Fünfhaus im Umbruch?

Die vorliegende Arbeit widmet sich der Frage nach der Rolle von künstlerischen, kulturellen und kreativwirtschaftlichen Nutzungen in Zielgebieten der Stadterneuerung. Ausgangspunkt ist dabei der aktuell oft diskutierte Begriff der „Kreativen Stadt“ und seine Bedeutung für die postindustrielle, mitteleuropäische Stadt der Dienstleistungs- und Wissensgesellschaft. Unter Miteinbeziehung der Themenfelder Stadterneuerung und Gentrification analysiert die Arbeit das Potential eines Wiener Außenbezirks. Das Untersuchungsgebiet steht stellvertretend für zahlreiche Viertel deren Erscheinungsbild zwischen vorrangiger Wohnnutzung, spannendem Szeneviertel oder zunehmendem Verfall schwankt. Im Rahmen einer Kartierung wurden 2011 sämtliche Häuser und deren Erdgeschoß- und Hinterhofnutzungen sowie die Nutzungen umfunktionierter Industriegebäude erhoben. Diese Kartierung stellt die Verteilung der künstlerischen, kulturellen und kreativwirtschaftlichen Nutzungen sowie der sich durch Leerstände ergebenden Potentialräume dar. Die Berücksichtigung soziodemographischer Daten ermöglicht ein differenziertes Bild der Unterschiede einzelner Viertel des Untersuchungsgebiets, Interviews mit den BetreiberInnen von Kulturnutzungen geben Einblick in die Faktoren der Standortwahl und deren Sicht des Gebiets. Die Arbeit liefert eine Beschreibung der Bebauungs- und Nutzungsstruktur des Viertels und zeigt existierende und potentielle Räume der Kulturnutzungen auf.This thesis discusses the role of art, culture and creative industries in urban renewal processes. The “Creative City” has been the focus of various urban sciences, debating the future of post industrial cities in an service and knowledge based economy. Considering theories of urban renewal and gentrification the thesis analyses the places used for artistic, cultural or creative operations in a part of the 15th Viennese district. This district represents an urban quarter between the centre and the outskirts of a european city and between different possible developments such as increased residental function, becoming a hotspot for young people or urban decay. Mapping the structures of the buildings as well as the functions of the ground floor, the backyards and the industrial buildings allows for various conclusions regarding the different atmospheres found in the district. The map also points out the distribution of premises used for art, culture and creative industries and shows the amount of unused spaces on the ground floor. Available demographic data offers further knowledge of the area. Interviews with the people operating cultural or creative enterprises give some insight in their choice of location and how they observe the district. The thesis describes patterns of use and shows existing and potential spaces for cultural and creative developments

Iterative convergent computation may not be a useful inductive bias for residual neural networks

Recent work has suggested that feedforward residual neural networks (ResNets) approximate iterative recurrent computations. Iterative computations are useful in many domains, so they might provide good solutions for neural networks to learn. Here we quantify the degree to which ResNets learn iterative solutions and introduce a regularization approach that encourages learning of iterative solutions. Iterative methods are characterized by two properties: iteration and convergence. To quantify these properties, we define three indices of iterative convergence. Consistent with previous work, we show that, even though ResNets can express iterative solutions, they do not learn them when trained conventionally on computer vision tasks. We then introduce regularizations to encourage iterative convergent computation and test whether this provides a useful inductive bias. To make the networks more iterative, we manipulate the degree of weight sharing across layers using soft gradient coupling. This new method provides a form of recurrence regularization and can interpolate smoothly between an ordinary ResNet and a “recurrent” ResNet (i.e., one that uses identical weights across layers and thus could be physically implemented with a recurrent network computing the successive stages iteratively across time). To make the networks more convergent we impose a Lipschitz constraint on the residual functions using spectral normalization. The three indices of iterative convergence reveal that the gradient coupling and the Lipschitz constraint succeed at making the networks iterative and convergent, respectively. However, neither recurrence regularization nor spectral normalization improve classification accuracy on standard visual recognition tasks (MNIST, CIFAR-10, CIFAR-100) or on challenging recognition tasks with partial occlusions (Digitclutter). Iterative convergent computation, in these tasks, does not provide a useful inductive bias for ResNets

Low-noise supercontinuum generation in chiral all-normal dispersion photonic crystal fibers

We present the advantages of supercontinuum generation in chiral, therefore circularly birefringent, all-normal dispersion fibers. Due to the absence of nonlinear power transfer between the polarization eigenstates of the fiber, chiral all-normal dispersion fibers do not exhibit any polarization instabilities and thus are an ideal platform for a low-noise supercontinuum generation. By pumping a chiral all-normal dispersion fiber at 802 nm, we obtained an octave-spanning, robustly circularly polarized supercontinuum with a low noise

Can neural networks benefit from objectives that encourage iterative convergent computations? A case study of ResNets and object classification

Recent work has suggested that feedforward residual neural networks (ResNets) approximate iterative recurrent computations. Iterative computations are useful in many domains, so they might provide good solutions for neural networks to learn. However, principled methods for measuring and manipulating iterative convergence in neural networks remain lacking. Here we address this gap by 1) quantifying the degree to which ResNets learn iterative solutions and 2) introducing a regularization approach that encourages the learning of iterative solutions. Iterative methods are characterized by two properties: iteration and convergence. To quantify these properties, we define three indices of iterative convergence. Consistent with previous work, we show that, even though ResNets can express iterative solutions, they do not learn them when trained conventionally on computer-vision tasks. We then introduce regularizations to encourage iterative convergent computation and test whether this provides a useful inductive bias. To make the networks more iterative, we manipulate the degree of weight sharing across layers using soft gradient coupling. This new method provides a form of recurrence regularization and can interpolate smoothly between an ordinary ResNet and a "recurrent"ResNet (i.e., one that uses identical weights across layers and thus could be physically implemented with a recurrent network computing the successive stages iteratively across time). To make the networks more convergent we impose a Lipschitz constraint on the residual functions using spectral normalization. The three indices of iterative convergence reveal that the gradient coupling and the Lipschitz constraint succeed at making the networks iterative and convergent, respectively. To showcase the practicality of our approach, we study how iterative convergence impacts generalization on standard visual recognition tasks (MNIST, CIFAR-10, CIFAR-100) or challenging recognition tasks with partial occlusions (Digitclutter). We find that iterative convergent computation, in these tasks, does not provide a useful inductive bias for ResNets. Importantly, our approach may be useful for investigating other network architectures and tasks as well and we hope that our study provides a useful starting point for investigating the broader question of whether iterative convergence can help neural networks in their generalization

Low-noise supercontinuum generation in chiral all-normal dispersion photonic crystal fibers

We present the advantages of supercontinuum generation in chiral, therefore circularly birefringent, all-normal dispersion fibers. Due to the absence of nonlinear power transfer between the polarization eigenstates of the fiber, chiral all-normal dispersion fibers do not exhibit any polarization instabilities and thus are an ideal platform for low-noise supercontinuum generation. By pumping a chiral all-normal dispersion fiber at 802 nm, we obtained an octave-spanning, robustly circularly polarized supercontinuum with low-noise.Comment: 4 pages, 5 figure

FACT: Federated Adversarial Cross Training

Federated Learning (FL) facilitates distributed model development to aggregate multiple confidential data sources. The information transfer among clients can be compromised by distributional differences, i.e., by non-i.i.d. data. A particularly challenging scenario is the federated model adaptation to a target client without access to annotated data. We propose Federated Adversarial Cross Training (FACT), which uses the implicit domain differences between source clients to identify domain shifts in the target domain. In each round of FL, FACT cross initializes a pair of source clients to generate domain specialized representations which are then used as a direct adversary to learn a domain invariant data representation. We empirically show that FACT outperforms state-of-the-art federated, non-federated and source-free domain adaptation models on three popular multi-source-single-target benchmarks, and state-of-the-art Unsupervised Domain Adaptation (UDA) models on single-source-single-target experiments. We further study FACT's behavior with respect to communication restrictions and the number of participating clients

Soziale Sicherheit durch den Sozialstaat? Einschätzungen zu Rente, Arbeitslosigkeit und Krankheit in Ost- und Westdeutschland

'Der Sozialstaat in Deutschland verfolgt das Ziel, den Wohlstand aller Bürgerinnen und Bürger zu fördern, indem er individuelle Lebensrisiken sozial absichert und die gesellschaftliche Teilhabe aller ermöglicht. Er legitimiert sich unter anderem aus einer breiten, überparteilichen und über die Zeit unverändert hohen Akzeptanz durch die Bevölkerung, die auch in bisherigen Umfragen nachgewiesen wurde. Allerdings könnte dieser Befund möglicherweise auch darauf zurückzuführen sein, dass sowohl Einstellungen bezüglich der Leistungen des Sozialstaats als auch normative Vorstellungen über seine Zuständigkeit für den Schutz vor Risiken in bestimmten Lebensbereichen in diesen Umfragen nur in relativ allgemeiner Form erhoben wurden. Vermutlich konnten deshalb Konflikte um die Aufgaben des Sozialstaates, die aus unterschiedlichen Interessenlagen und Werthaltungen verschiedener Bevölkerungsgruppen resultieren, in empirischen Untersuchungen bisher nicht ausreichend berücksichtigt werden. Im Rahmen des 'International Social Justice Project' (ISJP) wurde Ende 2000 eine deutschlandweit repräsentative Umfrage durchgeführt, in der einige sozialpolitisch relevante Einstellungen differenzierter erhoben wurden. Die Ergebnisse zeigen, dass sich Ost- und Westdeutsche in ihren sozialpolitischen Einstellungen auch noch zehn Jahre nach der Vereinigung deutlich unterscheiden und dass darüber hinaus auch Einstellungsunterschiede zwischen Bevölkerungsgruppen zu beobachten sind.' (Autorenreferat