Algebroid Yang-Mills Theories

A framework for constructing new kinds of gauge theories is suggested. Essentially it consists in replacing Lie algebras by Lie or Courant algebroids. Besides presenting novel topological theories defined in arbitrary spacetime dimensions, we show that equipping Lie algebroids E with a fiber metric having sufficiently many E-Killing vectors leads to an astonishingly mild deformation of ordinary Yang-Mills theories: Additional fields turn out to carry no propagating modes. Instead they serve as moduli parameters gluing together in part different Yang-Mills theories. This leads to a symmetry enhancement at critical points of these fields, as is also typical for String effective field theories.Comment: 4 pages; v3: Minor rewording of v1, version to appear in Phys. Rev. Let

Evaluating Microarray-based Classifiers: An Overview

For the last eight years, microarray-based class prediction has been the subject of numerous publications in medicine, bioinformatics and statistics journals. However, in many articles, the assessment of classification accuracy is carried out using suboptimal procedures and is not paid much attention. In this paper, we carefully review various statistical aspects of classifier evaluation and validation from a practical point of view. The main topics addressed are accuracy measures, error rate estimation procedures, variable selection, choice of classifiers and validation strategy

Nuclear magnetic resonance measurements reveal the origin of the Debye process in monohydroxy alcohols

Monohydroxy alcohols show a structural relaxation and at longer time scales a Debye-type dielectric peak. From spin-lattice relaxation experiments using different nuclear probes an intermediate, slower-than-structural dynamics is identified for n-butanol. Based on these findings and on diffusion measurements, a model of self-restructuring, transient chains is proposed. The model is demonstrated to explain consistently the so far puzzling observations made for this class of hydrogen-bonded glass forming liquids.Comment: 4 pages, 4 figure

Generalized 2d dilaton gravity with matter fields

We extend the classical integrability of the CGHS model of 2d dilaton gravity [1] to a larger class of models, allowing the gravitational part of the action to depend more generally on the dilaton field and, simultaneously, adding fermion- and U(1)-gauge-fields to the scalar matter. On the other hand we provide the complete solution of the most general dilaton-dependent 2d gravity action coupled to chiral fermions. The latter analysis is generalized to a chiral fermion multiplet with a non-abelian gauge symmetry as well as to the (anti-)self-dual sector df = *df (df = -*df) of a scalar field f.Comment: 37 pages, Latex; typos and Eqs. (44,45) corrected; paragraph on p. 26, referring to a work of S. Solodukhin, reformulated; references adde

Two-dimensional interactions between a BF-type theory and a collection of vector fields

Consistent interactions that can be added to a two-dimensional, free abelian gauge theory comprising a special class of BF-type models and a collection of vector fields are constructed from the deformation of the solution to the master equation based on specific cohomological techniques. The deformation procedure modifies the Lagrangian action, the gauge transformations, as well as the accompanying algebra of the interacting model.Comment: LaTeX 2e, 31 page

Maximum-likelihood absorption tomography

Maximum-likelihood methods are applied to the problem of absorption tomography. The reconstruction is done with the help of an iterative algorithm. We show how the statistics of the illuminating beam can be incorporated into the reconstruction. The proposed reconstruction method can be considered as a useful alternative in the extreme cases where the standard ill-posed direct-inversion methods fail.Comment: 7 pages, 5 figure

Classical and Quantum Gravity in 1+1 Dimensions, Part I: A Unifying Approach

We provide a concise approach to generalized dilaton theories with and without torsion and coupling to Yang-Mills fields. Transformations on the space of fields are used to trivialize the field equations locally. In this way their solution becomes accessible within a few lines of calculation only. In this first of a series of papers we set the stage for a thorough global investigation of classical and quantum aspects of more or less all available 2D gravity-Yang-Mills models.Comment: 24 pages, no figures, some sign errors in Eqs. 52--59 have been corrected (according to the Erratum

The Feature Importance Ranking Measure

Most accurate predictions are typically obtained by learning machines with complex feature spaces (as e.g. induced by kernels). Unfortunately, such decision rules are hardly accessible to humans and cannot easily be used to gain insights about the application domain. Therefore, one often resorts to linear models in combination with variable selection, thereby sacrificing some predictive power for presumptive interpretability. Here, we introduce the Feature Importance Ranking Measure (FIRM), which by retrospective analysis of arbitrary learning machines allows to achieve both excellent predictive performance and superior interpretation. In contrast to standard raw feature weighting, FIRM takes the underlying correlation structure of the features into account. Thereby, it is able to discover the most relevant features, even if their appearance in the training data is entirely prevented by noise. The desirable properties of FIRM are investigated analytically and illustrated in simulations.Comment: 15 pages, 3 figures. to appear in the Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 200

Populism and inequality: Does reality match the populist rhetoric?

Populists since the Roman Republic have argued for redistribution from an elite to ordinary people and depicted themselves as the true representative of the ‘people’. However, very little research has explored whether populists actually affect the distribution of income or consumption when in power. The present paper therefore asks, whether populists admin- istrations actually achieve redistribution. After a short theoretical discussion, our empirical strategy combines new data on populism in Latin America and the Caribbean with infor- mation on income and consumption inequality since 1970. Estimates suggest that populist governments in the region generally have achieved no redistribution, leading us to con- clude that the redistributive aims of populists are mainly empty rhetoric

An AUC-based Permutation Variable Importance Measure for Random Forests

The random forest (RF) method is a commonly used tool for classification with high dimensional data as well as for ranking candidate predictors based on the so-called random forest variable importance measures (VIMs). However the classification performance of RF is known to be suboptimal in case of strongly unbalanced data, i.e. data where response class sizes differ considerably. Suggestions were made to obtain better classification performance based either on sampling procedures or on cost sensitivity analyses. However to our knowledge the performance of the VIMs has not yet been examined in the case of unbalanced response classes. In this paper we explore the performance of the permutation VIM for unbalanced data settings and introduce an alternative permutation VIM based on the area under the curve (AUC) that is expected to be more robust towards class imbalance. We investigated the performance of the standard permutation VIM and of our novel AUC-based permutation VIM for different class imbalance levels using simulated data and real data. The results suggest that the standard permutation VIM loses its ability to discriminate between associated predictors and predictors not associated with the response for increasing class imbalance. It is outperformed by our new AUC-based permutation VIM for unbalanced data settings, while the performance of both VIMs is very similar in the case of balanced classes. The new AUC-based VIM is implemented in the R package party for the unbiased RF variant based on conditional inference trees. The codes implementing our study are available from the companion website: http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/070_drittmittel/janitza/index.html