Report No. 3: Assessment of Possible Migration Pressure and its Labour Market Impact Following EU Enlargement to Central and Eastern Europe

Study for the Department for Education and Employment of the United Kingdom, Bonn 1999 (117 pages).

Serien ohne Modelle : Architektur gefühlsecht

Wissenschaftliches Kolloquium vom 27. bis 30. Juni 1996 in Weimar an der Bauhaus-Universität zum Thema: ‚Techno-Fiction. Zur Kritik der technologischen Utopien

Wolf Barth (1942--2016)

In this article we describe the life and work of Wolf Barth who died on 30th December 2016. Wolf Barth's contributions to algebraic variety span a wide range of subjects. His achievements range from what is now called the Barth-Lefschetz theorems to his fundamental contributions to the theory of algebraic surfaces and moduli of vector bundles, and include his later work on algebraic surfaces with many singularities, culminating in the famous Barth sextic.Comment: accepted for publication in Jahresbericht der Deutschen Mathematiker-Vereinigung, obituary, 17 pages, 2 figures, 1 phot

Development of a satellite SAR image spectra and altimeter wave height data assimilation system for ERS-1

The applicability of ERS-1 wind and wave data for wave models was studied using the WAM third generation wave model and SEASAT altimeter, scatterometer and SAR data. A series of global wave hindcasts is made for the surface stress and surface wind fields by assimilation of scatterometer data for the full 96-day SEASAT and also for two wind field analyses for shorter periods by assimilation with the higher resolution ECMWF T63 model and by subjective analysis methods. It is found that wave models respond very sensitively to inconsistencies in wind field analyses and therefore provide a valuable data validation tool. Comparisons between SEASAT SAR image spectra and theoretical SAR spectra derived from the hindcast wave spectra by Monte Carlo simulations yield good overall agreement for 32 cases representing a wide variety of wave conditions. It is concluded that SAR wave imaging is sufficiently well understood to apply SAR image spectra with confidence for wave studies if supported by realistic wave models and theoretical computations of the strongly nonlinear mapping of the wave spectrum into the SAR image spectrum. A closed nonlinear integral expression for this spectral mapping relation is derived which avoids the inherent statistical errors of Monte Carlo computations and may prove to be more efficient numerically

A multivariate approach for onset detection using supervised classification

In this paper we introduce a new onset detection approach which incorporates a supervised classification model for estimating the tone onset probability in signal frames. In contrast to the most classical strategies where only one detection function can be applied for signal feature extraction, the classification model can be fitted on a large feature set. This is meaningful since, depending on the music characteristics, some detection functions can be more advantageous that the others. Although the idea of the considering of many detection functions is not new in the literature, these functions are, so far, treated in a univariate way by, e.g., building of weighted sums. This probably lies on the difficulties of the direct transfer of the classification ideas to the onset detection task. The goodness measure of onset detection is namely based on the comparison of two time vectors while by the classification such a measure is derived from the framewise matches of predicted and true labels. In this work we first construct { based on several resent publications { a comprehensive univariate onset detection algorithm which depends on many free settable parameters. Then, the new multivariate approach also depending on many free parameters is introduced. The parameters of the both onset detection strategies are optimized for online and offline cases by utilizing an appropriate validation technique. The main funding is that the multivariate strategy outperforms the univariate one significantly regarding the F-measure. Furthermore, the multivariate approach seems to be especially beneficial in online case since it requires only the halve of the future signal information comparing to the best setting of the univariate onset detection

Model based optimization of music onset detection

In this paper a comprehensive online music onset detection algorithm is introduced where - in contrast to many other relevant publications - 14 important algorithm parameters are optimized simultaneously. For solving the optimization problem we derive an extensive tool for iterative model based optimization. In each iteration, a very time consuming evaluation has to be per- formed on a large music data base. To speed up this procedure, the expected performance of each newly proposed setting is estimated in a pretest on a representative part of the data so that just very promising points are evaluated on all data. We compare different variants of the classical and the fast optimization strategies with respect to the F-values of their best identified parameter settings. The performance of the fast approach appears to be competitive with the classical one while saving more than 80% of music piece evaluations on average. Our best found parameter settings, both for online and offline onset detection, are mainly in accordance with the usual choices in the state- of-the art literature concerning, e.g., the spectral flux detection function or preferences for window length and overlap. However, we also found unexpected results. For example, the adaptive whitening pre-processing step showed no effect

Self-Supervised Training with Autoencoders for Visual Anomaly Detection

Deep autoencoders provide an effective tool for learning non-linear dimensionality reduction in an unsupervised way. Recently, they have been used for the task of anomaly detection in the visual domain. By optimizing for the reconstruction error using anomaly-free examples, the common belief is that a corresponding network should fail to accurately reconstruct anomalous regions in the application phase. This goal is typically addressed by controlling the capacity of the network, either by reducing the size of the bottleneck layer or by enforcing sparsity constraints on the activations. However, neither of these techniques does explicitly penalize reconstruction of anomalous signals often resulting in poor detection. We tackle this problem by adapting a self-supervised learning regime that allows the use of discriminative information during training but focuses on the data manifold of normal examples. We emphasize that inference with our approach is very efficient during training and prediction requiring a single forward pass for each input image. Our experiments on the MVTec AD dataset demonstrate high detection and localization performance. On the texture-subset, in particular, our approach consistently outperforms recent anomaly detection methods by a significant margin