Read All About It! Understanding the Role of Media in Economic Development

This paper explores the role of media in economic development. In particular, we seek to outline the conditions under which the media contributes to the successful adoption of policies aimed at economic progress. Our core thesis is that successful economic development requires the coordination of efforts by politicians with the interests of the populace on policies that bring about economic growth. However, the nature of the relationship between political actors in charge of reform is characterized by a conflict of interests. The role of media as a key mechanism for transforming these situations of conflict into situations of coordination between politicians and the populace is analyzed. Specifically, we consider four factors - media autonomy, legal structure, quality of the media and consumer demand - and how they impact media as a coordination-enhancing mechanism. Copyright WWZ and Helbing & Lichtenhahn Verlag AG 2004.

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low-and high-grade glioma patients-manually annotated by up to four raters-and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.This research was supported by the NIH NCRR (P41-RR14075), the NIH NIBIB (R01EB013565), the Academy of Finland (133611), TEKES (ComBrain), the Lundbeck Foundation (R141-2013-13117), the Swiss Cancer League, the Swiss Institute for Computer Assisted Surgery (SICAS), the NIH NIBIB NAMIC (U54-EB005149), the NIH NCRR NAC (P41-RR13218), the NIH NIBIB NAC (P41-EB-015902), the NIH NCI (R15CA115464), the European Research Council through the ERC Advanced Grant MedYMA 2011-291080 (on Biophysical Modeling and Analysis of Dynamic Medical Images), the FCT and COMPETE (FCOM-01-0124-FEDER-022674), the MICAT Project (EU FP7 Marie Curie Grant No. PIRG-GA-2008-231052), the European Union Seventh Framework Programme under grant agreement no. 600841, the Swiss NSF project Computer Aided and Image Guided Medical Interventions (NCCR CO-ME), the Technische Universitat Munchen-Institute for Advanced Study (funded by the German Excellence Initiative and the European Union Seventh Framework Programme under Grant agreement 291763), the Marie Curie COFUND program of the European Union (Rudolf Mossbauer Tenure-Track Professorship to BHM).info:eu-repo/semantics/publishedVersio