by experimental biologists. However, the lack of principled statistical methods for the highly noisy data has hindered the comprehensive analysis of these data sets. We introduced a probabilistic change point model on the cell lineage tree to estimate the embryonic gene expression onset time. A Bayesian approach is used to fit the 4D confocal movies data to the model. Subsequent classification methods are used to decide a model selection threshold and further refine the expression onset time from the branch level to the specific cell time level. Extensive simulations have shown the high accuracy of our method. Its application on real data yields both previously known results and new findings.Comment: Published at http://dx.doi.org/10.1214/15-AOAS820 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

    Multi-wavelength emissions from the millisecond pulsar binary PSR J1023+0038 during an accretion active state

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    Recent observations strongly suggest that the millisecond pulsar binary PSR J1023+0038 has developed an accretion disk since 2013 June. We present a multi-wavelength analysis of PSR J1023+0038, which reveals that 1) its gamma-rays suddenly brightened within a few days in June/July 2013 and has remained at a high gamma-ray state for several months; 2) both UV and X-ray fluxes have increased by roughly an order of magnitude, and 3) the spectral energy distribution has changed significantly after the gamma-ray sudden flux change. Time variabilities associated with UV and X-rays are on the order of 100-500 seconds and 50-100 seconds, respectively. Our model suggests that a newly formed accretion disk due to the sudden increase of the stellar wind could explain the changes of all these observed features. The increase of UV is emitted from the disk, and a new component in gamma-rays is produced by inverse Compton scattering between the new UV component and pulsar wind. The increase of X-rays results from the enhancement of injection pulsar wind energy into the intra-binary shock due to the increase of the stellar wind. We also predict that the radio pulses may be blocked by the evaporated winds from the disk and the pulsar is still powered by rotation.Comment: 8 pages, 3 figures; accepted for publication in Ap
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