Flash-lag chimeras: the role of perceived alignment in the composite face effect

Spatial alignment of different face halves results in a configuration that mars the recognition of the identity of either face half (). What would happen to the recognition performance for face halves that were aligned on the retina but were perceived as misaligned, or were misaligned on the retina but were perceived as aligned? We used the 'flash-lag' effect () to address these questions. We created chimeras consisting of a stationary top half-face initially aligned with a moving bottom half-face. Flash-lag chimeras were better recognized than their stationary counterparts. However when flashed face halves were presented physically ahead of moving halves thereby nulling the flash-lag effect, recognition was impaired. This counters the notion that relative movement between the two face halves per se is sufficient to explain better recognition of flash-lag chimeras. Thus, the perceived spatial alignment of face halves (despite retinal misalignment) impairs recognition, while perceived misalignment (despite retinal alignment) does not

Quadruple Simulations of Thermographic Inspections of Impacted Composites

Thermography has been shown to be a viable technique for inspection of composites. Impact damage in composites typically contains multiple overlapping delaminations at different depths. Understanding the limitations of the thermographic inspection is enhanced by performing simulations of the technique. Most simulations of composite thermographic inspections have focused on simulations of a single delamination at a fixed depth. The quadrupole method has been shown as a viable technique for rapid three-dimensional thermographic simulations of a delamination. This method is expanded to enable rapid simulation of multiple overlapping delaminations at different depths. Quadrupole simulations are compared to finite element simulations of multiple delaminations at different depths. The simulations are also compared to the thermographic measurements on impacted composites where shape and depth of the delaminations are known from x-ray computed tomography data

ALOJA: A benchmarking and predictive platform for big data performance analysis

The main goals of the ALOJA research project from BSC-MSR, are to explore and automate the characterization of cost-effectivenessof Big Data deployments. The development of the project over its first year, has resulted in a open source benchmarking platform, an online public repository of results with over 42,000 Hadoop job runs, and web-based analytic tools to gather insights about system's cost-performance1. This article describes the evolution of the project's focus and research lines from over a year of continuously benchmarking Hadoop under dif- ferent configuration and deployments options, presents results, and dis cusses the motivation both technical and market-based of such changes. During this time, ALOJA's target has evolved from a previous low-level profiling of Hadoop runtime, passing through extensive benchmarking and evaluation of a large body of results via aggregation, to currently leveraging Predictive Analytics (PA) techniques. Modeling benchmark executions allow us to estimate the results of new or untested configu- rations or hardware set-ups automatically, by learning techniques from past observations saving in benchmarking time and costs.This work is partially supported the BSC-Microsoft Research Centre, the Span- ish Ministry of Education (TIN2012-34557), the MINECO Severo Ochoa Research program (SEV-2011-0067) and the Generalitat de Catalunya (2014-SGR-1051).Peer ReviewedPostprint (author's final draft