The Redner - Ben-Avraham - Kahng cluster system

We consider a coagulation model first introduced by Redner, Ben-Avraham and Krapivsky in [Redner, Ben-Avraham, Kahng: Kinetics of 'cluster eating', J. Phys. A: Math. Gen., 20 (1987), 1231-1238], the main feature of which is that the reaction between a j-cluster and a k-cluster results in the creation of a |j-k|-cluster, and not, as in Smoluchowski's model, of a (j+k)-cluster. In this paper we prove existence and uniqueness of solutions under reasonably general conditions on the coagulation coefficients, and we also establish differenciability properties and continuous dependence of solutions. Some interesting invariance properties are also proved. Finally, we study the long-time behaviour of solutions, and also present a preliminary analysis of their scaling behaviour.Comment: 24 pages. 2 figures. Dedicated to Carlos Rocha and Luis Magalhaes on the occasion of their sixtieth birthday

The Redner - Ben-Avraham - Kahng coagulation system with constant coefficients: the finite dimensional case

We study the behaviour as $t\to\infty$ of solutions $(c_j(t))$ to the Redner--Ben-Avraham--Kahng coagulation system with positive and compactly supported initial data, rigorously proving and slightly extending results originally established in [4] by means of formal arguments.Comment: 13 pages, 1 figur

Same traits, different variance : Item-Level Variation Within Personality Measures

© 2014 the Author(s). This article has been published under the terms of the Creative Commons Attribution License. Without requesting permission from the Author or SAGE, you may further copy, distribute, transmit, and adapt the article, with the condition that the Author and SAGE Open are in each case credited as the source of the article. The version of record, Jamie S. Churcyard, Karen J. Pine, Shivani Sharma, Ben (C) Fletcher, ' Same Traits, Difference Variance: Item-Level Variation Within Personality Measures', SAGE Open, 2014, is available online via doi: 10.1177/2158244014522634Personality trait questionnaires are regularly used in individual differences research to examine personality scores between participants, although trait researchers tend to place little value on intra-individual variation in item ratings within a measured trait. The few studies that examine variability indices have not considered how they are related to a selection of psychological outcomes, so we recruited 160 participants (age M = 24.16, SD = 9.54) who completed the IPIP-HEXACO personality questionnaire and several outcome measures. Heterogenous within-subject differences in item ratings were found for every trait/facet measured, with measurement error that remained stable across the questionnaire. Within-subject standard deviations, calculated as measures of individual variation in specific item ratings within a trait/facet, were related to outcomes including life satisfaction and depression. This suggests these indices represent valid constructs of variability, and that researchers administering behavior statement trait questionnaires with outcome measures should also apply item-level variability indices.Peer reviewedFinal Published versio

Saber: window-based hybrid stream processing for heterogeneous architectures

Modern servers have become heterogeneous, often combining multicore CPUs with many-core GPGPUs. Such heterogeneous architectures have the potential to improve the performance of data-intensive stream processing applications, but they are not supported by current relational stream processing engines. For an engine to exploit a heterogeneous architecture, it must execute streaming SQL queries with sufficient data-parallelism to fully utilise all available heterogeneous processors, and decide how to use each in the most effective way. It must do this while respecting the semantics of streaming SQL queries, in particular with regard to window handling. We describe SABER, a hybrid high-performance relational stream processing engine for CPUs and GPGPUs. SABER executes windowbased streaming SQL queries in a data-parallel fashion using all available CPU and GPGPU cores. Instead of statically assigning query operators to heterogeneous processors, SABER employs a new adaptive heterogeneous lookahead scheduling strategy, which increases the share of queries executing on the processor that yields the highest performance. To hide data movement costs, SABER pipelines the transfer of stream data between different memory types and the CPU/GPGPU. Our experimental comparison against state-ofthe-art engines shows that SABER increases processing throughput while maintaining low latency for a wide range of streaming SQL queries with small and large windows sizes