Abstract
Prime numbers play a pivotal role in current encryption algorithms and given the rise of cloud computing, the need for larger primes has never been so high. This increase in available computation power can be used to either try to break the encryption or to strength it by finding larger prime numbers. With this in mind, this paper provides an analysis of different sieve implementations that can be used to generate primes to near 2(64). It starts by analyzing cache friendly sequential sieves with wheel factorization, then expands to multi-core architectures and ends with a cache friendly segmented hybrid implementation of a distributed prime sieve, designed to efficiently use all the available computation resources of heterogeneous computer clusters with variable workload and to scale very well in both the shared and distributed memory versions.

Abstract
The use of Augmented Reality (AR) techniques to visualize virtual archaeological sites is neither a new or recent issue. In those approaches the virtual models are only visualized using the existent in situ illumination, which does not allow a visitor to have a similar visual experience to that which he would have at the time the structures were built. In Augmented Virtuality (AV) approaches the virtual world prevails, which is augmented with information from the real world, which allows a better control over the parameters of the Mixed Reality (MR) environment created. In some cases, there is the need to use both approaches (AR or AV), depending on some context conditions. This paper proposes an architecture and an information system for an adaptive MR system which main goal is to visualize in situ virtual reconstructions of archaeological sites that are seamlessly merged with the real scene. In this context, a new adaptive methodology will be defined to manage the level of mixing between the real and the virtual scene, identifying in each instant the most proper approach to use (AR or AV), as well as defining the way how transitions between approaches are made. (C) 2014 The Authors. Published by Elsevier Ltd.

Abstract
The quantification of low rate active tectonic structures is a major target of geodetic and geological studies to improve the knowledge of seismic hazards. The central Betic Cordillera (southern Spain) is affected by moderately active tectonic structures and seismicity. Part of this seismic activity is produced by several NW-SE normal faults located in the E of the Granada Basin. Here, we apply Multi-temporal InSAR (MTI) data to quantify the deformation produced by the Granada fault and the Padul fault zones and the surrounding area. The Granada NW-SE active normal fault zone, 17 km in length, crosses the city of Granada, a very sensitive area from a seismic hazard point of view due to the population of the Granada town. At the Padul fault, there is no geodetic evidence of contemporary motion. Considering the evidence of recent activity from geologic data, this fault may experience discontinuous motion with a different seismogenic character. Despite the InSAR uncertainties, InSAR results are consistent with the estimated geologic deformation rates lower than 1 mm/yr. Our results also confirm previous InSAR studies in the Otura area showing an estimated average annual velocity along the SAR line-of-sight of up to 10 mm/year anthropogenic subsidence. (C) 2014 The Authors. Published by Elsevier Ltd.

Abstract

Abstract
Three-dimensional object detection and recognition is increasingly in manipulation and navigation applications in autonomous service robots. It involves clustering points of the point cloud from an unstructured scene into objects candidates and estimating features to recognize the objects under different circumstances such as occlusions and clutter. This paper presents an efficient approach capable of learning and recognizing object categories in an interactive and open-ended manner. In this paper, "open-ended" implies that the set of object categories to be learned is not known in advance. The training instances are extracted from actual experiences of a robot, and thus become gradually available, rather than being available at the beginning of the learning process. This paper focuses on two state-of-the-art questions: (1) How to automatically detect, conceptualize and recognize objects in 3D unstructured scenes in an open-ended manner? (2) How to acquire and utilize high-level knowledge obtained from the user (e. g. category label) to improve the system performance? This approach starts with a pre-processing phase to remove unnecessary information and prepare a suitable point cloud. Clustering is then applied to detect object candidates. Subsequently, all object candidates are described based on a 3D shape descriptor called spin-image. Finally, a nearest-neighbor classification rule is used to assign category labels to the detected objects. To examine the performance of the proposed approach, a leave-one-out cross validation algorithm is utilized to compute precision and recall. The experimental results show the fulfilling performance of this approach on different types of objects.

Abstract
This paper addresses the problem of grounding semantic representations in intelligent service robots. In particular, this work contributes to addressing two important aspects, namely the anchoring of object symbols into the perception of the objects and the grounding of object category symbols into the perception of known instances of the categories. The paper discusses memory requirements for storing both semantic and perceptual data and, based on the analysis of these requirements, proposes an approach based on two memory components, namely a semantic memory and a perceptual memory. The perception, memory, learning and interaction capabilities, and the perceptual memory, are the main focus of the paper. Three main design options address the key computational issues involved in processing and storing perception data: a lightweight, NoSQL database, is used to implement the perceptual memory; a thread-based approach with zero copy transport of messages is used in implementing the modules; and a multiplexing scheme, for the processing of the different objects in the scene, enables parallelization. The system is designed to acquire new object categories in an incremental and open-ended way based on user-mediated experiences. The system is fully integrated in a broader robot system comprising low-level control and reactivity to high-level reasoning and learning.