Abstract
Multi-objective optimization evolutionary techniques provide solutions for a specific problem using optimally concepts taking into consideration all the design criteria. In the last years, several multi-objective algorithms were proposed but usually the performance is measured at the end neglecting, therefore, the solution diversity along the interactions. In order to understand the evolution of the solutions this work studies the dynamic of the successive iterations. The analysis adopts the fractional entropy for measuring the statistical behavior of the population. The results show that the entropy is a good tool to monitor and capture phenomena such as the diversity and convergence during the algorithm execution.

Abstract
We present a novel approach that uses an iterative deepening algorithm in order to perform probabilistic logic inference for ProbLog, a probabilistic extension of Prolog. The most used inference method for ProbLog is exact inference combined with tabling. Tabled exact inference first collects a set of SLG derivations which contain the probabilistic structure of the ProbLog program including the cycles. At a second step, inference requires handling these cycles in order to create a noncyclic Boolean representation of the probabilistic information. Finally, the Boolean representation is compiled to a data structure where inference can be performed in linear time. Previous work has illustrated that there are two limiting factors for ProbLog’s exact inference. The first factor is the target compilation language and the second factor is the handling of the cycles. In this paper, we address the second factor by presenting an iterative deepening algorithm which handles cycles and produces solutions to problems that previously ProbLog was not able to solve. Our experimental results show that our iterative deepening approach gets approximate bounded values in almost all cases and in most cases we are able to get the exact result for the same or one lower scaling factor.

Abstract
The secondary mirror unit of the European Extremely Large Telescope (ELT) is supported by six 50-cm wide spiders, providing the necessary stiffness to the structure while minimising the obstruction of the beam. The deformable quaternary mirror (M4) contains over 5000 actuators on a nearly hexagonal pattern. The reflective surface of M4 itself is composed of a segmented thin shell made of 6 discontinuous petals. This segmentation of the telescope pupil will create areas of phase isolated by the width of the spiders on the wavefront sensor (WFS) detector, breaking the spatial continuity of the wavefront data. The poor sensitivity of the Pyramid WFS (PWFS) to differential piston (or of any WFS sensitive to the derivative of the wavefront such as the Shack-Hartmann) will lead to badly seen and therefore uncontrollable differential pistons between these areas. In close loop operation, differential pistons between segments will settle around integer values of the average sensing wavelength lambda. The differential pistons typically range from one to tens of time the sensing wavelength and vary rapidly over time, leading to extremely poor performance. In addition, aberrations created by atmospheric turbulence will naturally contain some differential piston between the segments. This differential piston is typically a relatively large multiple of the sensing wavelength, especially for 40 m class telescopes. Trying to directly remove the entire piston contribution over each of the DM segments will undoubtedly lead to poor performance. In an attempt to reduce the impact of unwanted differential pistons that are injected by the AO correction, we compare three different approaches. A first step is to try to limit ourselves to use only the information measured by the PWFS, in particular by reducing the modulation. We show that using this information sensibly is important but it is only a prerequisite and will not be sufficient. We discuss possible ways of improvement by removing the unwanted differential pistons from the DM commands while still trying to maintain the atmospheric segment-piston contribution by using prior information. A second approach is based on phase closure of the DM commands and assumes the continuity of the correction wavefront over the entire unsegmented pupil. The last approach is based on the pair-wise slaving of edge actuators and shows the best results. We compare the performance of these methods using realistic end-to-end simulations. We find that pair-wise slaving leads to a small increase of the total wavefront error, only adding between 20-45 nm RMS in quadrature for seeing conditions between 0.45"-0.85". Finally, we discuss the possibility of combining the different proposed solutions to increase robustness.

Abstract

Abstract
Context-aware pervasive applications can improve user experiences by tracking people in their surroundings. Such systems use multiple sensors to gather information regarding people and devices. However, when developing novel user experiences, researchers are left to building foundation code to support multiple network-connected sensors, a major hurdle to rapidly developing and testing new ideas. We introduce Creepy Tracker, an open-source toolkit to ease prototyping with multiple commodity depth cameras. It automatically selects the best sensor to follow each person, handling occlusions and maximizing interaction space, while providing full-body tracking in scalable and extensible manners. It also keeps position and orientation of stationary interactive surfaces while offering continuously updated point-cloud user representations combining both depth and color data. Our performance evaluation shows that, although slightly less precise than marker-based optical systems, Creepy Tracker provides reliable multi-joint tracking without any wearable markers or special devices. Furthermore, implemented representative scenarios show that Creepy Tracker is well suited for deploying spatial and context-aware interactive experiences.

Abstract
Surveillance missions in vast, difficult access environments are responsible for logistic difficulties in comparison to using an in loco monitoring team. For this and many other reasons, solutions with robotic platforms such as unmanned aerial vehicles (UAVs), present economic advantages. © 1986-2012 IEEE.