The Labor Movement’s Framework for Comprehensive Immigration Reform

[Excerpt] Immigration reform is a component of a shared prosperity agenda that focuses on improving productivity and quality; limiting wage competition; strengthening labor standards, especially the freedom of workers to form unions and bargain collectively; and providing social safety nets and high-quality lifelong education and training for workers and their families. To achieve this goal, immigration reform must fully protect U.S. workers, reduce the exploitation of immigrant workers and reduce employers’ incentive to hire undocumented workers rather than U.S. workers. The most effective way to do that is for all workers— immigrant and native-born—to have full and complete access to the protection of labor, health and safety and other laws. Comprehensive immigration reform must complement a strong, well-resourced and effective labor standards enforcement initiative that prioritizes workers’ rights and workplace protections. This approach will ensure that immigration does not depress wages and working conditions or encourage marginal low-wage industries that depend heavily on substandard wages, benefits and working conditions

Instance-level Facial Attributes Transfer with Geometry-Aware Flow

We address the problem of instance-level facial attribute transfer without paired training data, e.g. faithfully transferring the exact mustache from a source face to a target face. This is a more challenging task than the conventional semantic-level attribute transfer, which only preserves the generic attribute style instead of instance-level traits. We propose the use of geometry-aware flow, which serves as a well-suited representation for modeling the transformation between instance-level facial attributes. Specifically, we leverage the facial landmarks as the geometric guidance to learn the differentiable flows automatically, despite of the large pose gap existed. Geometry-aware flow is able to warp the source face attribute into the target face context and generate a warp-and-blend result. To compensate for the potential appearance gap between source and target faces, we propose a hallucination sub-network that produces an appearance residual to further refine the warp-and-blend result. Finally, a cycle-consistency framework consisting of both attribute transfer module and attribute removal module is designed, so that abundant unpaired face images can be used as training data. Extensive evaluations validate the capability of our approach in transferring instance-level facial attributes faithfully across large pose and appearance gaps. Thanks to the flow representation, our approach can readily be applied to generate realistic details on high-resolution images.Comment: To appear in AAAI 2019. Code and models are available at: https://github.com/wdyin/GeoGA

ERM Quarterly, Quarter 3, October 2016

In this issue • Support instruments in focus: innovative business transfer measures • Case in focus: Major overhaul in Polish mining sector • Case in focus: Planned Caterpillar closure at Gosselie