Abstract
Contemporary AO systems, such as the Multi-Object Adaptive Optics system (MOAO) RAVEN currently associated with the Subaru Telescope, can suffer from significant Non-Common Path Aberrations (NCPA). These errors ultimately affect image quality and arise from optical path differences between the wavefront sensor (WFS) path and the science path. A typical correction of NCPA involves estimating the aberration phase and correcting the system with an offset on the deformable mirror (DM). We summarize two methods used to correct for NCPA on an experimental bench. We also successfully calibrate the NCPA on RAVEN using one of these methods. Finally, we report on some first science results with RAVEN, obtained after NCPA correction.

Abstract
Raven is a Multi-Object Adaptive Optics science demonstrator which has been used on-sky at Subaru telescope from May 2014 to July 2015. Raven has been developed at the University of Victoria AO Lab, in partnership with NRC, NAOJ and Tohoku University. Raven includes three open loop WFSs, a central laser guide star WFS, and two science pick-off arms feeding light to the Subaru IRCS spectrograph. Raven supports different AO modes: SCAO, open-loop GLAO and MOAO. This paper gives an overview of the instrument design, compares the on-sky performance of the different AO modes and presents some of the science results achieved with MOAO.

Abstract
Multi-object astronomical adaptive optics (MOAO) is now a mature wide-field observation mode to enlarge the adaptive-optics-corrected field in a few specific locations over tens of arcminutes. The work-scope provided by open-loop tomography and pupil conjugation is amenable to a spatio-angular linear-quadratic-Gaussian (SA-LQG) formulation aiming to provide enhanced correction across the field with improved performance over static reconstruction methods and less stringent computational complexity scaling laws. Starting from our previous work [J. Opt. Soc. Am. A 31, 101 (2014)], we use stochastic time-progression models coupled to approximate sparse measurement operators to outline a suitable SA-LQG formulation capable of delivering near optimal correction. Under the spatio-angular framework the wavefronts are never explicitly estimated in the volume, providing considerable computational savings on 10-m-class telescopes and beyond. We find that for Raven, a 10-m-class MOAO system with two science channels, the SA-LQG improves the limiting magnitude by two stellar magnitudes when both the Strehl ratio and the ensquared energy are used as figures of merit. The sky coverage is therefore improved by a factor of ~5. © 2015 Optical Society of America.

Abstract
Multi-object astronomical adaptive optics (MOAO) is now a mature wide-field observation mode to enlarge the adaptive-optics-corrected field in a few specific locations over tens of arcminutes. The work-scope provided by open-loop tomography and pupil conjugation is amenable to a spatio-angular linear-quadratic-Gaussian (SA-LQG) formulation aiming to provide enhanced correction across the field with improved performance over static reconstruction methods and less stringent computational complexity scaling laws. Starting from our previous work [J. Opt. Soc. Am. A 31, 101 (2014)], we use stochastic time-progression models coupled to approximate sparse measurement operators to outline a suitable SA-LQG formulation capable of delivering near optimal correction. Under the spatio-angular framework the wavefronts are never explicitly estimated in the volume, providing considerable computational savings on 10-m-class telescopes and beyond. We find that for Raven, a 10-m-class MOAO system with two science channels, the SA-LQG improves the limiting magnitude by two stellar magnitudes when both the Strehl ratio and the ensquared energy are used as figures of merit. The sky coverage is therefore improved by a factor of similar to 5. (C) 2015 Optical Society of America

Abstract
Data centres are large energy consumers. A large portion of this power consumption is due to the control of physical parameters of the data centre (such as temperature and humidity). However, these physical parameters are tightly coupled with computations, and even more so in upcoming data centres, where the location of workloads can vary substantially due, for example, to workloads being moved in the cloud infrastructure hosted in the data centre. Therefore, managing the physical and compute infrastructure of a large data centre is an embodiment of a cyber-physical system (CPS). In this paper, we describe a data collection and distribution architecture that enables gathering physical parameters of a large data centre at a very high temporal and spatial resolution of the sensor measurements. We detail this architecture and define the structure of the underlying messaging system that is used to collect and distribute the data.

Abstract