Antiferromagnetic long-range order in dissipative Rydberg lattices

We study the dynamics of dissipative spin lattices with power-law interactions, realized via few-level atoms driven by coherent laser-coupling and decoherence processes. Using Monte-Carlo simulations, we determine the phase diagram in the steady state and analyze the dynamics of its generation. As opposed to mean-field predictions and nearest-neighbour models there is no phase transition to long-range ordered phases for realistic interactions and resonant driving. However, for finite laser detunings, we demonstrate the emergence of crystalline order with a vanishing dissipative gap. Although the found steady states differ considerably from those of an equilibrium Ising magnet, the critical exponent of the revealed dissipative phase transition falls into the 2D Ising universality class. Two complementary schemes for an experimental implementation with cold Rydberg atoms are discussed

Robust risk aggregation with neural networks

We consider settings in which the distribution of a multivariate random variable is partly ambiguous. We assume the ambiguity lies on the level of the dependence structure, and that the marginal distributions are known. Furthermore, a current best guess for the distribution, called reference measure, is available. We work with the set of distributions that are both close to the given reference measure in a transportation distance (e.g. the Wasserstein distance), and additionally have the correct marginal structure. The goal is to find upper and lower bounds for integrals of interest with respect to distributions in this set. The described problem appears naturally in the context of risk aggregation. When aggregating different risks, the marginal distributions of these risks are known and the task is to quantify their joint effect on a given system. This is typically done by applying a meaningful risk measure to the sum of the individual risks. For this purpose, the stochastic interdependencies between the risks need to be specified. In practice the models of this dependence structure are however subject to relatively high model ambiguity. The contribution of this paper is twofold: Firstly, we derive a dual representation of the considered problem and prove that strong duality holds. Secondly, we propose a generally applicable and computationally feasible method, which relies on neural networks, in order to numerically solve the derived dual problem. The latter method is tested on a number of toy examples, before it is finally applied to perform robust risk aggregation in a real world instance.Comment: Revised version. Accepted for publication in "Mathematical Finance

Auswirkungen der Risikomessmethode aufdieAnlageperformance - Eine empirische Untersuchung für den Fall definierter Risikolimite inderPlanungsrechnung anhand des DAX

Zusammenfassung: Risikomessmodelle werden als Controllinginstrumente hinsichtlich ihrer Güte oft einseitig unter reinen Risikogesichtspunkten getestet. Der Einfluss, den sie auf die Anlageperformance besitzen, wird dabei in der Regel vernachlässigt. Der vorliegende Beitrag zeigt den konzeptionellen Zusammenhang zwischen Risikomessmodellen und Anlageperformance auf und weist ihn empirisch nach. Dabei wird deutlich, dass die Anwendung von Normalverteilungsannahme, historischer Simulation und impliziter Volatilität zur Risikomessung im Rahmen von Limitsystemen zu deutlich unterschiedlichen Portfoliorenditen bei gleichem Portfoliorisiko führen kann. In einer Betrachtung des Risikomanagements aus Performancesicht kann somit erhebliches Potential liege

Developing a Classroom Culture of Thinking: A Whole School Approach

One of the primary objectives of schooling should involve the teaching of tools for lifelong learning. As a result, many educators place a high priority on empowering students with thinking skills such as the ability to reason, to make informed judgements, to critically evaluate information, and to think both creatively and caringly. Immersion in high order thinking, therefore, should be an integral aspect of learning. This paper discusses the need for a framework that ensures this occurs in every classroom, every day

An Investigation Into Building Level Leadership That Promotes Professional Learning Communities

The extent to which principals in suburban elementary and secondary schools lead schools to be ready to transform into professional learning committees was investigated. Data were collected through the use of the Professional Learning Communities Assessment – Revised (PLCA-R) questionnaire administered to principals, assistant principals and lead teachers as identified by their principals. Findings from the PLCA-R determined a ranked order for the 45 schools that completed the questionnaire. Following the questionnaire, one-on-one face-to-face interviews were conducted with the six principals from the three highest ranked elementary schools and the three highest ranked secondary schools. Six lead teachers, one from each of the principal\u27s schools, were also interviewed. Interview data were reviewed to develop five main themes and thirteen subthemes. The data collected primarily supported research conducted with PLC\u27s. The findings further suggested producing seven ideals principals could use to help their schools become ready to transform to professional learning communities. These ideals were based on communication, development and understanding of school-wide goals, instructional based meetings, easy access to data, availability of the principal, administrative support for teachers and transparency of the decision making process. Implications for future practice and directions for further research are also discussed