Contestability and sunk costs: An analysis of product R&D competition

The paper shows that Bertrand competition and contestability can be reconciled with sunk costs. When average total costs are constant over a range of output, marginal cost pricing does not conflict with the budget constraint faced by firms. Empirical observations support the notion of constant average total costs. When average total costs are constant, there is no trade-off between dynamic economies and static efficiency. The argument is applied to product R&D competition.industrial organization ;

Learning effective color features for content based image retrieval in dermatology

We investigate the extraction of effective color features for a content-based image retrieval (CBIR) application in dermatology. Effectiveness is measured by the rate of correct retrieval of images from four color classes of skin lesions. We employ and compare two different methods to learn favorable feature representations for this special application: limited rank matrix learning vector quantization (LiRaM LVQ) and a Large Margin Nearest Neighbor (LMNN) approach. Both methods use labeled training data and provide a discriminant linear transformation of the original features, potentially to a lower dimensional space. The extracted color features are used to retrieve images from a database by a k-nearest neighbor search. We perform a comparison of retrieval rates achieved with extracted and original features for eight different standard color spaces. We achieved significant improvements in every examined color space. The increase of the mean correct retrieval rate lies between 10% and 27% in the range of k=1–25 retrieved images, and the correct retrieval rate lies between 84% and 64%. We present explicit combinations of RGB and CIE-Lab color features corresponding to healthy and lesion skin. LiRaM LVQ and the computationally more expensive LMNN give comparable results for large values of the method parameter κ of LMNN (κ≥25) while LiRaM LVQ outperforms LMNN for smaller values of κ. We conclude that feature extraction by LiRaM LVQ leads to considerable improvement in color-based retrieval of dermatologic images

GEO debris and interplanetary dust: fluxes and charging behavior

In September 1996, a dust/debris detector: GORID was launched into the geostationary (GEO) region as a piggyback instrument on the Russian Express-2 telecommunications spacecraft. The instrument began its normal operation in April 1997 and ended its mission in July 2002. The goal of this work was to use GORID's particle data to identify and separate the space debris to interplanetary dust particles (IDPs) in GEO, to more finely determine the instrument's measurement characteristics and to derive impact fluxes. While the physical characteristics of the GORID impacts alone are insufficient for a reliable distinction between debris and interplanetary dust, the temporal behavior of the impacts are strong enough indicators to separate the populations based on clustering. Non-cluster events are predominantly interplanetary, while cluster events are debris. The GORID mean flux distributions (at mass thresholds which are impact speed dependent) for IDPs, corrected for dead time, are 1.35x10^{-4} m^{-2} s^{-1} using a mean detection rate: 0.54 d^{-1}, and for space debris are 6.1x10^{-4} m^{-2} s^{-1} using a mean detection rate: 2.5 d^{-1}. Beta-meteoroids were not detected. Clusters could be a closely-packed debris cloud or a particle breaking up due to electrostatic fragmentation after high charging.Comment: * Comments: 6 pages, 4 postscript figures, in Dust in Planetary Systems 2005, Krueger, H. and Graps, A. eds., ESA Publications, SP in press (2006). For high resolution version, see: http://www.mpi-hd.mpg.de/dustgroup/~graps/dips2005/GrapsetalDIPS2005.pd

Mathematical Foundations of the Self Organized Neighbor Embedding ({SONE}) for Dimension Reduction and Visualization

Abstract. In this paper we propose the generalization of the recently introduced Neighbor Embedding Exploratory Observation Machine (NE-XOM) for dimension reduction and visualization. We provide a general mathematical framework called Self Organized Neighbor Embedding (SONE).Ittreatsthecomponents, likedatasimilarity measures andneighborhood functions, independently and easily changeable. And it enables the utilization of different divergences, based on the theory of Fréchet derivatives. In this way we propose a new dimension reduction and visualization algorithm, which can be easily adapted to the user specific request and the actual problem.