Academic Performance and Behavioral Patterns

Identifying the factors that influence academic performance is an essential part of educational research. Previous studies have documented the importance of personality traits, class attendance, and social network structure. Because most of these analyses were based on a single behavioral aspect and/or small sample sizes, there is currently no quantification of the interplay of these factors. Here, we study the academic performance among a cohort of 538 undergraduate students forming a single, densely connected social network. Our work is based on data collected using smartphones, which the students used as their primary phones for two years. The availability of multi-channel data from a single population allows us to directly compare the explanatory power of individual and social characteristics. We find that the most informative indicators of performance are based on social ties and that network indicators result in better model performance than individual characteristics (including both personality and class attendance). We confirm earlier findings that class attendance is the most important predictor among individual characteristics. Finally, our results suggest the presence of strong homophily and/or peer effects among university students

A Performance Analysis of Movement Patterns

This study investigates the differences in movement patterns followed by users navigating within a virtual environment. The analysis has been carried out between two groups of users, identified on the basis of their performance on a search task. Results indicate significant differences between efficient and inefficient navigators’ trajectories. They are related to rotational, translational and localised-landmarks behaviour. These findings are discussed in the light of theoretical outcomes provided by environmental psychology

On the hopping pattern design for D2D Discovery

The hopping pattern for D2D Discovery are investi- gated. We propose three metrics for hopping pattern performance evaluation: column period, maximal collision ratio, maximal con- tinual collision number. A class of hopping patterns is constructed based on the metrics, and through simulation the patterns show better discovery performance

Validation of hardware events for successful performance pattern identification in High Performance Computing

Hardware performance monitoring (HPM) is a crucial ingredient of performance analysis tools. While there are interfaces like LIKWID, PAPI or the kernel interface perf\_event which provide HPM access with some additional features, many higher level tools combine event counts with results retrieved from other sources like function call traces to derive (semi-)automatic performance advice. However, although HPM is available for x86 systems since the early 90s, only a small subset of the HPM features is used in practice. Performance patterns provide a more comprehensive approach, enabling the identification of various performance-limiting effects. Patterns address issues like bandwidth saturation, load imbalance, non-local data access in ccNUMA systems, or false sharing of cache lines. This work defines HPM event sets that are best suited to identify a selection of performance patterns on the Intel Haswell processor. We validate the chosen event sets for accuracy in order to arrive at a reliable pattern detection mechanism and point out shortcomings that cannot be easily circumvented due to bugs or limitations in the hardware

Group Communication Patterns for High Performance Computing in Scala

We developed a Functional object-oriented Parallel framework (FooPar) for high-level high-performance computing in Scala. Central to this framework are Distributed Memory Parallel Data structures (DPDs), i.e., collections of data distributed in a shared nothing system together with parallel operations on these data. In this paper, we first present FooPar's architecture and the idea of DPDs and group communications. Then, we show how DPDs can be implemented elegantly and efficiently in Scala based on the Traversable/Builder pattern, unifying Functional and Object-Oriented Programming. We prove the correctness and safety of one communication algorithm and show how specification testing (via ScalaCheck) can be used to bridge the gap between proof and implementation. Furthermore, we show that the group communication operations of FooPar outperform those of the MPJ Express open source MPI-bindings for Java, both asymptotically and empirically. FooPar has already been shown to be capable of achieving close-to-optimal performance for dense matrix-matrix multiplication via JNI. In this article, we present results on a parallel implementation of the Floyd-Warshall algorithm in FooPar, achieving more than 94 % efficiency compared to the serial version on a cluster using 100 cores for matrices of dimension 38000 x 38000

Sovereign Wealth Fund Investment Patterns and Performance

This study describes the newly created Monitor-FEEM Sovereign Wealth Fund Database and discusses the investment patterns and performance of 1,216 individual investments, worth over $357 billion, made by 35 sovereign wealth funds (SWFs) between January 1986 and September 2008. Approximately half of the investments we document occur after June 2005, reflecting a recent surge of SWF activity. We document large SWF investments in listed and unlisted equity, real estate, and private equity funds, with the bulk of investments being targeted in cross-border acquisitions of sizeable but non-controlling stakes in operating companies and commercial properties. The average (median) SWF investment is a$441 million ($55 million) acquisition of a 42.3$57billion on their holdings of listed stock investments alone through March 2009.Sovereign Wealth Funds, International Financial Markets, Government Policy and Regulation