In most cases, prosthetic rehabilitation of patients suffering from microstomia is inconvenient for the patient and challenging for both the dentist and dental technician. In such cases, conventional impression-taking requires an individualized tray design and modified impression techniques, amplifying the risk for impression and cast deformation and thereby compromising the final outcome. An intraoral scanner (IOS) might overcome these limitations due to its reduced size compared to an impression tray. In the present case history report, a microstomia patient was successfully restored with a telescopic prosthesis by digitizing both retaining teeth and the palate with an IOS.
Abstract. An increasing number of networked applications, like video conference and video-on-demand, benefit from knowledge about Internet path measures like available bandwidth. Server selection and placement of infrastructure nodes based on accurate information about network conditions help to improve the quality-of-service of these systems. Acquiring this knowledge usually requires fully-meshed ad-hoc measurements. These, however, introduce a large overhead and a possible delay in communication establishment. Thus, prediction-based approaches like Sequoia have been proposed, which treat path properties as a semimetric and embed them onto trees, leveraging labelling schemes to predict distances between hosts not measured before. In this paper, we identify asymmetry as a cause of serious distortion in these systems causing inaccurate prediction. We study the impact of asymmetric network conditions on the accuracy of existing tree-embedding approaches, and present direction-aware embedding, a novel scheme that separates upstream from downstream properties of hosts and significantly improves the prediction accuracy for highly asymmetric datasets. This is achieved by embedding nodes for each direction separately and constraining the distance calculation to inversely labelled nodes. We evaluate the effectiveness and trade-offs of our approach using synthetic as well as real-world datasets.
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