Dynamic Visual Abstraction of Soccer Movement

Trajectory-based visualization of coordinated movement data within a bounded area, such as player and ball movement within a soccer pitch, can easily result in visual crossings, overplotting, and clutter. Trajectory abstraction can help to cope with these issues, but it is a challenging problem to select the right level of abstraction (LoA) for a given data set and analysis task. We present a novel dynamic approach that combines trajectory simplification and clustering techniques with the goal to support interpretation and understanding of movement patterns. Our technique provides smooth transitions between different abstraction types that can be computed dynamically and on-the-fly. This enables the analyst to effectively navigate and explore the space of possible abstractions in large trajectory data sets. Additionally, we provide a proof of concept for supporting the analyst in determining the LoA semi-automatically with a recommender system. Our approach is illustrated and evaluated by case studies, quantitative measures, and expert feedback. We further demonstrate that it allows analysts to solve a variety of analysis tasks in the domain of soccer

Bring it to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis

Analysts in professional team sport regularly perform analysis to gain strategic and tactical insights into player and team behavior. Goals of team sport analysis regularly include identification of weaknesses of opposing teams, or assessing performance and improvement potential of a coached team. Current analysis workflows are typically based on the analysis of team videos. Also, analysts can rely on techniques from Information Visualization, to depict e.g., player or ball trajectories. However, video analysis is typically a time-consuming process, where the analyst needs to memorize and annotate scenes. In contrast, visualization typically relies on an abstract data model, often using abstract visual mappings, and is not directly linked to the observed movement context anymore. We propose a visual analytics system that tightly integrates team sport video recordings with abstract visualization of underlying trajectory data. We apply appropriate computer vision techniques to extract trajectory data from video input. Furthermore, we apply advanced trajectory and movement analysis techniques to derive relevant team sport analytic measures for region, event and player analysis in the case of soccer analysis. Our system seamlessly integrates video and visualization modalities, enabling analysts to draw on the advantages of both analysis forms. Several expert studies conducted with team sport analysts indicate the effectiveness of our integrated approach

First-principles study of TMNan (TM= Cr, Mn, Fe, Co, Ni; n = 4-7) clusters

Geometry, electronic structure, and magnetic properties of TMNan (TM=Cr-Ni; n = 4-7) clusters are studied within a gradient corrected density functional theory (DFT) framework. Two complementary approaches, the first adapted to all-electron calculations on free clusters, and the second been on plane wave projector augmented wave (PAW) method within a supercell approach are used. Except for NiNan, the clusters in this series are found to retain the atomic moments of the TM atoms, and the magnetic moment presented an odd-even oscillation with respect to the number of Na atoms. The origin of these odd-even oscillations is explained from the nature of chemical bonding in these clusters. Differences and similarities between the chemical bonding and the magnetic properties of these clusters and the TMNan (TM = Sc, V and Ti; n = 4-6) clusters on one hand, and TM-doped Au and Ag clusters on the other hand, are discussed

Model-Based Methods for Assessment, Learning, and Instruction: Innovative Educational Technology at Florida State University

Abstract In this chapter, we describe our research and development efforts relating to eliciting, representing, and analyzing how individuals and small groups conceptualize complex problems. The methods described herein have all been devel-oped and are in various states of being validated. In addition, the methods we describe have been automated and most have been integrated in an online model-based set of tools called HIMATT (Highly Interactive Model-based Assessment Tools and Technologies; available for research purposes a

Performance Analysis and Enabling of the RayBen Code for the Intel® MIC Architecture

The subject of this project is the analysis and enabling of the RayBen code, which implements a finite difference scheme for the simulation of turbulent Rayleigh-Bénard convection in a closed cylindrical cell, for the Intel® Xeon Phi coprocessor architecture. After a brief introduction to the physical background of the code, the integration of Rayben into the benchmarking environment JuBE is discussed. The structure of the code is analysed through its call graph. The most performance-critical routines were identified. A detailed analysis of the OpenMP parallelization revealed several race conditions which were eliminated. The code was ported to the JUROPA cluster at the Jülich Supercomputing as well as to the EURORA cluster at CINECA. The performance of the code is discussed using the results of pure MPI and hybrid MPI/OpenMP benchmarks. It is shown that RayBen is a memory-intensive application that highly benefits from the MPI parallelization. The offloading mechanism for the Intel® MIC architecture lowers considerably the performance while the use of binaries that run exclusively on the coprocessor show a satisfactory performance and a scalability which is comparable to the CPU