Understanding task inter-dependence and co-ordination efforts in multi-sourcing: the suppliers' perspective

The last decade has witnessed a significant growth in the outsourcing of information technologies and business processes. Of a particular trend within the outsourcing industry is the shift from the client firm contracting a single supplier to utilizing multiple suppliers, which is also known as multi-sourcing. Multi-sourcing may potentially offer numerous advantages to client firms, however, it might present some challenges to suppliers. In particular, multi-sourcing could create coordination challenges, as there are inter-dependencies between the outsourced tasks to numerous suppliers. While the current outsourcing literature acknowledges the existence of inter-dependencies, little is known about the efforts required for coordinating the work between suppliers and how these coordination efforts are made to manage task inter-dependence. Three case studies at Pactera (case one) and TCS (cases two and three) serve as the empirical base to investigate the inter-dependence between outsourced tasks and suppliers coordination efforts. This research offers theoretical contributions to both coordination studies and the outsourcing body of knowledge

MDU-Net: Multi-scale Densely Connected U-Net for biomedical image segmentation

Radiologist is "doctor's doctor", biomedical image segmentation plays a central role in quantitative analysis, clinical diagnosis, and medical intervention. In the light of the fully convolutional networks (FCN) and U-Net, deep convolutional networks (DNNs) have made significant contributions in biomedical image segmentation applications. In this paper, based on U-Net, we propose MDUnet, a multi-scale densely connected U-net for biomedical image segmentation. we propose three different multi-scale dense connections for U shaped architectures encoder, decoder and across them. The highlights of our architecture is directly fuses the neighboring different scale feature maps from both higher layers and lower layers to strengthen feature propagation in current layer. Which can largely improves the information flow encoder, decoder and across them. Multi-scale dense connections, which means containing shorter connections between layers close to the input and output, also makes much deeper U-net possible. We adopt the optimal model based on the experiment and propose a novel Multi-scale Dense U-Net (MDU-Net) architecture with quantization. Which reduce overfitting in MDU-Net for better accuracy. We evaluate our purpose model on the MICCAI 2015 Gland Segmentation dataset (GlaS). The three multi-scale dense connections improve U-net performance by up to 1.8% on test A and 3.5% on test B in the MICCAI Gland dataset. Meanwhile the MDU-net with quantization achieves the superiority over U-Net performance by up to 3% on test A and 4.1% on test B.Comment: 10 pages, 6 figures, 6 Tabl