A neural encoder for earthquake rate forecasting.

Forecasting the timing of earthquakes is a long-standing challenge. Moreover, it is still debated how to formulate this problem in a useful manner, or to compare the predictive power of different models. Here, we develop a versatile neural encoder of earthquake catalogs, and apply it to the fundamental problem of earthquake rate prediction, in the spatio-temporal point process framework.

Vocal folds analysis using global energy tracking.

Introduction: Detection and quantification of oscillatory irregularities in laryngeal videostroboscopy can be particularly difficult for the human expert. Accordingly, there is a wide interest in automated methods for recovering the folds' temporal trajectory. Unfortunately, current methods typically provide only crude glottal measurements.

Markov random field based automatic image alignment for electron tomography.

We present a method for automatic full-precision alignment of the images in a tomographic tilt series. Full-precision automatic alignment of cryo electron microscopy images has remained a difficult challenge to date, due to the limited electron dose and low image contrast. These facts lead to poor signal to noise ratio (SNR) in the images, which causes automatic feature trackers to generate errors, even with high contrast gold particles as fiducial features.

Towards an integrated protein-protein interaction network: a relational Markov network approach.

Protein-protein interactions play a major role in most cellular processes. Thus, the challenge of identifying the full repertoire of interacting proteins in the cell is of great importance and has been addressed both experimentally and computationally. Today, large scale experimental studies of protein interactions, while partial and noisy, allow us to characterize properties of interacting proteins and develop predictive algorithms.