MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation

In this work, we propose a novel and efficient method for articulated human pose estimation in videos using a convolutional network architecture, which incorporates both color and motion features. We propose a new human body pose dataset, FLIC-motion, that extends the FLIC dataset with additional motion features. We apply our architecture to this dataset and report significantly better performance than current state-of-the-art pose detection systems

Oscillation Effects and Time Variation of the Supernova Neutrino Signal

The neutrinos detected from the next Galactic core-collapse supernova will contain valuable information on the internal dynamics of the explosion. One mechanism leading to a temporal evolution of the neutrino signal is the variation of the induced neutrino flavor mixing driven by changes in the density profile. With one and two dimensional hydrodynamical simulations we identify the behavior and properties of prominent features of the explosion. Using these results we demonstrate the time variation of the neutrino crossing probabilities due to changes in the MSW neutrino transformations as the star explodes by using the S-matrix - Monte Carlo - approach to neutrino propagation. After adopting spectra for the neutrinos emitted from the proto-neutron star we calculate for a Galactic supernova the evolution of the positron spectra within a water Cerenkov detector and the ratio of charged current to neutral current event rates for a heavy water - SNO like - detector and find that these detector signals are feasible probes of a number of explosion features

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201