Understanding Marine Microbes, the Driving Engines of the Ocean

When you hear the word microbes, what comes to your mind? Something much too small to see and that makes you fall ill? Just because some microbes cause diseases that does not mean they are all evil. For example, in the marine (ocean) environment, the vast majority of microbes are good ones. They are the “driving engines” of the ocean and are essential for the health of our whole planet. Unfortunately, most of the marine microbes and their interactions with the marine environment are poorly understood. So, it is important to get an idea of which microbes are helping us and how they are doing this. These data will provide scientists with the knowledge to fight against big global challenges, such as climate change and environmental pollution. Unfortunately, it is very hard to study marine microbes due to their microscopic size, huge diversity, and their big home – the ocean. Therefore, we would like to engage “citizen scientists” in this project to help us to sample marine microbes so that we can identify them

Constraints on fluid flow processes in the Hellenic Accretionary Complex (eastern Mediterranean Sea) from numerical modeling

The dynamics of accretionary convergent margins are severely influenced by intense deformation and fluid expulsion. To quantify the fluid pressure and fluid flow velocities in the Hellenic subduction system, we set up 2-D hydrogeological numerical models following two seismic reflection lines across the Mediterranean Ridge. These profiles bracket the along-strike variation in wedge geometry: moderate compression and a >4 km thick underthrust sequence in the west versus enhanced compression and <1 km of downgoing sediment in the center. Input parameters were obtained from preexisting geophysical data, drill cores, and new geotechnical laboratory experiments. A permeability-porosity relationship was determined by a sensitivity analysis, indicating that porosity and intrinsic permeability are small. This hampers the expulsion of fluids and leads to the build up of fluid overpressure in the deeper portion of the wedge and in the underthrust sediment. The loci of maximum fluid pressure are mainly controlled by the compactional fluid source, which generally decreases toward the backstop. However, pore pressure is still high at the decollement level at distances <100 km from the deformation front, either by the incorporation of low permeability evaporites or additional compaction of the wedge sediments in the two profiles. In the west, however, formation of a wide accretionary complex is facilitated by high pore pressure zones. When compared to other large accretionary complexes such as Nankai or Barbados, our results not only show broad similarities but also that near-lithostatic pore pressures may be easier to maintain in the Hellenic Arc because of accentuated collision, some underthrust evaporates, and a thicker underthrust sequence

Semantically Guided Depth Upsampling

We present a novel method for accurate and efficient up- sampling of sparse depth data, guided by high-resolution imagery. Our approach goes beyond the use of intensity cues only and additionally exploits object boundary cues through structured edge detection and semantic scene labeling for guidance. Both cues are combined within a geodesic distance measure that allows for boundary-preserving depth in- terpolation while utilizing local context. We model the observed scene structure by locally planar elements and formulate the upsampling task as a global energy minimization problem. Our method determines glob- ally consistent solutions and preserves fine details and sharp depth bound- aries. In our experiments on several public datasets at different levels of application, we demonstrate superior performance of our approach over the state-of-the-art, even for very sparse measurements.Comment: German Conference on Pattern Recognition 2016 (Oral

Aspects of noncommutative Lorentzian geometry for globally hyperbolic spacetimes

Connes' functional formula of the Riemannian distance is generalized to the Lorentzian case using the so-called Lorentzian distance, the d'Alembert operator and the causal functions of a globally hyperbolic spacetime. As a step of the presented machinery, a proof of the almost-everywhere smoothness of the Lorentzian distance considered as a function of one of the two arguments is given. Afterwards, using a $C^*$-algebra approach, the spacetime causal structure and the Lorentzian distance are generalized into noncommutative structures giving rise to a Lorentzian version of part of Connes' noncommutative geometry. The generalized noncommutative spacetime consists of a direct set of Hilbert spaces and a related class of $C^*$-algebras of operators. In each algebra a convex cone made of self-adjoint elements is selected which generalizes the class of causal functions. The generalized events, called {\em loci}, are realized as the elements of the inductive limit of the spaces of the algebraic states on the $C^*$-algebras. A partial-ordering relation between pairs of loci generalizes the causal order relation in spacetime. A generalized Lorentz distance of loci is defined by means of a class of densely-defined operators which play the r\^ole of a Lorentzian metric. Specializing back the formalism to the usual globally hyperbolic spacetime, it is found that compactly-supported probability measures give rise to a non-pointwise extension of the concept of events.Comment: 43 pages, structure of the paper changed and presentation strongly improved, references added, minor typos corrected, title changed, accepted for publication in Reviews in Mathematical Physic

PCA detection and denoising of Zeeman signatures in stellar polarised spectra

Our main objective is to develop a denoising strategy to increase the signal to noise ratio of individual spectral lines of stellar spectropolarimetric observations. We use a multivariate statistics technique called Principal Component Analysis. The cross-product matrix of the observations is diagonalized to obtain the eigenvectors in which the original observations can be developed. This basis is such that the first eigenvectors contain the greatest variance. Assuming that the noise is uncorrelated a denoising is possible by reconstructing the data with a truncated basis. We propose a method to identify the number of eigenvectors for an efficient noise filtering. Numerical simulations are used to demonstrate that an important increase of the signal to noise ratio per spectral line is possible using PCA denoising techniques. It can be also applied for detection of magnetic fields in stellar atmospheres. We analyze the relation between PCA and commonly used well-known techniques like line addition and least-squares deconvolution. Moreover, PCA is very robust and easy to compute.Comment: accepted to be published in A&

An output-sensitive algorithm for the minimization of 2-dimensional String Covers

String covers are a powerful tool for analyzing the quasi-periodicity of 1-dimensional data and find applications in automata theory, computational biology, coding and the analysis of transactional data. A \emph{cover} of a string $T$ is a string $C$ for which every letter of $T$ lies within some occurrence of $C$. String covers have been generalized in many ways, leading to \emph{k-covers}, \emph{$\lambda$-covers}, \emph{approximate covers} and were studied in different contexts such as \emph{indeterminate strings}. In this paper we generalize string covers to the context of 2-dimensional data, such as images. We show how they can be used for the extraction of textures from images and identification of primitive cells in lattice data. This has interesting applications in image compression, procedural terrain generation and crystallography

Frictional strengthening explored during non-steady state shearing. Implications for fault stability and slip event recurrence time

On natural faults that host repeating slip events, the inter-event loading time is quite large compared to the slip event duration. Since most friction studies focus on steady-state frictional behavior, the fault loading phase is not typically examined. Here, we employ a method specifically designed to evaluate fault strength evolution during active loading, under shear driving rates as low as 10−10&nbsp;m/s, on natural fault gouge samples from the Waikukupa Thrust in southern New Zealand. These tests reveal that in the early stages of loading following a slip event, there is a period of increased stability, which fades with accumulated slip. In the framework of rate- and state-dependent friction laws, this temporary stable phase exists as long as slip is less than the critical slip distance and the elapsed time is less than the value of the state variable at steady state. These observations indicate a minimum earthquake recurrence time, which depends on the field value of the critical slip distance and the background slip rate. We compare estimates of minimum earthquake recurrence times with the recurrence times of repeating large earthquakes on the Alpine Fault in southern New Zealand and repeating small-magnitude earthquakes on the San Andreas Fault system in California. We find that the observed recurrence times are mostly longer than the predicted minimum values, and exceptions in the San Andreas system may be explained by elevated slip rates due to larger earthquakes in this region