10,252 research outputs found

    Fiscal and Migration Competition

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    It is often argued that tax competition may lead to a ‘race to the bottom’. This result may indeed hold in the case of factor mobility (such as capital). However, in this paper we emphasize the unique feature of labor migration, that may nullify the’race to the bottom’ hypothesis. Labor migration is governed not only by net-of-tax factor rewards, but rather importantly also by the benefits that the welfare state provides. The paper analyzes fiscal competition with and without migration in a two-country, political-economy, model with labor of different skills. The paper assigns an active fiscal role for both the host and the source countries. It models the host country stylistically as a core EU welfare state, with tax financed benefits and migration policies, and the migration source country as an accession country (following the EU enlargement to 27 states), with its own welfare (tax-benefit) policy. We let these two asymmetric countries (in terms of their productivity) engage in fiscal competition. Using numerical simulations we examine how the migration and tax policies are shaped, and how they are affected by whether the skilled or the unskilled are in power. As the driving force behind migration is a productivity gap, we also analyze the implications of the productivity gap for the design of migration and tax policies.

    Learning single-image 3D reconstruction by generative modelling of shape, pose and shading

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    We present a unified framework tackling two problems: class-specific 3D reconstruction from a single image, and generation of new 3D shape samples. These tasks have received considerable attention recently; however, most existing approaches rely on 3D supervision, annotation of 2D images with keypoints or poses, and/or training with multiple views of each object instance. Our framework is very general: it can be trained in similar settings to existing approaches, while also supporting weaker supervision. Importantly, it can be trained purely from 2D images, without pose annotations, and with only a single view per instance. We employ meshes as an output representation, instead of voxels used in most prior work. This allows us to reason over lighting parameters and exploit shading information during training, which previous 2D-supervised methods cannot. Thus, our method can learn to generate and reconstruct concave object classes. We evaluate our approach in various settings, showing that: (i) it learns to disentangle shape from pose and lighting; (ii) using shading in the loss improves performance compared to just silhouettes; (iii) when using a standard single white light, our model outperforms state-of-the-art 2D-supervised methods, both with and without pose supervision, thanks to exploiting shading cues; (iv) performance improves further when using multiple coloured lights, even approaching that of state-of-the-art 3D-supervised methods; (v) shapes produced by our model capture smooth surfaces and fine details better than voxel-based approaches; and (vi) our approach supports concave classes such as bathtubs and sofas, which methods based on silhouettes cannot learn.Comment: Extension of arXiv:1807.09259, accepted to IJCV. Differentiable renderer available at https://github.com/pmh47/dir

    Night and Fog in Japan: Fifty Years On

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    Paula Rego: printmaker

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    ‘Paula Rego: Printmaker’ is an extended essay commissioned from the author by Marlborough Fine Art and Talbot Rice Museum, Edinburgh, for the illustrated catalogue to accompany the touring exhibition of Rego’s graphic work. My essay reveals Rego’s working method, and places her prints within the overall context of her practice. The essay contains much original research evidenced over a 20-year period of collaboration with the artist, including first hand observation and hitherto unpublished stage proofs revealing the progression and development of images from first stage to final print
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