For an in-depth, AOI-based analysis of mobile eye tracking data, a preceding gaze assignment step is inevitable. Current solutions such as manual gaze mapping or marker-based approaches are tedious and not suitable for applications manipulating tangible objects. This makes mobile eye tracking studies with several hours of recording difficult to analyse quantitatively. We introduce a new machine learning-based algorithm, the computational Gaze-Object Mapping (cGOM), that automatically maps gaze data onto respective AOIs. cGOM extends state-of-the-art object detection and segmentation by mask R-CNN with a gaze mapping feature. The new algorithm’s performance is validated against a manual fixation-by-fixation mapping, which is considered as ground truth, in terms of true positive rate (TPR), true negative rate (TNR) and efficiency. Using only 72 training images with 264 labelled object representations, cGOM is able to reach a TPR of approx. 80% and a TNR of 85% compared to the manual mapping. The break-even point is reached at 2 hours of eye tracking recording for the total procedure, respectively 1 hour considering human working time only. Together with a real-time capability of the mapping process after completed training, even hours of eye tracking recording can be evaluated efficiently. (Code and video examples have been made available at: https://gitlab.ethz.ch/pdz/cgom.git)

We study the entanglement evolution of photonic orbital angular momentum qubit states with opposite azimuthal indices l0, in a weakly turbulent atmosphere. Using asymptotic methods, we deduce analytical expressions for the amplitude of turbulence-induced crosstalk between the modes l0 and −l0. Furthermore, we analytically establish distinct, universal entanglement decay laws for Kolmogorov's turbulence model and for two approximations thereof.

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We carry out a numerical analysis of the spatial structure of the eigenmodes of light in atmospheric turbulence and assess the distribution of the singular values under variable turbulence conditions characterized by the Fried parameter and Rytov variance. Under weak scintillation, the highly transmitting eigenmodes found here possess a modal structure that is reminiscent of Laguerre-Gaussian (LG) modes and their simple superpositions. When scintillation becomes significant, we establish that the optimal eigenmodes for communication differ substantially from LG modes and tend to have highly localized transverse intensity distributions.