Price discrimination and price sensitivity in the car market : working paper, comment welcome

The model in Verboven (2002) is extended to include non-zero price elasticities and behavior in the fuel market is modelled explicitly. With the aid of simulations it is shown, that this makes quite a difference and, therefore, might lead to bias in parameter estimates.

Designing Virtuous Sex Robots

We propose that virtue ethics can be used to address ethical issues central to discussions about sex robots. In particular, we argue virtue ethics is well equipped to focus on the implications of sex robots for human moral character. Our evaluation develops in four steps. First, we present virtue ethics as a suitable framework for the evaluation of human–robot relationships. Second, we show the advantages of our virtue ethical account of sex robots by comparing it to current instrumentalist approaches, showing how the former better captures the reciprocal interaction between robots and their users. Third, we examine how a virtue ethical analysis of intimate human–robot relationships could inspire the design of robots that support the cultivation of virtues. We suggest that a sex robot which is equipped with a consent-module could support the cultivation of compassion when used in supervised, therapeutic scenarios. Fourth, we discuss the ethical implications of our analysis for user autonomy and responsibility

Analysis of Feature Models using Generalised Feature Trees

This paper introduces the concept of generalised feature trees, which are feature trees where features can have multiple occurrences. It is shown how an important class of feature models can be transformed into generalised feature trees. We present algorithms which, after transforming a feature model to a generalised feature tree, compute properties of the corresponding software product line. We discuss the computational complexity of these algorithms and provide executable specifications in the functional programming language Miranda

Modelling Software Evolution using Algebraic Graph Rewriting

We show how evolution requests can be formalized using algebraic graph rewriting. In particular, we present a way to convert the UML class diagrams to colored graphs. Since changes in software may effect the relation between the methods of classes, our colored graph representation also employs the relations in UML interaction diagrams. Then, we provide a set of algebraic graph rewrite rules that formalizes the changes that may be caused by an evolution request, using the pushout construction in the category of marked colored graphs

Isointense infant brain MRI segmentation with a dilated convolutional neural network

Quantitative analysis of brain MRI at the age of 6 months is difficult because of the limited contrast between white matter and gray matter. In this study, we use a dilated triplanar convolutional neural network in combination with a non-dilated 3D convolutional neural network for the segmentation of white matter, gray matter and cerebrospinal fluid in infant brain MR images, as provided by the MICCAI grand challenge on 6-month infant brain MRI segmentation.Comment: MICCAI grand challenge on 6-month infant brain MRI segmentatio

Generic Security Proof of Quantum Key Exchange using Squeezed States

Recently, a Quantum Key Exchange protocol that uses squeezed states was presented by Gottesman and Preskill. In this paper we give a generic security proof for this protocol. The method used for this generic security proof is based on recent work by Christiandl, Renner and Ekert.Comment: 5 pages, 7 figures, accepted at IEEE ISIT 200

Convergence of rank based degree-degree correlations in random directed networks

We introduce, and analyze, three measures for degree-degree dependencies, also called degree assortativity, in directed random graphs, based on Spearman's rho and Kendall's tau. We proof statistical consistency of these measures in general random graphs and show that the directed configuration model can serve as a null model for our degree-degree dependency measures. Based on these results we argue that the measures we introduce should be preferred over Pearson's correlation coefficients, when studying degree-degree dependencies, since the latter has several issues in the case of large networks with scale-free degree distributions