Infrared spectroscopy of circumstellar dust: signs of differentiated materials?

Mid-infrared absorption spectra of powdered achondrites are compared with the astronomical spectra of dust around young, evolving stars, to find evidence (or not) of dust formed in collisional cascades of material from planetesimals

Structural and electronic properties of MgO nanotube clusters

Finite magnesium oxide nanotubes are investigated. Stacks of four parallel squares, hexagons, octagons, and decagons are constructed and studied by the pseudopotential density functional theory within the local-density approximation. Optimized structures are slightly distorted stacks of polygons. These clusters are insulators and the band gap of 8.5 eV is constant over an investigated range of the diameters of stacked polygonal rings. Using the L"owdin population analysis a charge transfer towards the oxygen atoms is estimated as 1.4, which indicates that the mixed ionocovalent bonding exists in investigated MgO nanotubes

Ions in glass forming glycerol: Close correlation of alpha and fast beta relaxation

We provide broadband dielectric loss spectra of glass-forming glycerol with varying additions of LiCl. The measurements covering frequencies up to 10 THz extend well into the region of the fast beta process, commonly ascribed to caged molecule dynamics. Aside of the known variation of the structural alpha relaxation time and a modification of the excess wing with ion content, we also find a clear influence on the shallow loss minimum arising from the fast beta relaxation. Within the framework of mode-coupling theory, the detected significant broadening of this minimum is in reasonable accord with the found variation of the alpha-relaxation dynamics. A correlation between alpha-relaxation rate and minimum position holds for all ion concentrations and temperatures, even below the critical temperature defined by mode-coupling theory.Comment: 5 pages, 5 figure

Orbits and masses in the young triple system TWA 5

We aim to improve the orbital elements and determine the individual masses of the components in the triple system TWA 5. Five new relative astrometric positions in the H band were recorded with the adaptive optics system at the Very Large Telescope (VLT). We combine them with data from the literature and a measurement in the Ks band. We derive an improved fit for the orbit of TWA 5Aa-b around each other. Furthermore, we use the third component, TWA 5B, as an astrometric reference to determine the motion of Aa and Ab around their center of mass and compute their mass ratio. We find an orbital period of 6.03+/-0.01 years and a semi-major axis of 63.7+/-0.2 mas (3.2+/-0.1 AU). With the trigonometric distance of 50.1+/-1.8 pc, this yields a system mass of 0.9+/-0.1 Msun, where the error is dominated by the error of the distance. The dynamical mass agrees with the system mass predicted by a number of theoretical models if we assume that TWA5 is at the young end of the age range of the TW Hydrae association. We find a mass ratio of M_Ab / M_Aa = 1.3 +0.6/-0.4, where the less luminous component Ab is more massive. This result is likely to be a consequence of the large uncertainties due to the limited orbital coverage of the observations.Comment: 9 pages, 8 figures, accepted by Astronomy and Astrophysic

Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images

Convolutional neural networks (CNNs) show impressive performance for image classification and detection, extending heavily to the medical image domain. Nevertheless, medical experts are sceptical in these predictions as the nonlinear multilayer structure resulting in a classification outcome is not directly graspable. Recently, approaches have been shown which help the user to understand the discriminative regions within an image which are decisive for the CNN to conclude to a certain class. Although these approaches could help to build trust in the CNNs predictions, they are only slightly shown to work with medical image data which often poses a challenge as the decision for a class relies on different lesion areas scattered around the entire image. Using the DiaretDB1 dataset, we show that on retina images different lesion areas fundamental for diabetic retinopathy are detected on an image level with high accuracy, comparable or exceeding supervised methods. On lesion level, we achieve few false positives with high sensitivity, though, the network is solely trained on image-level labels which do not include information about existing lesions. Classifying between diseased and healthy images, we achieve an AUC of 0.954 on the DiaretDB1.Comment: Accepted in Proc. IEEE International Conference on Image Processing (ICIP), 201