Ethical Consumption: US/Leap Views on Fair Trade, Other Certification Programs, and Consumer Power

This article presents US/Leap’s views Fair Trade labeling schemes and their benefits to workers and consumers globally. Examples of effective certification labels and other tools that serve mainly as corporate marketing tools are included. US/Leap mostly takes the position that certification programs have not yet demonstrated that they are effective enough to ensure protection for worker rights and justice for workers

US/Leap Quarterly Newsletter, Issue #3

The US Labor Education in the Americas Project is an independent non-profit organization that supports the basic rights of workers in Latin America, especially those who are employed directly or indirectly by U.S. companies. This newsletter includes articles on the Latin American Banana Union’s action plan in their campaign with Dole, Maquila updates, and the Colombian flower workers’ strike

Accurate Liability Estimation Improves Power in Ascertained Case Control Studies

Linear mixed models (LMMs) have emerged as the method of choice for confounded genome-wide association studies. However, the performance of LMMs in non-randomly ascertained case-control studies deteriorates with increasing sample size. We propose a framework called LEAP (Liability Estimator As a Phenotype, https://github.com/omerwe/LEAP) that tests for association with estimated latent values corresponding to severity of phenotype, and demonstrate that this can lead to a substantial power increase

Hand gesture recognition with jointly calibrated Leap Motion and depth sensor

Novel 3D acquisition devices like depth cameras and the Leap Motion have recently reached the market. Depth cameras allow to obtain a complete 3D description of the framed scene while the Leap Motion sensor is a device explicitly targeted for hand gesture recognition and provides only a limited set of relevant points. This paper shows how to jointly exploit the two types of sensors for accurate gesture recognition. An ad-hoc solution for the joint calibration of the two devices is firstly presented. Then a set of novel feature descriptors is introduced both for the Leap Motion and for depth data. Various schemes based on the distances of the hand samples from the centroid, on the curvature of the hand contour and on the convex hull of the hand shape are employed and the use of Leap Motion data to aid feature extraction is also considered. The proposed feature sets are fed to two different classifiers, one based on multi-class SVMs and one exploiting Random Forests. Different feature selection algorithms have also been tested in order to reduce the complexity of the approach. Experimental results show that a very high accuracy can be obtained from the proposed method. The current implementation is also able to run in real-time