In astrophysical N-body simulations, Dehnen's algorithm, implemented in the serial falcON code and based on a dual tree traversal, is faster than serial Barnes-Hut tree-codes, but outperformed by parallel CPU and GPU tree-codes. In this paper, we present a parallel dual tree traversal, implemented in the pfalcON code, targeting multi-core CPUs and manycore architectures (Xeon Phi). We focus here on both performance and portability, while preserving Dehnen's original algorithm. We first use task parallelism, with either OpenMP or Intel TBB, for the dual tree traversal. We then rely on the SPMD (single-program, multipledata) model for the SIMD vectorization of the near field part thanks to the Intel SPMD Program Compiler. We compare the pfalcON performance to related work, and finally obtain performance results that match one of the best current tree-code implementations on GPU.
Last years have seen a growing interest on the Serious Games topic-and in particular on Games for Health-from both scientific and industrial communities. However not only the effectiveness of this kind of games is not yet demonstrated but the distribution and adoption of these games from the normal public is still very low. In this paper we present a design strategy we adopted in on the occasion of the development of a game for hemiplegic rehabilitation named "Hammer and Planks". This game strategy allowed us to create a "game for all", as will be demonstrated by the example of the usage of the game on the occasion of a game event in the south of France.
The use of computer vision techniques to build hands-free input devices has long been a topic of interest to researchers in the field of natural interaction. In recent years Microsoft's Kinect has brought these technologies to the layman, but the most commonly used libraries for Kinect human pose recognition are closed-source. There is not yet an accepted, effective open-source alternative upon which highly specific applications can be based. We propose a novel technique for extracting the appendage configurations of users from the Kinect camera's depth feed, based on stochastic local search techniques rather than per-pixel classification.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.