Surgical approach to the facial recess influences the acceptable trajectory of cochlear implantation electrodes.

PURPOSE: To provide practical guidance to the operative surgeon by mapping the location, where acceptable straight-line virtual cochlear implant electrode trajectories intersect the facial recess. In addition, to investigate the influence of facial recess preparation, virtual electrode width and surgical approach to the cochlea on these available trajectories. METHODS: The study was performed on imaging data from eight cadaveric temporal bones within the University of Melbourne Virtual Reality (VR) Temporal Bone Surgery Simulator. The facial recess was opened to varying degrees, and acceptable trajectory vectors with varying diameters were calculated for electrode insertions via cochleostomy or round window membrane (RWM). The percentage of acceptable insertion vectors through each location of the facial recess was visually represented using heatmaps. RESULTS: Seven of the eight bones allowed for acceptable vector trajectories via both cochleostomy and RWM approaches. These acceptable trajectories were more likely to lie superiorly within the facial recess for insertion via the round window, and inferiorly for insertion via cochleostomy. Cochleostomy insertions required a greater degree of preparation and skeletonisation of the junction of the facial nerve and chorda tympani within the facial recess. The width of the virtual electrode had only marginal impact on the availability of acceptable trajectories. Heatmaps emphasised the intimate relationship the acceptable trajectories have with the facial nerve and chorda tympani. CONCLUSION: These findings highlight the differences in the acceptable straight-line trajectories for electrodes when implanted via the round window or cochleostomy. There were notable exceptions to both surgical approaches, likely explained by the variation of hook region anatomy. The methodology used in this study holds promise for translation to patient specific surgical planning.

Local Intrinsic Dimensionality, Entropy and Statistical Divergences.

Properties of data distributions can be assessed at both global and local scales. At a highly localized scale, a fundamental measure is the local intrinsic dimensionality (LID), which assesses growth rates of the cumulative distribution function within a restricted neighborhood and characterizes properties of the geometry of a local neighborhood. In this paper, we explore the connection of LID to other well known measures for complexity assessment and comparison, namely, entropy and statistical distances or divergences. In an asymptotic context, we develop analytical new expressions for these quantities in terms of LID. This reveals the fundamental nature of LID as a building block for characterizing and comparing data distributions, opening the door to new methods for distributional analysis at a local scale.

Privacy and Robustness in Federated Learning: Attacks and Defenses.

As data are increasingly being stored in different silos and societies becoming more aware of data privacy issues, the traditional centralized training of artificial intelligence (AI) models is facing efficiency and privacy challenges. Recently, federated learning (FL) has emerged as an alternative solution and continues to thrive in this new reality. Existing FL protocol designs have been shown to be vulnerable to adversaries within or outside of the system, compromising data privacy and system robustness. Besides training powerful global models, it is of paramount importance to design FL systems that have privacy guarantees and are resistant to different types of adversaries. In this article, we conduct a comprehensive survey on privacy and robustness in FL over the past five years. Through a concise introduction to the concept of FL and a unique taxonomy covering: 1) threat models; 2) privacy attacks and defenses; and 3) poisoning attacks and defenses, we provide an accessible review of this important topic. We highlight the intuitions, key techniques, and fundamental assumptions adopted by various attacks and defenses. Finally, we discuss promising future research directions toward robust and privacy-preserving FL, and their interplays with the multidisciplinary goals of FL.