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
Parkinson's Disease (PD) patients often need Deep Brain Stimulation (DBS) surgery when they become intolerant to drugs or these lose efficiency. A stimulation electrode is implanted in the basal ganglia to promote the functional control of the deregulated dopaminergic motor pathways. The stimulation target is defined by medical imaging, followed by electrophysiological inspection for fine electrode position trimming and electrical stimulation tuning. Intra-operative stimulation of the target and the evaluation of wrist rigidity allows to choose the stimulation parameters which best alleviate PD symptoms without side effects. Neurologists impose a passive wrist flexion movement and qualitatively describe the perceived decrease in rigidity under different voltages, based on its experience and with subjectivity. We designed a novel, comfortable and wireless wearable motion sensor to classify the wrist rigidity by deriving a robust signal descriptor from angular speed values and a polynomial mathematical model to classify signals using a quantitative continuous scale. The descriptor significantly (p < 0.05) distinguished between non-rigid and rigid states, and the classification model labelled correctly 83.9 % of the evaluated signals against the blind-agreement of two specialists. Additionally, we developed a methodology to detect cogwheel rigidity from the angular speed signal with high sensitivity (0.93). Our system provides a reliable evaluation of wrist rigidity, improving upon the inherent subjective clinical evaluation while using small, simple and easy to use motion sensor.

Abstract

Abstract
A lens is an optical device which refracts light. Properly adjusted, it can be used to project sharp images of objects onto a screen— a principle underlying photography as well as human vision. Striving for clarity, we shift our focus to lenses as abstractions for bidirectional programming. By means of standard mathematical terminology as well as intuitive properties of bidirectional programs, we observe different ways to characterize lenses and show exactly how their laws interact. Like proper adjustment of optical lenses is essential for taking clear pictures, proper organization of lens laws is essential for forming a clear picture of different lens classes. Incidentally, the process of understanding bidirectional lenses clearly is quite similar to the process of taking a good picture. By showing that it is exactly the backward computation which defines lenses of a certain standard class, we provide an unusual perspective, as contemporary research tends to focus on the forward computation. © Springer International Publishing Switzerland 2015

Abstract
Declarative programming in the style of functional and logic programming has been hailed as an alternative parallel programming style where computer programs are automatically parallelized without programmer control. Although this approach removes many pitfalls of explicit parallel programming, it hides important information about the underlying parallel architecture that could be used to improve the scalability and efficiency of programs. In this paper, we present a novel programming model that allows the programmer to reason about thread state in data-driven declarative programs. This abstraction has been implemented on top of Linear Meld, a linear logic programming language that is designed for writing graphbased programs. Wepresent several programs that show theflavorofour new programming model, including graph algorithms and a machine learning algorithm. Our goal is to show thatitis possible to take advantage of architectural details without losing the key advantages of logic programming.

Abstract