Labor Pooling in R&D Intensive Industries

We investigate firms’ incentives to locate in the same region to gain access to a large pool of skilled labor. Firms engage in risky R&D activities and thus create stochastic product and implied labor demand. Agglomeration in a cluster is more likely in situations where the innovation step is large and the probability for a firm to be the only innovator is high. When firms cluster, they tend to invest more and take more risk in R&D compared to spatially dispersed firms. Agglomeration is welfare maximizing, because expected labor productivity is higher and firms choose a more efficient, technically diversified portfolio of R&D projects at the industry level.

TBI Contusion Segmentation from MRI using Convolutional Neural Networks

Traumatic brain injury (TBI) is caused by a sudden trauma to the head that may result in hematomas and contusions and can lead to stroke or chronic disability. An accurate quantification of the lesion volumes and their locations is essential to understand the pathophysiology of TBI and its progression. In this paper, we propose a fully convolutional neural network (CNN) model to segment contusions and lesions from brain magnetic resonance (MR) images of patients with TBI. The CNN architecture proposed here was based on a state of the art CNN architecture from Google, called Inception. Using a 3-layer Inception network, lesions are segmented from multi-contrast MR images. When compared with two recent TBI lesion segmentation methods, one based on CNN (called DeepMedic) and another based on random forests, the proposed algorithm showed improved segmentation accuracy on images of 18 patients with mild to severe TBI. Using a leave-one-out cross validation, the proposed model achieved a median Dice of 0.75, which was significantly better (p<0.01) than the two competing methods.Comment: https://ieeexplore.ieee.org/abstract/document/8363545/, IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018