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We examine whether having previously been self-employed is a negative signal on the job market. In a UK field experiment where two applications of otherwise equally qualified individuals were sent out in response to the same vacancies in human resource management, we find that entrepreneurs systematically receive fewer responses than non-entrepreneurs. Empirical studies that treat market wages as the opportunity cost of remaining self-employed are therefore likely to overestimate alternative earnings to entrepreneurship.

Self-Employed but Looking: A Labor Market Experiment

In June 2018, iCog Labs held its second annual robosoccer competition which featured groups of humanoid robots playing soccer against each other. The authors were members of a team called upon to represent Nigeria with the University of Lagos at the competition which took place in Ethiopia. The work here presents a review of the approach taken to address the problem of automating robot coordination in real-world soccer applications. The design methodology relies on the Robot Operating System (ROS) as the platform upon which an asynchronous communication network between each robot and a central server is built. On the network, each robot is a node that consists of sub nodes for object detection and motion control. For object detection the work makes use of the you only look once (YOLO)v2 deep learning algorithm, and a simple decision-making algorithm for controlling vcv the robot based on the objects detected is devised. To quantify the object detection results, the common objects in context (COCO) evaluation metric is used. The results indicate an average recall and precision of 84% across different IOU. For qualitative results on the robot coordination in the ball's direction, a reference to the open-source implementation of the work has been provided.

Object detection for robot coordination in robotics soccer

Both polygenicity (i.e., many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of test statistic inflation in many GWAS of large sample size.

"I THINK I CAN, I THINK I CAN": OVERCONFIDENCE AND ENTREPRENEURIAL BEHAVIOR..

LD Score Regression Distinguishes Confounding from Polygenicity in Genome-Wide Association Studies

Abstract Both polygenicity 1,2 ( i.e. many small genetic effects) and confounding biases, such as cryptic relatedness 3 population stratification polygenicity , can yield inflated distributions of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from bias many true signal and polygenicity. We have developed an approach that quantifies the contributions and each by examining and relationship from and confounding the linkage disequilibrium (LD). can term this of LD Score regression. of of regression provides Score upper bound on LD contribution of test test to test observed statistics statistics in in between inflation be used inflation estimate a more powerful correction factor than genomic control 4–14 . approach find strong evidence bias to accounts for We majority We an statistic that the the GWAS the large sample size.