Abstract
This paper presents the AO performance we got on-sky with RAVEN, a Multi-Object Adaptive Optics (MOAO) technical and science demonstrator installed and tested at the Subaru telescope. We report Ensquared-Energy (EE) and Full Width at Half Maximum (FWHM) measured from science images on Subaru's IRCS taken during all of the on-sky observing runs. We show these metrics as function of different AO modes and atmospheric conditions for two asterisms of natural guide stars. The performances of the MOAO and Ground-Layer AO (GLAO) modes are between the classical Single-Conjugate AO (SCAO) and seeing-limited modes. We achieve the EE of 30% in H-band with the MOAO correction, which is a science requirement for RAVEN. The MOAO provides sightly better performance than the GLAO mode in both asterisms. One of the reasons which cause this small difference between the MOAO and GLAO modes may be the strong GL contribution. Also, the performance of the MOAO modes is affected by the accuracy of the on-sky turbulence profiling by the SLOpe Detection And Ranging (SLODAR) method.

Abstract
The CG research community has a renewed interest on rendering algorithms based on path space integration, mainly due to new approaches to discover, generate and exploit relevant light paths while keeping the numerical integrator unbiased or, at the very least, consistent. Simultaneously, the current trend towards massive parallelism and heterogeneous environments, based on a mix of conventional computing units with accelerators, is playing a major role both in HPC and embedded platforms. To efficiently use the available resources in these and future systems, algorithms and software packages are being revisited and reevaluated to assess their adequateness to these environments. This paper assesses the performance and scalability of three different path based algorithms running on homogeneous servers (dual multicore Xeons) and heterogeneous systems (those multicore plus manycore Xeon and NVidia Kepler GPU devices). These algorithms include path tracing (PT), its bidirectional counterpart (BPT) and the more recent Vertex Connect and Merge (VCM). Experimental results with two conventional scenes (one mainly diffuse, the other exhibiting specular-diffuse-specular paths) show that all algorithms scale well across the different platforms, the actual scalability depending on whether shared data structures are accessed or not (PT vs. BPT vs. VCM).

Abstract
Recent advances in the cost-efficiency of sequencing technologies enabled the combined DNA-and RNA-sequencing of human individuals at the population-scale, making genome-wide investigations of the inter-individual genetic impact on gene expression viable. Employing mRNA-sequencing data from the Geuvadis Project and genome sequencing data from the 1000 Genomes Project we show that the computational analysis of DNA sequences around splice sites and poly-A signals is able to explain several observations in the phenotype data. In contrast to widespread assessments of statistically significant associations between DNA polymorphisms and quantitative traits, we developed a computational tool to pinpoint the molecular mechanisms by which genetic markers drive variation in RNA-processing, cataloguing and classifying alleles that change the affinity of core RNA elements to their recognizing factors. The in silico models we employ further suggest RNA editing can moonlight as a splicing-modulator, albeit less frequently than genomic sequence diversity. Beyond existing annotations, we demonstrate that the ultra-high resolution of RNA-Seq combined from 462 individuals also provides evidence for thousands of bona fide novel elements of RNA processing-alternative splice sites, introns, and cleavage sites-which are often rare and lowly expressed but in other characteristics similar to their annotated counterparts.

Abstract
This work demonstrates the viability of using a cavity ring-down technique (CRD) for remote sensing. A conventional CRD configuration is used where and optical circulator is added inside the fibre loop to couple 20 km of optical fibre with a gold mirror at its end with the purpose of remote sensing. As a proof-of-concept, an intensity sensor based on an eight-figure configuration is used at the end of the 20 km of fibre for displacement sensing. In this case, a commercial OTDR is used as modulated light source to send impulses down to the fibre ring.

Abstract
Currently, a learning management system (LMS) plays a central role in any e-learning environment. These environments include systems to handle the pedagogic aspects of the teaching-learning process (e.g. specialized tutors, simulation games) and the academic aspects (e.g. academic management systems). Thus, the potential for interoperability is an important, although over looked, aspect of an LMS. In this paper, we make a comparative study of the interoperability level of the most relevant LMS. We start by defining an application and a specification model. For the application model, we create a basic application that acts as a tool provider for LMS integration. The specification model acts as the API that the LMS should implement to communicate with the tool provider. Based on researches, we select the Learning Tools Interoperability (LTI) from IMS. Finally, we compare the LMS interoperability level defined as the effort made to integrate the application on the study LMS.

Abstract
When an autonomous vehicle is traveling through some scenario it receives a continuous stream of sensor data. This sensor data arrives in an asynchronous fashion and often contains overlapping or redundant information. Thus, it is not trivial how a representation of the environment observed by the vehicle can be created and updated over time. This paper presents a novel methodology to compute an incremental 3D representation of a scenario from 3D range measurements. We propose to use macro scale polygonal primitives to model the scenario. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Furthermore, we propose mechanisms designed to update the geometric polygonal primitives over time whenever fresh sensor data is collected. Results show that the approach is capable of producing accurate descriptions of the scene, and that it is computationally very efficient when compared to other reconstruction techniques.