Uniformly Rotating Rings in General Relativity

In this paper, we discuss general relativistic, self-gravitating and uniformly rotating perfect fluid bodies with a toroidal topology (without central object). For the equations of state describing the fluid matter we consider polytropic as well as completely degenerate, perfect Fermi gas models. We find that the corresponding configurations possess similar properties to the homogeneous relativistic Dyson rings. On the one hand, there exists no limit to the mass for a given maximal mass-density inside the body. On the other hand, each model permits a quasistationary transition to the extreme Kerr black hole.Comment: 6 pages, 4 figures, added material and one new referenc

An acoustic view of ocean mixing

Knowledge of the parameter K (turbulent diffusivity/"mixing intensity") is a key to understand transport processes of matter and energy in the ocean. Especially the almost vertical component of K across the ocean stratification (diapycnal diffusivity) is vital for research on biogeochemical cycles or greenhouse gas budgets. Recent boost in precision of water velocity data that can be obtained from vessel-mounted acoustic instruments (vmADCP) allows identifying ocean regions of elevated diapycnal diffusivity during research cruises - in high horizontal resolution and without extra ship time needed. This contribution relates acoustic data from two cruises in the Tropical North East Atlantic Oxygen Minimum Zone to simultaneous field observations of diapycnal diffusivity: pointwise measurements by a microstructure profiler as well as one integrative value from a large scale Tracer Release Experiment

Diapycnal oxygen supply to the tropical North Atlantic oxygen minimum zone

The replenishment of consumed oxygen in the open ocean oxygen minimum zone (OMZ) off northwest Africa is accomplished by oxygen transport across and along density surfaces, i.e. diapycnal and isopycnal oxygen supply. Here the diapycnal oxygen supply is investigated using a large observational set of oxygen profiles and diapycnal mixing data from years 2008 to 2010. Diapycnal mixing is inferred from different sources: (i) a large-scale tracer release experiment, (ii) microstructure profiles, and (iii) shipboard acoustic current measurements plus density profiles. From these measurements, the average diapycnal diffusivity in the studied depth interval from 150 to 500m is estimated to be 1×10−5 m2 s−1, with lower and upper 95%confidence limits of 0.8×10−5 m2 s−1 and 1.4×10−5 m2 s−1. Diapycnal diffusivity in this depth range is predominantly caused by turbulence, and shows no significant vertical gradient. Diapycnal mixing is found to contribute substantially to the oxygen supply of the OMZ. Within the OMZ core, 1.5 μmol kg−1 yr−1 of oxygen is supplied via diapycnal mixing, contributing about one-third of the total demand. This oxygen which is supplied via diapycnal mixing originates from oxygen that has been laterally supplied within the upper CentralWater layer above the OMZ, and within the Antarctic Intermediate Water layer below the OMZ. Due to the existence of a separate shallow oxygen minimum at about 100m depth throughout most of the study area, there is no net vertical oxygen flux from the surface layer into the Central Water layer. Thus all oxygen supply of the OMZ is associated with remote pathways

Sequence-Structure Alignment Using a Statistical Analysis of Core Models and Dynamic Programming

The expanding availability of protein data enforces the application of empirical methods necessary to recognize protein structures. In this paper a sequence-structure alignment method is described and applied to various Ubiquitin-like folded Ras-binding domains. On the basis of two probability functions that evaluate similarities between the occurrence of amino-acids in the primary and secondary protein structure, different versions of simple scoring functions are proposed. The application of the program ’PLACER’ that uses a dynamic programming approach enables the search for an optimal sequence-structure alignment and the prediction of the secondary structure

A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis

Automatic analysis of scanned historical documents comprises a wide range of image analysis tasks, which are often challenging for machine learning due to a lack of human-annotated learning samples. With the advent of deep neural networks, a promising way to cope with the lack of training data is to pre-train models on images from a different domain and then fine-tune them on historical documents. In the current research, a typical example of such cross-domain transfer learning is the use of neural networks that have been pre-trained on the ImageNet database for object recognition. It remains a mostly open question whether or not this pre-training helps to analyse historical documents, which have fundamentally different image properties when compared with ImageNet. In this paper, we present a comprehensive empirical survey on the effect of ImageNet pre-training for diverse historical document analysis tasks, including character recognition, style classification, manuscript dating, semantic segmentation, and content-based retrieval. While we obtain mixed results for semantic segmentation at pixel-level, we observe a clear trend across different network architectures that ImageNet pre-training has a positive effect on classification as well as content-based retrieval