Frankophonie im Wandel : Alphabetisierung für frankophone Kanadier oder Migranten aus französischsprachigen Ländern?

Kanada ist ein offiziell zweisprachiges Land, in dem der Dualismus von Englisch und Französisch Geschichte hat. Die Frankophonie in Kanada ist in den letzten 20 Jahren in Bewegung geraten: Wirtschaftswandel und Migration aus französischsprachigen Ländern haben ihre soziale Struktur deutlich verändert. Damit einher geht auch ein Wandel in der Politik: Die Basis-Alphabetisierung für frankophone Erwachsene hat Priorität, um damit die Voraussetzung für bessere ökonomische Chancen zu schaffen. Dagegen rücken kulturelle Interessen, wie sie noch in den 1980er Jahren eine wesentliche Rolle für die "Selbstidentifikation" der Frankophonen spielten, in den Hintergrund

Topological invariants of classification problems

AbstractThere is a general agreement that problems which are highly complex in any naive sense are also difficult from the computational point of view. It is therefore of great interest to find invariants and invariant structures which measure in some respect the complexity of the given problem. The question which we are going to consider in the following paper are classification problems, the “computations” are described by questionnaires [3, 10] or, as they are called nowadays, by “branching programs” [11]. The “complexity” of the problem is measured by classical topological invariants (Betti numbers, Euler-Poincaré characteristic) of topological structures (simplicial complexes, topological spaces)

Explainable deep learning models for biological sequence classification

Biological sequences - DNA, RNA and proteins - orchestrate the behavior of all living cells and trying to understand the mechanisms that govern and regulate the interactions among these molecules has motivated biological research for many years. The introduction of experimental protocols that analyze such interactions on a genome- or transcriptome-wide scale has also established the usage of machine learning in our field to make sense of the vast amounts of generated data. Recently, deep learning, a branch of machine learning based on artificial neural networks, and especially convolutional neural networks (CNNs) were shown to deliver promising results for predictive tasks and automated feature extraction. However, the resulting models are often very complex and thus make model application and interpretation hard, but the possibility to interpret which features a model has learned from the data is crucial to understand and to explain new biological mechanisms. This work therefore presents pysster, our open source software library that enables researchers to more easily train, apply and interpret CNNs on biological sequence data. We evaluate and implement different feature interpretation and visualization strategies and show that the flexibility of CNNs allows for the integration of additional data beyond pure sequences to improve the biological feature interpretability. We demonstrate this by building, among others, predictive models for transcription factor and RNA-binding protein binding sites and by supplementing these models with structural information in the form of DNA shape and RNA secondary structure. Features learned by models are then visualized as sequence and structure motifs together with information about motif locations and motif co-occurrence. By further analyzing an artificial data set containing implanted motifs we also illustrate how the hierarchical feature extraction process in a multi-layer deep neural network operates. Finally, we present a larger biological application by predicting RNA-binding of proteins for transcripts for which experimental protein-RNA interaction data is not yet available. Here, the comprehensive interpretation options of CNNs made us aware of potential technical bias in the experimental eCLIP data (enhanced crosslinking and immunoprecipitation) that were used as a basis for the models. This allowed for subsequent tuning of the models and data to get more meaningful predictions in practice

Detection of longitudinal waves in resonance with capillary waves at the air-water interface by energy transfer.

Capillary (transverse) ripples are generated at a monolayer-covered air-water interface. Compression of the monolayer changes the surface dilatational modulus ε and the reduced elasticity ε/σ of the monolayer. When the reduced elasticity reaches a particular value, resonance between the capillary wave and the longitudinal wave should occur. Longitudinal waves (lateral oscillations, modulation of the average distances of the molecules) are detected by using energy transfer (Förster type) between dye molecules incorporated in the monolayer. Capillary ripples are detected by specular reflection of a laser beam from the water surface. The dependence of the longitudinal and the transverse wave characteristics on the area per molecule was investigated, and the resonance phenomenon was observed near the theoretical value of ε/σ for the resonance condition

Temporal evolution of fault systems in the Upper Jurassic of the Central German Molasse Basin: case study Unterhaching

The structural evolution of faults in foreland basins is linked to a complex basin history ranging from extension to contraction and inversion tectonics. Faults in the Upper Jurassic of the German Molasse Basin, a Cenozoic Alpine foreland basin, play a significant role for geothermal exploration and are therefore imaged, interpreted and studied by 3D seismic reflection data. Beyond this applied aspect, the analysis of these seismic data help to better understand the temporal evolution of faults and respective stress fields. In 2009, a 27 km2 3D seismic reflection survey was conducted around the Unterhaching Gt 2 well, south of Munich. The main focus of this study is an in-depth analysis of a prominent v-shaped fault block structure located at the center of the 3D seismic survey. Two methods were used to study the periodic fault activity and its relative age of the detected faults: (1) horizon flattening and (2) analysis of incremental fault throws. Slip and dilation tendency analyses were conducted afterwards to determine the stresses resolved on the faults in the current stress field. Two possible kinematic models explain the structural evolution: One model assumes a left-lateral strike slip fault in a transpressional regime resulting in a positive flower structure. The other model incorporates crossing conjugate normal faults within a transtensional regime. The interpreted successive fault formation prefers the latter model. The episodic fault activity may enhance fault zone permeability hence reservoir productivity implying that the analysis of periodically active faults represents an important part in successfully targeting geothermal wells