DYRE: a DYnamic REconfigurable solution to increase GPGPU’s reliability

The Journal of Supercomputing - General-purpose graphics processing units (GPGPUs) are extensively used in high-performance computing. However, it is well known that these devices’...     

Role of semiochemicals in mate location by parasitic sea louse,Lepeophtheirus salmonis

The role of olfaction and diffusible pheromones in mate location behavior of sea lice, Lepeophtheirus salmonis, was assessed with Y-tube behavioral bioassays. The pheromone emitting animals were located in a chamber in one arm of a Y-tube arena, with artificial seawater flowing through both arms. Adult male sea lice displayed both activation and directional responses to seawater conditioned with preadult II virgin females, but were only activated by mated adult female conditioned water. Further, when males were given the choice of preadult II virgin females or mated adult females, a significant number of males chose the arm with the preadult II virgin females. Adult males showed activation responses when presented with water conditioned with adult males but were not attracted to them. When presented with adult males, preadult II virgin females showed only directional responses, but not activation responses. Preadult II virgin female conditioned water was extracted using solid-phase extraction (SPE) protocols pioneered for semiochemical isolation. Adult male sea lice showed significant directional responses to the preadult II virgin female SPE extract. Distillation under vacuum was performed on the extract to give a distillate comprising components with a molecular weight range and physical properties comparable to those of compounds utilized as volatile semiochemicals by terrestrial organisms and a residue comprising components with higher molecular weight range comparable to those utilized as involatile semiochemicals. Adult males were found to be both significantly activated and attracted to the distillate, but not to the residue. This research provides evidence that small, lipophilic organic molecules are used by sea lice as sex pheromone signals to locate a member of the opposite sex.     

Autoregressive model-based fuzzy clustering and its application for detecting information redundancy in air pollution monitoring networks

Fuzzy clustering enables the simultaneous membership of objects in two or more clusters. This is particularly pertinent where time series are concerned, because very often patterns of time series change over time. Thus, a time series might belong to different clusters over different periods of time, in which case, crisp clustering is unable to capture this multi-cluster membership. In this paper, we adopt a Fuzzy C-Medoids approach to clustering time series based on autoregressive estimates of models fitted to the time series. We illustrate very good performance of this approach in a range of simulation studies. By means of two applications, we also show the usefulness of this clustering approach in the air pollution monitoring, by considering air pollution time series, i.e., CO time series, CO₂ time series and NO time series monitored on world and urban scales. In particular, we show that, by considering in the clustering process, the autoregressive representation of these air pollution time series, we are able to detect possible information redundancy in the monitoring networks and then, decreasing the number of monitoring stations, to reduce the monitoring costs and then to increase the monitoring efficiency of the networks.     

Fuzzy clustering of time series in the frequency domain

Traditional and fuzzy cluster analyses are applicable to variables whose values are uncorrelated. Hence, in order to cluster time series data which are usually serially correlated, one needs to extract features from the time series, the values of which are uncorrelated. The periodogram which is an estimator of the spectral density function of a time series is a feature that can be used in the cluster analysis of time series because its ordinates are uncorrelated. Additionally, the normalized periodogram and the logarithm of the normalized periodogram are also features that can be used. In this paper, we consider a fuzzy clustering approach for time series based on the estimated cepstrum. The cepstrum is the spectrum of the logarithm of the spectral density function. We show in our simulation studies for the typical generating processes that have been considered, fuzzy clustering based on the cepstral coefficients performs very well compared to when it is based on other features.     

Distributed and Collaborative Visualization

Visualization is a powerful tool for analyzing data and presenting results in science, engineering and medicine. This paper reviews ways in which it can be used in distributed and/or collaborative environments. Distributed visualization addresses a number of resource allocation problems, including the location of processing close to data for the minimization of data traffic. The advent of the Grid Computing paradigm and the link to Web Services provides fresh challenges and opportunities for distributed visualization—including the close coupling of simulations and visualizations in a steering environment. Recent developments in collaboration have seen the growth of specialized facilities (such as Access Grid) which have supplemented traditional desktop video conferencing using the Internet and multicast communications. Collaboration allows multiple users—possibly at remote sites—to take part in the visualization process at levels which range from the viewing of images to the shared control of the visualization methods. In this review, we present a model framework for distributed and collaborative visualization and assess a selection of visualization systems and frameworks for their use in a distributed or collaborative environment. We also discuss some examples of enabling technology and review recent work from research projects in this field.     

A least squares approach to principal component analysis for interval valued data

Principal Component Analysis (PCA) is a well-known technique, the aim of which is to synthesize huge amounts of numerical data by means of a low number of unobserved variables, called components. In this paper, an extension of PCA to deal with interval valued data is proposed. The method, called Midpoint Radius Principal Component Analysis (MR-PCA), recovers the underlying structure of interval valued data by using both the midpoints (or centers) and the radii (a measure of the interval width) information. In order to analyze how MR-PCA works, the results of a simulation study and two applications on chemical data are proposed.     

A formal framework for secure and complying services

Internet is offering a variety of services that are assembled to accomplish requests made by clients. While serving a request, security of the communications and of the data exchanged among services is crucial. Since communications occur along specific channels, it is equally important to guarantee that the interactions between a client and a server never get blocked because either cannot access a selected channel. We address here both these problems, from a formal point of view. A static analysis is presented, guaranteeing that a composition of a client and of possibly nested services respects both security policies for access control, and compliance between clients and servers.     

IEA Mobility Model (MoMo) and its use in the ETP 2008

The IEA published “Energy Technology Perspectives” (ETP) in June 2008. That document reports on IEA scenarios for baseline and low-CO₂ alternative scenarios to 2050, across the energy economy. The study included creating scenarios for transport, using the IEA Mobility Model (MoMo). This paper reports on the transport-related ETP scenarios and describes the model used in the analysis. According to the ETP Baseline scenario, world transport energy use and CO₂ emissions will more than double by 2050. In the most challenging scenario, called “BLUE”, transport emissions are reduced by 70% in 2050 compared to their baseline level in that year (and about 25% below their 2005 levels). There are several versions of the BLUE scenario, but all involve: a 50% or greater improvement in LDV efficiency, 30–50% improvement in efficiency of other modes (e.g. trucks, ships and aircraft), 25% substitution of liquid fossil fuels by biofuels, and considerable penetration of electric and/or fuel-cell vehicles. In the second half of this paper, an overview of the MoMo model is provided. Details on the complete analysis are contained in the ETP 2008 document, available at www.iea.org. Details of the LDV fuel economy analysis are contained in a separate paper in this collection.     

PolyMorph: Increasing the Spelling Efficiency of P300 by Selection Matrix PolyMorphism and Sentence-Based Predictions

One application of the P300 brain electric signal is sentence spelling, which enables subjects who have lost control of their motor pathways to communicate by selecting characters in a matrix containing all alphabet symbols. This technology still suffers from both low communication/high error rates. A P300 speller, named PolyMorph, which jointly introduces the selection matrix polymorphism (reducing the matrix size by removing useless symbols) and sentence-based predictions (which forecast words on the basis of language statistics) is presented. This is accomplished by using a custom dynamic knowledge-base, tailored to the subject lexicon, and updated in real time with the selections of the subject. The effectiveness of the presented speller is measured in vivo and in silico. The results suggest that the use of PolyMorph increases the number of spelt characters per time unit and reduces the error rate.