5,236 research outputs found

    Learning a Hierarchical Latent-Variable Model of 3D Shapes

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    We propose the Variational Shape Learner (VSL), a generative model that learns the underlying structure of voxelized 3D shapes in an unsupervised fashion. Through the use of skip-connections, our model can successfully learn and infer a latent, hierarchical representation of objects. Furthermore, realistic 3D objects can be easily generated by sampling the VSL's latent probabilistic manifold. We show that our generative model can be trained end-to-end from 2D images to perform single image 3D model retrieval. Experiments show, both quantitatively and qualitatively, the improved generalization of our proposed model over a range of tasks, performing better or comparable to various state-of-the-art alternatives.Comment: Accepted as oral presentation at International Conference on 3D Vision (3DV), 201

    My Ears Hear More Than English: Granting Multilingual Jurors Accommodations and Treating Multilingualism as a Common Type of Juror Expertise

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    To find an example of court-sanctioned discrimination against Spanish-speaking prospective jurors, one need not look further than the 2011 U.S. Court of Appeals for the Fourth Circuit decision in United States v. Cabrera-Beltran. Three multilingual jurors were struck for cause during voir dire for not agreeing to ignore all Spanish-language evidence that would be presented at trial and adhere solely to the English-language interpretation, even if they detected errors in the interpretation. Although these jurors could have been accommodated, the court upheld the decision to strike them. In other cases, jurors with other types of expertise typically have not been asked to abandon their expertise during a trial, nor have they been subject to for-cause challenges on the basis of their skills. This Note argues that multilingual jurors should be provided appropriate accommodations for their linguistic skills, and that they, like other expert jurors, should typically not be subject to for-cause challenges because they possess particular skills
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