Designing a benchmark for the performance evaluation of agent-based simulation applications on HPC

The Journal of Supercomputing - Agent-based modeling and simulation (ABMS) is a class of computational models for simulating the actions and interactions of autonomous agents with the goal of...     

Enacting archetypes in movies: grounding the unconscious mind in emotion-driven media

Abstract

The concept of emotion-driven media integrates affective computing into developing new types of digital media by including the human spectator as an essential part of the whole system. Enactive media, as a landmark in this direction, provides a theoretical basis that is rooted in the enactivist approach in cognitive science. Based on this thesis, it is required to establish a practical procedure for developing the media content through integrating psychological approaches. We demonstrate a systematic approach for developing the content for emotion-driven media. The theory of archetypes, one of the psychoanalytical approaches, was applied for editing movie clips as the media content, and coupled the content with emotional responses by using experimental psychological methodologies. The results showed that it is promising to utilise archetypal movie clips as the content of emotion-driven media and archetypal symbolism can be a useful resource for developing the content of media systems.     

Improving Performance on Data-Intensive Applications Using a Load Balancing Methodology Based on Divisible Load Theory

Data-intensive applications are those that explore, query, analyze, and, in general, process very large data sets. Generally, these applications can be naturally implemented in parallel but, in many cases, these implementations show severe performance problems mainly due to load imbalances, inefficient use of available resources, and improper data partition policies. It is worth noticing that the problem becomes more complex when the conditions causing these problems change at run time. This paper proposes a methodology for dynamically improving the performance of certain data-intensive applications based on: adapting the size and number of data partitions, and the number of processing nodes, to the current application conditions in homogeneous clusters. To this end, the processing of each exploration is monitored and gathered data is used to dynamically tune the performance of the application. The tuning parameters included in the methodology are: (i) the partition factor of the data set, (ii) the distribution of the data chunks, and (iii) the number of processing nodes to be used. The methodology assumes that a single execution includes multiple related explorations on the same partitioned data set, and that data chunks are ordered according to their processing times during the application execution to assign first the most time consuming partitions. The methodology has been validated using the well-known bioinformatics tool—BLAST—and through extensive experimentation using simulation. Reported results are encouraging in terms of reducing total execution time of the application (up to a 40 % in some cases).     

Biomimetic Architectures for the Impedimetric Discrimination of Influenza Virus Phenotypes

Rapid discrimination of avian vs. human phenotypes of emerging influenza A virus isolates with pandemic potential is an important issue in pathogenesis and epidemiology studies of the infection. In this work, functional architectures are tailored on the surface of a gold electrode, introducing receptor molecules as a sensing entity that mimics those found in the membrane of target cells of the influenza A virus and with the aim of developing an impedimetric‐based detector for influenza A virus phenotyping. In a bottom‐up approach, the artificial receptors are built by sequential assembly of a 1‐octanethiol/octyl‐galactoside hybrid bilayer, followed by an enzyme‐mediated functionalization of the terminal galactoside groups with sialic acid molecules. The detection mechanism relies hence on the specific affinity between the sialic acid‐galactose receptor moieties anchored on the modified electrode surface and the hemagglutinin (HA) viral surface protein. By using the appropriate type of sialyltransferase enzyme, sialylation of galactose residues is made through α‐2,3 or α‐2,6 linkages. This permits the envisaged impedimetric detector to discriminate rapidly between avian vs. human strains of influenza A virus with the absence of elaborate sample preparation steps. In contrast to immunosensors based on antibodies as bioreceptor, the sialylated modified gold electrode is also able to distinguish among influenza phenotypes, which could make the here presented detector a reagentless, label‐free diagnostic device for influenza phenotyping.     

SVM-based posture identification with a single waist-located triaxial accelerometer

Analysis of human body movement is an important research area, specially for health applications. In order to assess the quality of life of people with mobility problems like Parkinson’s disease o stroke patients, it is crucial to monitor and assess their daily life activities. The main goal of this work is the characterization of basic activities using a single triaxial accelerometer located at the waist. This paper presents a novel postural detection algorithm based in SVM methods which is able to detect and identify Walking, Stand, Sit, Lying, Sit to Stand, Stand to sit, Bending up/down, Lying from Sit and Sit from Lying transitions with a sensitivity of 97% and specificity of 84% with 2884 postures analyzed from 31 healthy volunteers. Parameters and models found have been tested in another dataset from Parkinson’s disease patients, achieving results of 98% of sensitivity and 78% of specificity in postural transitions. The proposed algorithm has been optimized to be easily implemented in real-time system for on-line monitoring applications.     

Synthesis of Ceria–Zirconia Nanocrystals with Improved Microstructural Homogeneity and Oxygen Storage Capacity by Hydrolytic Sol–Gel Process in Coordinating Environment

Ceria–zirconia solid solution nanocrystals, (1‐x)CeO₂–xZrO₂, 0 ≤ x ≤ 1, are prepared by sol–gel processing in dodecylamine of solutions obtained by forced hydrolysis of inorganic salts. The as‐prepared nanoparticles have a ceria cubic structure, up to x = 0.35, or are amorphous. Heat‐treatment is carried out at temperatures ranging from 500 to 800 °C, the latter temperature begin suitable to obtain solid solutions throughout the composition range. For all the heating temperatures and x values, the fluorite cubic structure of pure CeO₂ transforms to a mixture (c′) of the cubic c and tetragonal t″ phases for x = 0.35, and to tetragonal t phase only for x = 0.8 at 650 °C, x = 0.65 at 800 °C, and, to a very limited extent, x = 0.5 at 1000 °C. No evidence is obtained at low x values of the t phase, which is detrimental to the oxygen storage capacity. Prolonged heating at 1000 °C demonstrates that only for x = 0.65 a limited separation of CeO₂‐rich nanocrystals occurs. The samples undergo the same transition without simultaneous occurrence of different phases, apart for the two mentioned limited cases. This result is attributed to the intimate mixing of the metal cations even in the early stages of processing. In as‐prepared samples the Zr distribution becomes inhomogeneous when going from x = 0.2 to x = 0.35, but no early phase separations appear. The oxygen storage capacity is favorably influenced by the persistence of the cubic c′ phase.