Feature discovery and visualization of robot mission data using convolutional autoencoders and Bayesian nonparametric topic models

The gap between our ability to collect interesting data and our ability to analyze these data is growing at an unprecedented rate. Recent algorithmic attempts to fill this gap have employed unsupervised tools to discover structure in data. Some of the most successful approaches have used probabilistic models to uncover latent thematic structure in discrete data. Despite the success of these models on textual data, they have not generalized as well to image data, in part because of the spatial and temporal structure that may exist in an image stream. We introduce a novel unsupervised machine learning framework that incorporates the ability of convolutional autoencoders to discover features from images that directly encode spatial information, within a Bayesian nonparametric topic model that discovers meaningful latent patterns within discrete data. By using this hybrid framework, we overcome the fundamental dependency of traditional topic models on rigidly hand-coded data representations, while simultaneously encoding spatial dependency in our topics without adding model complexity. We apply this model to the motivating application of high-level scene understanding and mission summarization for exploratory marine robots. Our experiments on a seafloor dataset collected by a marine robot show that the proposed hybrid framework outperforms current state-of-the-art approaches on the task of unsupervised seafloor terrain characterization.Comment: 8 page

Distinguishing Signatures of top-and bottom-type heavy vectorlike quarks at the LHC

An SU(2) vectorlike singlet quark with a charge either +2/3 (t') or -1/3 (b') is predicted in many extensions of the Standard Model. The mixing of these quarks with the top or bottom lead to Flavor Changing Yukawa Interactions and Neutral Current. The decay modes of the heavier mass eigenstates are therefore different from the Standard Model type chiral quarks. The Large Hadron Collider (LHC) will provide an ideal environment to look for the signals of these exotic quarks. Considering all decays, including those involving Z- and Yukawa interactions, we show how one can distinguish between t' and b' from ratios of event rates with different lepton multiplicities. The ability to reconstruct the Higgs boson with a mass around 125.5 GeV plays an important role in such differentiation.Comment: 18 pages, 10 figure