Unilateral posterior crossbite has been considered as a risk factor for temporomandibular joint clicking, with conflicting findings. The aim of this study was to investigate a possible association between unilateral posterior crossbite and temporomandibular disk displacement with reduction, by means of a survey carried out in young adolescents recruited from three schools. The sample included 1291 participants (708 males and 583 females) with a mean age of 12.3 yrs (range, 10.1-16.1 yrs), who underwent an orthodontic and functional examination performed by two independent examiners. Unilateral posterior crossbite was found in 157 participants (12.2%). Fifty-three participants (4.1%) were diagnosed as having disk displacement with reduction. Logistic regression analysis failed to reveal a significant association between unilateral posterior crossbite and disk displacement with reduction (odds ratio = 1.3; confidence limits = 0.6-2.9). Posterior unilateral crossbite does not appear to be a risk factor for temporomandibular joint clicking, at least in young adolescents.
Explainable reinforcement learning (XRL) is an emerging subfield of explainable machine learning that has attracted considerable attention in recent years. The goal of XRL is to elucidate the decision-making process of learning agents in sequential decision-making settings. In this survey, we propose a novel taxonomy for organizing the XRL literature that prioritizes the RL setting. We overview techniques according to this taxonomy. We point out gaps in the literature, which we use to motivate and outline a roadmap for future work.
The last decade has seen a significant increase of interest in deep learning research, with many public successes that have demonstrated its potential. As such, these systems are now being incorporated into commercial products. With this comes an additional challenge: how can we build AI systems that solve tasks where there is not a crisp, well-defined specification? While multiple solutions have been proposed, in this competition we focus on one in particular: learning from human feedback. Rather than training AI systems using a predefined reward function or using a labeled dataset with a predefined set of categories, we instead train the AI system using a learning signal derived from some form of human feedback, which can evolve over time as the understanding of the task changes, or as the capabilities of the AI system improve.The MineRL BASALT competition aims to spur forward research on this important class of techniques. We design a suite of four tasks in Minecraft for which we expect it will be hard to write down hardcoded reward functions. These tasks are defined by a paragraph of natural language: for example, "create a waterfall and take a scenic picture of it", with additional clarifying details. Participants must train a separate agent for each task, using any method they want. Agents are then evaluated by humans who have read the task description. To help participants get started, we provide a dataset of human demonstrations on each of the four tasks, as well as an imitation learning baseline that leverages these demonstrations.
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