Abstract: Simulation of age progression of 3D head models is an open problem in the field of computer graphics. Existing methods usually require a large set of training data, which may not be available. In this paper, a method for aging simulation of models created by deformation-based modeling is proposed that requires no training data. A user defines the position of wrinkles by selecting the position of endpoints of the desired wrinkles and the wrinkles are then generated using an erosion-inspired approach. The method can be used to simulate aging of any head model, however, if used for models created by deformations of a base model, the erosion factors can be calculated only for the base model and applied to the derived models. The results show that the approach is capable of creating visually plausible aged models.

Abstract: In this paper, we demonstrate how to extend perfect spatial hashing (PSH) to the problem domain of indexing nodes in a graph that represents of Super Carbon Nanotubes (SCNTs). The goal of PSH is to hash multidimensional data without collisions. Since PSH results from the research on computer graphics, its principles and methods have only been tested on 2− and 3−dimensional problems. In our case, we need to hash up to 28 dimensions. In contrast to the original applications of PSH, we do not focus on GPUs as target hardware but on an efficient CPU implementation. Thus, this paper highlights the extensions to the original algorithm to make it suitable for higher dimensions and the representation of SCNTs. Comparing the compression and performance results of the new PSH based graphs and a structure-tailored custom data structure in our parallelized SCNT simulation software, we find, that PSH in some cases achieves better compression by a factor of 1.7 while only increasing the total runtime by several percent. In particular, after our extension, PSH can also be employed to index sparse multidimensional scientific data from other domains.

Abstract: Nowadays, as large amounts of data are generated, either from experiments, satellite imagery or via simulations, access to this data becomes challenging for users who need to further process them, since existing data management makes it difficult to effectively access and share large data sets. In this paper we present an approach to enabling easy and secure collaborations based on the state of the art authentication and authorization mechanisms, advanced group/role mechanism for flexible authorization management and support for identity mapping between local systems, as applied in an eventually consistent distributed file system called Onedata.

Abstract: Predicting the compressive strength of concrete is an essential task in the construction process, since a prior knowledge on such information helps enhancing speed and quality of the process. Recently, many computational methods and techniques have been developed to predict distinct properties of concrete. However, a practical use of these solutions requires a high degree of engineering expertise and programming skills. Alternatively, this work advocates that software packages with off-the-shelf data mining algorithms can empower researchers and engineers on this task, while demanding less effort. In this direction, we present a detailed study on the use of Weka, evaluating different regression algorithms for predicting the compressive strength of concrete. Using the most complete dataset available at the UCI dataset repository, we demonstrate that most of the techniques available in Weka produces results close to the best ones reported in the literature. For instance, most of the evaluated predicting models generates a Mean Absolute Error (MAE) inferior to 10, while the best result found is 8. Moreover, by fine-tuning the parameters of the regression algorithm Bagging with REPTree, we achieved a MAE value inferior to 3.3 for the evaluated dataset. Hence, the process considered in this study is also useful as a guideline to devise new computational models based on off-the-shelf data mining algorithms.

Abstract: Large scale time-dependent particle simulations can generate massive amounts of data, making it so that storing the results is often the slowest phase and the primary time bottleneck of the simulation. Furthermore, analysing this amount of data with traditional tools has become increasingly challenging, and it is often virtually impossible to have a visual representation of the full set. We propose a novel architecture that integrates a HPC-based multi-physics simulation code, a NoSQL database, and a data analysis and visualisation application. The goals are two: On the one hand, we aim to speed up the simulations taking advantage of the scalability of key-value data stores, while at the same time enabling real-time approximated data visualisation and interactive exploration. On the other hand, we want to make it efficient to explore and analyse the large data base of results produced. Therefore, this work represents a clear example of integrating High Performance Computing with High Performance Data Analytics. Our prototype proves the validity of our approach and shows great performance improvements. Indeed, we reduced by 67.5% the time to store the simulation while we made real-time queries run 52 times faster than alternative solutions.