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April 9: X-ray spectrometer data processing with unsupervised clustering (Sideband signal seeking)
Date: April 9, 3pm
Speaker: Guanqun Zhou
Abstract: Online spectrometer plays an important role in the characterization of the free-electron laser (FEL) pulse spectrum. With the help of beam synchronization acquisition (BSA) system, the spectrum of independent shot can be stored, which helps the downstream scientific researchers a lot. However, because of spontaneous radiation, FEL intrinsic fluctuations and other stochastic effects, the data from spectrometer cannot be fully utilized. A specific case is sideband signal resolution in hard-xray self-seeding experiment. During the seminar, I will present my exploration of employing unsupervised clustering algorithm to mine the latent information in the spectrometer data. In this way, sideband signal starts to appear.
April 23:
TBDMachine learning at LLNL (tentative)
Date: April 23, 3pm
Speaker: Brian Spears (LLNL)
Abstract: TBD
TBD: On analyzing urban form at global scale with remote sensing data and generative adversarial networks
Date: TBD
Speaker: Adrian Albert
Abstract: Current analyses of urban development use either simple, bottom-up models, that have limited predictive performance, or highly engineered, complex models relying on many sources of survey data that are typically scarce and difficult and expensive to collect. This talk presents work-in-progress developing a data-driven, flexible, non-parametric framework to simulate realistic urban forms using generative adversarial networks and planetary-level remote-sensing data. To train our urban simulator, we curate and put forth a new dataset on urban form, integrating spatial distribution maps of population, nighttime luminosity, and built land densities, as well as best-available information on city administrative boundaries for 30,000 of the world's largest cities. This is the first analysis to date of urban form using modern generative models and remote-sensing data.
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