Fractal basins of escape and the formation of spiral arms in a galactic potential with a bar

We investigate the dynamics in the close vicinity of and within the critical area in a 2D effective galactic potential with a bar of Zotos. We have calculated Poincar\'e surfaces of section and the basins of escape. In both the Poincar\'e surfaces of section and the basins of escape we find numerical evidence for the existence of a separatrix which hinders orbits from escaping out of the bar region. We present numerical evidence for the similarity between spiral arms of barred spiral galaxies and tidal tails of star clusters.Comment: 12 pages, 10 figures, accepted by MNRA

Training working memory to reduce rumination

Cognitive symptoms of depression, such as rumination, have shown to be associated with deficits in working memory functioning. More precisely, the capacity to expel irrelevant negative information from working memory seems to be affected. Even though these associations have repeatedly been demonstrated, the nature and causal direction of this association is still unclear. Therefore, within an experimental design, we tried to manipulate working memory functioning of participants with heightened rumination scores in two similar experiments (n = 72 and n = 45) using a six day working memory training compared to active and passive control groups. Subsequently the effects on the processing of non-emotional and emotional information in working memory were monitored. In both experiments, performance during the training task significantly increased, but this performance gain did not transfer to the outcome working memory tasks or rumination and depression measures. Possible explanations for the failure to find transfer effects are discussed

Geochelone carbonaria

Number of Pages: 7Integrative BiologyGeological Science

Learning Dynamic Boltzmann Distributions as Reduced Models of Spatial Chemical Kinetics

Finding reduced models of spatially-distributed chemical reaction networks requires an estimation of which effective dynamics are relevant. We propose a machine learning approach to this coarse graining problem, where a maximum entropy approximation is constructed that evolves slowly in time. The dynamical model governing the approximation is expressed as a functional, allowing a general treatment of spatial interactions. In contrast to typical machine learning approaches which estimate the interaction parameters of a graphical model, we derive Boltzmann-machine like learning algorithms to estimate directly the functionals dictating the time evolution of these parameters. By incorporating analytic solutions from simple reaction motifs, an efficient simulation method is demonstrated for systems ranging from toy problems to basic biologically relevant networks. The broadly applicable nature of our approach to learning spatial dynamics suggests promising applications to multiscale methods for spatial networks, as well as to further problems in machine learning