EuFe2(As1−xPx)2: reentrant spin glass and superconductivity

By systematic investigations of the magnetic, transport and thermodynamic properties of single crystals of EuFe2(As1-xPx)2 we explore the complex interplay of superconductivity and Eu2+ magnetism. Below 30K, two magnetic transitions are observed for all P substituted crystals suggesting a revision of the phase diagram. In addition to the canted A-type antiferromagnetic order of Eu2+ at approximately 20K, a spin glass transition is discovered at lower temperatures. Most remarkably, the reentrant spin glass state of EuFe2(As1-xPx)2 coexists with superconductivity around x = 0.2.Comment: accepted by Physical Review Letter

Towards energy aware cloud computing application construction

The energy consumption of cloud computing continues to be an area of significant concern as data center growth continues to increase. This paper reports on an energy efficient interoperable cloud architecture realised as a cloud toolbox that focuses on reducing the energy consumption of cloud applications holistically across all deployment models. The architecture supports energy efficiency at service construction, deployment and operation. We discuss our practical experience during implementation of an architectural component, the Virtual Machine Image Constructor (VMIC), required to facilitate construction of energy aware cloud applications. We carry out a performance evaluation of the component on a cloud testbed. The results show the performance of Virtual Machine construction, primarily limited by available I/O, to be adequate for agile, energy aware software development. We conclude that the implementation of the VMIC is feasible, incurs minimal performance overhead comparatively to the time taken by other aspects of the cloud application construction life-cycle, and make recommendations on enhancing its performance

Agent-Based Modelling as a Foundation for Big Data

In this article we propose a process-based definition of big data, as opposed to the size - and technology-based definitions. We argue that big data should be perceived as a continu- ous, unstructured and unprocessed dynamics of primitives, rather than as points (snapshots) or summaries (aggregates) of an underlying phenomenon. Given this, we show that big data can be generated through agent-based models but not by equation-based models. Though statistical and machine learning tools can be used to analyse big data, they do not constitute a big data-generation mechanism. Furthermore, agent-based models can aid in evaluating the quality (interpreted as information aggregation efficiency) of big data. Based on this, we argue that agent-based modelling can serve as a possible foundation for big data. We substantiate this interpretation through some pioneering studies from the 1980s on swarm intelligence and several prototypical agent-based models developed around the 2000s