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This work studies the problem of predicting human intent to interact with a robot in a public environment. To facilitate research in this problem domain, we first contribute the People Approaching Robots Database (PAR-D), a new collection of datasets for intent prediction in Human-Robot Interaction. The database includes a subset of the ATC Approach Trajectory dataset [28] with augmented ground truth labels. It also includes two new datasets collected with a robot photographer on two locations of a university campus. Then, we contribute a novel human-annotated baseline for predicting intent. Our results suggest that the robot’s environment and the amount of time that a person is visible impacts human performance in this prediction task. We also provide computational baselines for intent prediction in PAR-D by comparing the performance of several machine learning models, including ones that directly model pedestrian interaction intent and others that predict motion trajectories as an intermediary step. From these models, we find that trajectory prediction seems useful for inferring intent to interact with a robot in a public environment. 

Graph-structured data is ubiquitous among a plethora of real-world applications. However, as graph learning algorithms have been increasingly deployed to help decision-making, there has been rising societal concern in the bias these algorithms may exhibit. In certain high-stake decision-making scenarios, the decisions made may be life-changing for the involved individuals. Accordingly, abundant explorations have been made to mitigate the bias for graph learning algorithms in recent years. However, there still lacks a library to collectively consolidate existing debiasing techniques and help practitioners to easily perform bias mitigation for graph learning algorithms. In this paper, we present PyGDebias, an open-source Python library for bias mitigation in graph learning algorithms. As the first comprehensive library of its kind, PyGDebias covers 13 popular debiasing methods under common fairness notions together with 26 commonly used graph datasets. In addition, PyGDebias also comes with comprehensive performance benchmarks and well-documented API designs for both researchers and practitioners. To foster convenient accessibility, PyGDebias is released under a permissive BSD-license together with performance benchmarks, API documentation, and use examples at https://github.com/yushundong/PyGDebias. 

In this work, β-Ga 2 O 3 fin field-effect transistors (FinFETs) with metalorganic chemical vapor deposition grown epitaxial Si-doped channel layer on (010) semi-insulating β-Ga 2 O 3 substrates are demonstrated. β-Ga 2 O 3 fin channels with smooth sidewalls are produced by the plasma-free metal-assisted chemical etching (MacEtch) method. A specific on-resistance (R on,sp ) of 6.5 mΩ·cm 2 and a 370 V breakdown voltage are achieved. In addition, these MacEtch-formed FinFETs demonstrate DC transfer characteristics with near zero (9.7 mV) hysteresis. The effect of channel orientation on threshold voltage, subthreshold swing, hysteresis, and breakdown voltages is also characterized. The FinFET with channel perpendicular to the [102] direction is found to exhibit the lowest subthreshold swing and hysteresis.