The ion energy-angle distribution (IEAD) at the wall of a magnetized plasma is of fundamental importance for the determination of the material processes occurring at the plasma-material interface, comprising secondary emissions and material sputtering. Here, we present a numerical characterization of the IEAD at the wall of a weakly collisional magnetized plasma with the magnetic field inclined at an arbitrary angle with respect to the wall. The analysis has been done using two different techniques: (1) a fluid-Monte Carlo method, and (2) particle-in-cell simulations, the former offering a fast but approximate method for the determination of the IEADs, the latter giving a computationally intensive but self-consistent treatment of the plasma behavior from the quasi-neutral region to the material boundary. The two models predict similar IEADs, whose similarities and differences are discussed. Data are presented for magnetic fields inclined at angles from normal to grazing incidence (0°–85°). We show the scaling factors of the average and peak ion energy and trends of the pitch angle at the wall as a function of the magnetic angle, for use in the correlation of fluid plasma models to material models.

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This article describes experimental evidence that the magnetic presheath is a fully three-dimensional structure modified by ion–neutral collisions. Velocity distributions of both ions and neutrals, obtained via laser-induced fluorescence, show that cross field ion drifts do not result from entrainment of ions in a flowing neutral background. Ion flows parallel to E×B arise and accelerate to as much as 0.2cs within several ion gyroradii of the boundary surface, where cs is the sound speed. Within measurement resolution, the onset of the E×B aligned flow occurs at the same distance to the surface that ions begin to deflect from travel along magnetic field lines. Collisional fluid and particle-in-cell simulations of the boundary region are compared to the experimental measurements. We find that, in contrast to the classical collisionless Chodura model, collisional effects between the ions and the non-flowing neutral population are essential to quantitatively predict the observed ion drift velocities. No momentum coupling between ions and neutrals, separable from noise and other effects, is observed in either signal. We discuss several explanations and implications of this observation.

Large-scale conversational assistants such as Cortana, Alexa, Google Assistant and Siri process requests through a series of modules for wake word detection, speech recognition, language understanding and response generation. An error in one of these modules can cascade through the system. Given the large traffic volumes in these assistants, it is infeasible to manually analyze the data, identify requests with processing errors and isolate the source of error. We present a machine learning system to address this challenge. First, we embed the incoming request and context, such as system response and subsequent turns, using pretrained transformer models. Then, we combine these embeddings with encodings of additional metadata features (such as confidence scores from different modules in the online system) using a "mixing-encoder" to output the failure point predictions. Our system obtains 92.2% of human performance on this task while scaling to analyze the entire traffic in 8 different languages of a large-scale conversational assistant. We present detailed ablation studies analyzing the impact of different modeling choices.

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