Competition vs. Concatenation in Skip Connections of Fully Convolutional Networks

Increased information sharing through short and long-range skip connections between layers in fully convolutional networks have demonstrated significant improvement in performance for semantic segmentation. In this paper, we propose Competitive Dense Fully Convolutional Networks (CDFNet) by introducing competitive maxout activations in place of naive feature concatenation for inducing competition amongst layers. Within CDFNet, we propose two architectural contributions, namely competitive dense block (CDB) and competitive unpooling block (CUB) to induce competition at local and global scales for short and long-range skip connections respectively. This extension is demonstrated to boost learning of specialized sub-networks targeted at segmenting specific anatomies, which in turn eases the training of complex tasks. We present the proof-of-concept on the challenging task of whole body segmentation in the publicly available VISCERAL benchmark and demonstrate improved performance over multiple learning and registration based state-of-the-art methods.Comment: Paper accepted on MICCAI-MLMI 2018 worksho

Technological Antecedents of Entrepreneurship and its Consequences for Organizational Performance

Under conditions of rapid change, companies compete intensely to achieve competitive advantages. Through technology, they differentiate themselves from other companies to obtain a better market position. Decisions concerning technological variables are thus essential to a firm’s overall competitive strategy, positioning and emplacement. Our research analyses how top management support for technology and technology skills enables technology acquisition, integration and infrastructure in firms, influencing organizational performance through corporate entrepreneurship. The analysis is performed using Structural Equation Model with a sample of 201 Spanish technological firms. The results show that awareness of technological issues enables entrepreneurship in the firm.This research was supported by the Regional Government of Andalusia’s Project (Ref.: P11-SEJ-7988)