Intelligent cores and intelligent agents: two approaches to achieve autonomy in swarm-array computing

How can a bridge be built between autonomic computing approaches and parallel computing systems? How can autonomic computing approaches be extended towards building reliable systems? How can existing technologies be merged to provide a solution for self-managing systems? The work reported in this paper aims to answer these questions by proposing Swarm-Array Computing, a novel technique inspired from swarm robotics and built on the foundations of autonomic and parallel computing paradigms. Two approaches based on intelligent cores and intelligent agents are proposed to achieve autonomy in parallel computing systems. The feasibility of the proposed approaches is validated on a multi-agent simulator.

Model selection approaches for non-linear system identification: a review

The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.

Functional verification: approaches and challenges

It's a fact that functional verification (FV) is paramount within the hardware's design cycle. With so many new techniques available today to help with FV, which techniques should we really use? The answer is not straightforward and is often confusing and costly. The tools and techniques to be used in a project have to be decided upon early in the design cycle to get the best value for these new verification methods. This paper gives a quick survey in the form of an overview on FV, establishes the difference between verification and validation, describes the bottlenecks that appear in the verification process, examines the challenges in FV and exposes the current FV technologies and trends.

Sex allocation and local mate competition in Old World non-pollinating fig wasps

The populations of many species are structured such that mating is not random and occurs between members of local patches. When patches are founded by a single female and all matings occur between siblings, brothers may compete with each other for matings with their sisters. This local mate competition (LMC) selects for a female-biased sex ratio, especially in species where females have control over offspring sex, as in the parasitic Hymenoptera. Two factors are predicted to decrease the degree of female bias: (1) an increase in the number of foundress females in the patch and (2) an increase in the fraction of individuals mating after dispersal from the natal patch. Pollinating fig wasps are well known as classic examples of species where all matings occur in the local patch. We studied non-pollinating fig wasps, which are more diverse than the pollinating fig wasps and also provide natural experimental groups of species with different male morphologies that are linked to different mating structures. In this group of wasps, species with wingless males mate in the local patch (i.e. the fig fruit) while winged male species mate after dispersal. Species with both kinds of male have a mixture of local and non-local mating. Data from 44 species show that sex ratios (defined as the proportion of males) are in accordance with theoretical predictions: wingless male species < wing-dimorphic male species < winged male species. These results are also supported by a formal comparative analysis that controls for phylogeny. The foundress number is difficult to estimate directly for non-pollinating fig wasps but a robust indirect method leads to the prediction that foundress number, and hence sex ratio, should increase with the proportion of patches occupied in a crop. This result is supported strongly across 19 species with wingless males, but not across 8 species with winged males. The mean sex ratios for species with winged males are not significantly different from 0.5, and the absence of the correlation observed across species with wingless males may reflect weak selection to adjust the sex ratio in species whose population mating structure tends not to be subdivided. The same relationship is also predicted to occur within species if individual females adjust their sex ratios facultatively. This final prediction was not supported by data from a wingless male species, a male wing-dimorphic species or a winged male species.

Learning and natal host influence host preference, handling time and sex allocation behaviour in a pupal parasitoid

The host choice and sex allocation decisions of a foraging female parasitoid will have an enormous influence on the life-history characteristics of her offspring. The pteromalid Pachycrepoideus vindemiae is a generalist idiobiont pupal parasitoid of many species of cyclorrhaphous Diptera. Wasps reared in Musca domestica were larger, had higher attack rates and greater male mating success than those reared in Drosophila melanogaster. In no-choice situations, naive female R vindemiae took significantly less time to accept hosts conspecific with their natal host. Parasitoids that emerged from M. domestica pupae spent similar amounts of time ovipositing in both D. melanogaster and M. domestica. Those parasitoids that had emerged from D. melanogaster spent significantly longer attacking M. domestica pupae. The host choice behaviour of female P. vindemiae was influenced by an interaction between natal host and experience. Female R vindemiae reared in M. domestica only showed a preference among hosts when allowed to gain experience attacking M. domestica, preferentially attacking that species. Similarly, female parasitoids reared on D. melanogaster only showed a preference among hosts when allowed to gain experience attacking D. melanogaster, again preferentially attacking that species. Wasp natal host also influenced sex allocation behaviour. While wasps from both hosts oviposited more females in the larger host, M. domestica, wasps that emerged from M. domestica had significantly more male-biased offspring sex ratios. These results indicate the importance of learning and natal host size in determining R vindemiae attack rates. mating success, host preference and sex allocation behaviour, all critical components of parasitoid fitness.

Physiological and proteomic approaches to address heat tolerance during anthesis in rice (Oryza sativa L.)

Episodes of high temperature at anthesis, which in rice is the most sensitive stage to temperature, are expected to occur more frequently in future climates. The morphology of the reproductive organs and pollen number, and changes in anther protein expression, were studied in response to high temperature at anthesis in three rice (Oryza sativa L.) genotypes. Plants were exposed to 6 h of high (38 °C) and control (29 °C) temperature at anthesis and spikelets collected for morphological and proteomic analysis. Moroberekan was the most heat-sensitive genotype (18% spikelet fertility at 38 °C), while IR64 (48%) and N22 (71%) were moderately and highly heat tolerant, respectively. There were significant differences among the genotypes in anther length and width, apical and basal pore lengths, apical pore area, and stigma and pistil length. Temperature also affected some of these traits, increasing anther pore size and reducing stigma length. Nonetheless, variation in the number of pollen on the stigma could not be related to measured morphological traits. Variation in spikelet fertility was highly correlated (r=0.97, n=6) with the proportion of spikelets with ≥20 germinated pollen grains on the stigma. A 2D-gel electrophoresis showed 46 protein spots changing in abundance, of which 13 differentially expressed protein spots were analysed by MS/MALDI-TOF. A cold and a heat shock protein were found significantly up-regulated in N22, and this may have contributed to the greater heat tolerance of N22. The role of differentially expressed proteins and morphology during anther dehiscence and pollination in shaping heat tolerance and susceptibility is discussed.

Resource allocation analysis of the stochastic diffusion search

The Stochastic Diffusion Search (SDS) was developed as a solution to the best-fit search problem. Thus, as a special case it is capable of solving the transform invariant pattern recognition problem. SDS is efficient and, although inherently probabilistic, produces very reliable solutions in widely ranging search conditions. However, to date a systematic formal investigation of its properties has not been carried out. This thesis addresses this problem. The thesis reports results pertaining to the global convergence of SDS as well as characterising its time complexity. However, the main emphasis of the work, reports on the resource allocation aspect of the Stochastic Diffusion Search operations. The thesis introduces a novel model of the algorithm, generalising an Ehrenfest Urn Model from statistical physics. This approach makes it possible to obtain a thorough characterisation of the response of the algorithm in terms of the parameters describing the search conditions in case of a unique best-fit pattern in the search space. This model is further generalised in order to account for different search conditions: two solutions in the search space and search for a unique solution in a noisy search space. Also an approximate solution in the case of two alternative solutions is proposed and compared with predictions of the extended Ehrenfest Urn model. The analysis performed enabled a quantitative characterisation of the Stochastic Diffusion Search in terms of exploration and exploitation of the search space. It appeared that SDS is biased towards the latter mode of operation. This novel perspective on the Stochastic Diffusion Search lead to an investigation of extensions of the standard SDS, which would strike a different balance between these two modes of search space processing. Thus, two novel algorithms were derived from the standard Stochastic Diffusion Search, ‘context-free’ and ‘context-sensitive’ SDS, and their properties were analysed with respect to resource allocation. It appeared that they shared some of the desired features of their predecessor but also possessed some properties not present in the classic SDS. The theory developed in the thesis was illustrated throughout with carefully chosen simulations of a best-fit search for a string pattern, a simple but representative domain, enabling careful control of search conditions.