Dynamic critical phenomena in the AdS/CFT duality

In critical phenomena, singular behaviors arise not only for thermodynamic quantities but also for transport coefficients. We study this dynamic critical phenomenon in the AdS/CFT duality. We consider black holes with a single R-charge in various dimensions and compute the R-charge diffusion in the linear perturbations. In this case, the black holes belong to model B according to the classification of Hohenberg and Halperin.Comment: 17 pages, ReVTeX4; v2: added references and discussio

Methane emissions from the upwelling area off Mauritania (NW Africa)

Coastal upwelling regions have been identified as sites of enhanced CH4 emissions to the atmosphere. The coastal upwelling area off Mauritania (NW Africa) is one of the most biologically productive regions of the world's ocean but its CH4 emissions have not been quantified so far. More than 1000 measurements of atmospheric and dissolved CH4 in the surface layer in the upwelling area off Mauritania were performed as part of the German SOPRAN (Surface Ocean Processes in the Anthropocene) study during two cruises in March/April 2005 (P320/1) and February 2007 (P348). During P348 enhanced CH4 saturations of up to 200% were found close to the coast and were associated with upwelling of South Atlantic Central Water. An area-weighted, seasonally adjusted estimate yielded overall annual CH4 emissions in the range from 1.6 to 2.9 Gg CH4. Thus the upwelling area off Mauritania represents a regional hot spot of CH4 emissions but seems to be of minor importance for the global oceanic CH4 emissions

The kernel Kalman rule: efficient nonparametric inference with recursive least squares

Nonparametric inference techniques provide promising tools for probabilistic reasoning in high-dimensional nonlinear systems. Most of these techniques embed distributions into reproducing kernel Hilbert spaces (RKHS) and rely on the kernel Bayes’ rule (KBR) to manipulate the embeddings. However, the computational demands of the KBR scale poorly with the number of samples and the KBR often suffers from numerical instabilities. In this paper, we present the kernel Kalman rule (KKR) as an alternative to the KBR. The derivation of the KKR is based on recursive least squares, inspired by the derivation of the Kalman innovation update. We apply the KKR to filtering tasks where we use RKHS embeddings to represent the belief state, resulting in the kernel Kalman filter (KKF). We show on a nonlinear state estimation task with high dimensional observations that our approach provides a significantly improved estimation accuracy while the computational demands are significantly decreased

Susceptibilities near the QCD (tri)critical point

Based on the proper-time renormalization group approach, the scalar and the quark number susceptibilities in the vicinity of possible critical end points of the hadronic phase diagram are investigated in the two-flavor quark-meson model. After discussing the quark-mass dependence of the location of such points, the critical behavior of the in-medium meson masses and quark number density are calculated. The universality classes of the end points are determined by calculating the critical exponents of the susceptibilities. In order to numerically estimate the influence of fluctuations we compare all quantities with results from a mean-field approximation. It is concluded that the region in the phase diagram where the susceptibilities are enhanced is more compressed around the critical end point if fluctuations are included.Comment: 14 pages, 19 figures; v3 typos and minor changes, references adde

Tidal disruption and ignition of white dwarfs by moderately massive black holes

We present a numerical investigation of the tidal disruption of white dwarfs by moderately massive black holes, with particular reference to the centers of dwarf galaxies and globular clusters. Special attention is given to the fate of white dwarfs of all masses that approach the black hole close enough to be disrupted and severely compressed to such extent that explosive nuclear burning can be triggered. Consistent modeling of the gas dynamics together with the nuclear reactions allows for a realistic determination of the explosive energy release. In the most favorable cases, the nuclear energy release may be comparable to that of typical type Ia supernovae. Although the explosion will increase the mass fraction escaping on hyperbolic orbits, a good fraction of the debris remains to be swallowed by the hole, causing a bright soft X-ray flare lasting for about a year. Such transient signatures, if detected, would be a compelling testimony for the presence of a moderately mass black hole (below $10^5 M_\odot$).Comment: 38 pages, 19 figures, further simulations adde

Learning robust policies for object manipulation with robot swarms

Swarm robotics investigates how a large population of robots with simple actuation and limited sensors can collectively solve complex tasks. One particular interesting application with robot swarms is autonomous object assembly. Such tasks have been solved successfully with robot swarms that are controlled by a human operator using a light source. In this paper, we present a method to solve such assembly tasks autonomously based on policy search methods. We split the assembly process in two subtasks: generating a high-level assembly plan and learning a low-level object movement policy. The assembly policy plans the trajectories for each object and the object movement policy controls the trajectory execution. Learning the object movement policy is challenging as it depends on the complex state of the swarm which consists of an individual state for each agent. To approach this problem, we introduce a representation of the swarm which is based on Hilbert space embeddings of distributions. This representation is invariant to the number of agents in the swarm as well as to the allocation of an agent to its position in the swarm. These invariances make the learned policy robust to changes in the swarm and also reduce the search space for the policy search method significantly. We show that the resulting system is able to solve assembly tasks with varying object shapes in multiple simulation scenarios and evaluate the robustness of our representation to changes in the swarm size. Furthermore, we demonstrate that the policies learned in simulation are robust enough to be transferred to real robots