The pick-up of cometary protons by the solar wind

The High Energy Range Spectrometer (HERS) of the Ion Mass Spectrometer on the Giotto spacecraft measured the 3-dimensional distribution of picked-up cometary protons over a distance of approximately 8 million km upstream of the bow shock of Comet Halley. The protons were observed to be elastically scattered out of their original cycloidal trajectories such that they were nonuniformly distributed over a spherical shell in velocity space. The shell radius (relative to its expected radius) and thickness increased as the bow shock was approached. Downstream of the shock, the cometary protons could not be distinguished from the heated solar wind protons

The Pulsed Neutron Beam EDM Experiment

We report on the Beam EDM experiment, which aims to employ a pulsed cold neutron beam to search for an electric dipole moment instead of the established use of storable ultracold neutrons. We present a brief overview of the basic measurement concept and the current status of our proof-of-principle Ramsey apparatus

Safety-Aware Apprenticeship Learning

Apprenticeship learning (AL) is a kind of Learning from Demonstration techniques where the reward function of a Markov Decision Process (MDP) is unknown to the learning agent and the agent has to derive a good policy by observing an expert's demonstrations. In this paper, we study the problem of how to make AL algorithms inherently safe while still meeting its learning objective. We consider a setting where the unknown reward function is assumed to be a linear combination of a set of state features, and the safety property is specified in Probabilistic Computation Tree Logic (PCTL). By embedding probabilistic model checking inside AL, we propose a novel counterexample-guided approach that can ensure safety while retaining performance of the learnt policy. We demonstrate the effectiveness of our approach on several challenging AL scenarios where safety is essential.Comment: Accepted by International Conference on Computer Aided Verification (CAV) 201

Sampling for Bayesian program learning

Towards learning programs from data, we introduce the problem of sampling programs from posterior distributions conditioned on that data. Within this setting, we propose an algorithm that uses a symbolic solver to efficiently sample programs. The proposal combines constraint-based program synthesis with sampling via random parity constraints. We give theoretical guarantees on how well the samples approximate the true posterior, and have empirical results showing the algorithm is efficient in practice, evaluating our approach on 22 program learning problems in the domains of text editing and computer-aided programming.National Science Foundation (U.S.) (Award NSF-1161775)United States. Air Force Office of Scientific Research (Award FA9550-16-1-0012

Geographical interdependence, international trade and economic dynamics: the Chinese and German solar energy industries

The trajectories of the German and Chinese photovoltaic industries differ significantly yet are strongly interdependent. Germany has seen a rapid growth in market demand and a strong increase in production, especially in the less developed eastern half of the country. Chinese growth has been export driven. These contrasting trajectories reflect the roles of market creation, investment and credit and the drivers of innovation and competitiveness. Consequent differences in competiveness have generated major trade disputes