International audienceIn this paper we present the 2D Shape Structure database, a public, user-generated dataset of 2D shape decompositions into a hierarchy of shape parts with geometric relationships retained. It is the outcome of a large-scale user study obtained by crowdsourcing, involving over 1200 shapes in 70 shape classes, and 2861 participants. A total of 41953 annotations has been collected with at least 24 annotations per shape. For each shape, user decompositions into main shape, one or more levels of parts, and a level of details are available. This database reinforces a philosophy that understanding shape structure as a whole, rather than in the separated categories of parts decomposition, parts hierarchy, and analysis of relationships between parts, is crucial for full shape understanding. We provide initial statistical explorations of the data to determine representative (" mean ") shape annotations and to determine the number of modes in the annotations. The primary goal of the paper is to make this rich and complex database openly available (through the website http://2dshapesstructure.github.io/index.html), providing the shape community with a ground truth of human perception of holistic shape structure

This paper presents a multilevel analysis of 2D shapes and uses it to find similarities between the different parts of a shape. Such an analysis is important for many applications such as shape comparison, editing, and compression. Our robust and stable method decomposes a shape into parts, determines a parts hierarchy, and measures similarity between parts based on a salience measure on the medial axis, the Weighted Extended Distance Function, providing a multi-resolution partition of the shape that is stable across scale and articulation. Comparison with an extensive user study on the MPEG-7 database demonstrates that our geometric results are consistent with user perception.

Background: Understanding gender gaps in trainee evaluations is critical because these may ultimately determine the duration of training. Currently, no studies describe the influence of gender on the evaluation of pediatric emergency medicine (PEM) fellows. Objective:The objective of our study was to compare milestone scores of female versus male PEM fellows. Methods: This is a multicenter retrospective cohort study of a national sample of PEM fellows from July 2014 to June 2018. Accreditation Council for Medical Education (ACGME) subcompetencies are scored on a 5-point scale and span six domains: patient care (PC), medical knowledge, systems-based practice, practice-based learning and improvement, professionalism, and interpersonal and communication skills (ICS). Summative assessments of the 23 PEM subcompetencies are assigned by each program's clinical competency committee and submitted semiannually for each fellow. Program directors voluntarily provided deidentified ACGME milestone reports. Demographics including sex, program region, and type of residency were collected. Descriptive analysis of milestones was performed for each year of fellowship. Multivariate analyses evaluated the difference in scores by sex for each of the subcompetencies.Results: Forty-eight geographically diverse programs participated, yielding data for 639 fellows (66% of all PEM fellows nationally); sex was recorded for 604 fellows, of whom 67% were female. When comparing the mean milestone scores in each of the six domains, there were no differences by sex in any year of training. When comparing scores within each of the 23 subcompetencies and correcting the significance level for comparison of multiple milestones, the scores for PC3 and ICS2 were significantly, albeit not meaningfully, higher for females.

Figure 1: SkelNetOn Challenges: Example shapes and corresponding skeletons are demonstrated for the three challenge tracks in pixel (left), point (middle), and parametric domain (right). AbstractWe present SkelNetOn 2019 Challenge and Deep Learning for Geometric Shape Understanding workshop to utilize existing and develop novel deep learning architectures for shape understanding. We observed that unlike traditional segmentation and detection tasks, geometry understanding is still a new area for deep learning techniques. SkelNetOn aims to bring together researchers from different domains to foster learning methods on global shape understanding tasks. We aim to improve and evaluate the state-of-theart shape understanding approaches, and to serve as reference benchmarks for future research. Similar to other challenges in computer vision [6,22], SkelNetOn proposes three datasets and corresponding evaluation methodologies; all coherently bundled in three competitions with a dedicated workshop co-located with CVPR 2019 conference. In this paper, we describe and analyze characteristics of datasets, define the evaluation criteria of the public competitions, and provide baselines for each task.Computer vision approaches have shown tremendous progress toward understanding shapes from various data formats, especially since entering the deep learning era. Although detection, recognition, and segmentation approaches achieve highly accurate results, there has been rel-arXiv:1903.09233v3 [cs.CV]

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