A comprehensible SOM-based scoring system.

The significant growth of consumer credit has resulted in a wide range of statistical and non-statistical methods for classifying applicants in 'good' and 'bad' risk categories. Traditionally, (logistic) regression used to be one of the most popular methods for this task, but recently some newer techniques like neural networks and support vector machines have shown excellent classification performance. Self-organizing maps (SOMs) have existed for decades and although they have been used in various application areas, only little research has been done to investigate their appropriateness for credit scoring. In this paper, it is shown how a trained SOM can be used for classification and how the basic SOM-algorithm can be integrated with supervised techniques like the multi-layered perceptron. Classification accuracy of the models is benchmarked with results reported previously.Decision; Knowledge; Knowledge discovery; Systems; Growth; Credit; Methods; Risk; Regression; Neural networks; Networks; Classification; Performance; Area; Research; Credit scoring; Models; Model;

Optical Flow on Evolving Surfaces with an Application to the Analysis of 4D Microscopy Data

We extend the concept of optical flow to a dynamic non-Euclidean setting. Optical flow is traditionally computed from a sequence of flat images. It is the purpose of this paper to introduce variational motion estimation for images that are defined on an evolving surface. Volumetric microscopy images depicting a live zebrafish embryo serve as both biological motivation and test data.Comment: The final publication is available at link.springer.co

The Glucuronyltransferase GlcAT-P Is Required for Stretch Growth of Peripheral Nerves in Drosophila

During development, the growth of the animal body is accompanied by a concomitant elongation of the peripheral nerves, which requires the elongation of integrated nerve fibers and the axons projecting therein. Although this process is of fundamental importance to almost all organisms of the animal kingdom, very little is known about the mechanisms regulating this process. Here, we describe the identification and characterization of novel mutant alleles of GlcAT-P, the Drosophila ortholog of the mammalian glucuronyltransferase b3gat1. GlcAT-P mutants reveal shorter larval peripheral nerves and an elongated ventral nerve cord (VNC). We show that GlcAT-P is expressed in a subset of neurons in the central brain hemispheres, in some motoneurons of the ventral nerve cord as well as in central and peripheral nerve glia. We demonstrate that in GlcAT-P mutants the VNC is under tension of shorter peripheral nerves suggesting that the VNC elongates as a consequence of tension imparted by retarded peripheral nerve growth during larval development. We also provide evidence that for growth of peripheral nerve fibers GlcAT-P is critically required in hemocytes; however, glial cells are also important in this process. The glial specific repo gene acts as a modifier of GlcAT-P and loss or reduction of repo function in a GlcAT-P mutant background enhances VNC elongation. We propose a model in which hemocytes are required for aspects of glial cell biology which in turn affects the elongation of peripheral nerves during larval development. Our data also identifies GlcAT-P as a first candidate gene involved in growth of integrated peripheral nerves and therefore establishes Drosophila as an amenable in-vivo model system to study this process at the cellular and molecular level in more detail

Marching cubes method with connectivity

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