Kevin Smith

TL;DR: A novel acquisition scheme and non-parametric multi-image based method for correcting illumination in fluorescence images by measuring changes in intensity by moving the microscope stage at regularly spaced intervals, and exploiting this information to learn the correction function.

TL;DR: This work proposes an automated graph partitioning scheme that reduces the computational complexity by operating on supervoxels instead of voxels, incorporates global shape features capable of describing the 3D shape of the target objects, and learns to recognize the distinctive appearance of true boundaries.

TL;DR: The Augmented Multi-Party Interaction (AMI) project as mentioned in this paper addresses the automatic recognition from audio, video, and combined audio-video streams, that have been recorded during meetings and describes the progress that has been made in the first two years of the project.

TL;DR: This work proposes a multi-person tracking solution based on a hybrid Dynamic Bayesian Network that simultaneously infers the number of people in a scene, their body locations, their head locations, and their head pose and proposes a trans-dimensional Markov Chain Monte Carlo sampling scheme that efficiently searches the state-space by allowing person-part state updates.

TL;DR: Using assays for pathogen entry, knockdown complementation, and colocalization at single-cell resolution, the requirement of the VPS retromer for Brucella to escape the lysosomal degradative pathway and to establish its intracellular replicative niche is identified and validated by a systematic cell-based small interfering RNA (siRNA) knockdown screen.