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Abstract: The progress of machine learning techniques in recent years has been impactful in many fields of research including physics science. While learning more about machine learning subject exciting, an implementation in our research can face a huge learning curve and take time. In this first AI seminar of 2018, I will show our implementation of convolution neural network for analyzing liquid argon time projection chamber (LArTPC) detector data. In particular, we will look at an instance of convolutional neural network trained for semantic-segmentation task (i.e. pixel-level object classification task). This seminar will be in an interactive tutorial style using Jupyter python notebook. In the first part of the seminar, I will give an overview of software stacks and workflow. Then I will demonstrate training the algorithm. Both the software and data will be made available in advance, and the audience is welcome to participate in training the algorithm (which may require an NVIDIA GPU enabled Linux interactive shell such as SLAC computing or AWS service).

Jan

Mar.

31

5: On analyzing urban form at global scale with remote sensing data and generative adversarial networks

Date: Jan. 31, 3pm

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.