Convex quantization preserves logconcavity

Much like convexity is key to variational optimization, a logconcave distribution is key to amenable statistical inference. Quantization is often disregarded when writing likelihood models: ignoring the limitations of physical detectors. This begs the questions: would including quantization preclude logconcavity, and, are the true data likelihoods logconcave? We show that the same simple assumption that leads to logconcave continuous data likelihoods also leads to logconcave quantized data likelihoods, provided that convex quantization regions are used.

Artifacts in optical projection tomography due to refractive index mismatch: model and correction

Optical Projection Tomography (OPT) is a powerful tool for 3D imaging of mesoscopic samples, thus of great importance to image whole organs for the study of various disease models in life sciences. OPT is able to achieve resolution at a few tens of microns over a large sample volume of several cubic centimeters. However, the reconstructed OPT images often suffer from artifacts caused by different kinds of physical miscalibration. This work focuses on the refractive index (RI) mismatch between the rotating object and the surrounding medium. We derive a 3D cone beam forward model to approximate the effect of RI mismatch and implement a fast and efficient reconstruction method to correct the induced seagull-shaped artifacts on experimental images of fluorescent beads.