Cell size distribution in a random tessellation of space governed by the Kolmogorov-Johnson-Mehl-Avrami model: Grain size distribution in crystallization

The space subdivision in cells resulting from a process of random nucleation and growth is a subject of interest in many scientific fields. In this paper, we deduce the expected value and variance of these distributions while assuming that the space subdivision process is in accordance with the premises of the Kolmogorov-Johnson-Mehl-Avrami model. We have not imposed restrictions on the time dependency of nucleation and growth rates. We have also developed an approximate analytical cell size probability density function. Finally, we have applied our approach to the distributions resulting from solid phase crystallization under isochronal heating conditions

Detecció automàtica de punts facials característics utilitzant Random Ferns i el criteri de la información mútua simplificada

En aquest article comparem el rendiment que presenten dos sistemes de reconeixement de punts característics en imatges: en el primer utilitzem la tècnica Random Ferns bàsica i en el segon (que anomenem Ferns amb Informació Mútua o FIM) apliquem una tècnica d'obtenció de Ferns utilitzant un criteri simplificat de la informació mútua.

Laws of the Seventy-Ninth Regular Session of the General Assembly of the State of Iowa, 2001

The session laws for General Assembly 79.

Optimization of floor cleaning coverage performance of a random path-planning mobile robot

Floor cleaning is a typical robot application. There are several mobile robots aviable in the market for domestic applications most of them with random path-planning algorithms. In this paper we study the cleaning coverage performances of a random path-planning mobile robot and propose an optimized control algorithm, some methods to estimate the are of the room, the evolution of the cleaning and the time needed for complete coverage.

Sistema de votació electrònica sobre corbes el·líptques amb mescla de vots mitjançant Random Group Full Checking

L'objectiu del treball és dissenyar i implementar un sistema de simulació de votació electrònica, emprant una adaptació sobre corbes el·líptiques del criptosistema ElGamal, per tal d'estudiar-ne la viabilitat, centrant l'atenció en temes de seguretat, especialment en el procés de mescla de vots per tal de desvincular un vot de la persona que l'ha emès.

Tightness of random knotting.

Long polymers in solution frequently adopt knotted configurations. To understand the physical properties of knotted polymers, it is important to find out whether the knots formed at thermodynamic equilibrium are spread over the whole polymer chain or rather are localized as tight knots. We present here a method to analyze the knottedness of short linear portions of simulated random chains. Using this method, we observe that knot-determining domains are usually very tight, so that, for example, the preferred size of the trefoil-determining portions of knotted polymer chains corresponds to just seven freely jointed segments.

Predicting optimal lengths of random knots

In a thermally fluctuating long linear polymeric chain in a solution, the ends, from time to time, approach each other. At such an instance, the chain can be regarded as closed and thus will form a knot or rather a virtual knot. Several earlier studies of random knotting demonstrated that simpler knots show a higher occurrence for shorter random walks than do more complex knots. However, up to now there have been no rules that could be used to predict the optimal length of a random walk, i.e. the length for which a given knot reaches its highest occurrence. Using numerical simulations, we show here that a power law accurately describes the relation between the optimal lengths of random walks leading to the formation of different knots and the previously characterized lengths of ideal knots of a corresponding type.

A comparison of random sequence reads versus 16S rDNA sequences for estimating the biodiversity of a metagenomic library

The construction of metagenomic libraries has permitted the study of microorganisms resistant to isolation and the analysis of 16S rDNA sequences has been used for over two decades to examine bacterial biodiversity. Here, we show that the analysis of random sequence reads (RSRs) instead of 16S is a suitable shortcut to estimate the biodiversity of a bacterial community from metagenomic libraries. We generated 10,010 RSRs from a metagenomic library of microorganisms found in human faecal samples. Then searched them using the program BLASTN against a prokaryotic sequence database to assign a taxon to each RSR. The results were compared with those obtained by screening and analysing the clones containing 16S rDNA sequences in the whole library. We found that the biodiversity observed by RSR analysis is consistent with that obtained by 16S rDNA. We also show that RSRs are suitable to compare the biodiversity between different metagenomic libraries. RSRs can thus provide a good estimate of the biodiversity of a metagenomic library and, as an alternative to 16S, this approach is both faster and cheaper.

Irregular to regular sampling, denoising and deconvolution

We propose a restoration algorithm for band limited images that considers irregular(perturbed) sampling, denoising, and deconvolution. We explore the application of a family ofregularizers that allow to control the spectral behavior of the solution combined with the irregular toregular sampling algorithms proposed by H.G. Feichtinger, K. Gr¨ochenig, M. Rauth and T. Strohmer.Moreover, the constraints given by the image acquisition model are incorporated as a set of localconstraints. And the analysis of such constraints leads to an early stopping rule meant to improvethe speed of the algorithm. Finally we present experiments focused on the restoration of satellite images, where the micro-vibrations are responsible of the type of distortions we are considering here. We will compare results of the proposed method with previous methods and show an extension tozoom.

Multiple target tracking using random sets

This paper presents several algorithms for joint estimation of the target number and state in a time-varying scenario. Building on the results presented in [1], which considers estimation of the target number only, we assume that not only the target number, but also their state evolution must be estimated. In this context, we extend to this new scenario the Rao-Blackwellization procedure of [1] to compute Bayes recursions, thus defining reduced-complexity solutions for the multi-target set estimator. A performance assessmentis finally given both in terms of Circular Position Error Probability - aimed at evaluating the accuracy of the estimated track - and in terms of Cardinality Error Probability, aimed at evaluating the reliability of the target number estimates.

Non-random fertilization in mice correlates with the MHC and something else

One evolutionary explanation for the success of sexual reproduction assumes that sex is an advantage in the coevolutionary arms race between pathogens and hosts. Accordingly, an important criterion in mate choice and maternal selection thereafter could be the allelic specificity at polymorphic loci involved in parasite-host interactions, e.g. the MHC (major histocompatibility complex). The MHC has been found to influence mate choice and selective abortions in mice and humans. However, it could also influence the fertilization process itself, i.e. (i) the oocyte's choice for the fertilizing sperm, and (ii) the outcome of the second meiotic division after the sperm has entered the egg. We tested both hypotheses in an in vitro fertilization experiment with two inbred mouse strains congenic for their MHC. The genotypes of the resulting blastocysts were determined by polymerase chain reaction. We found nonrandom MHC combinations in the blastocysts which may result from both possible choice mechanisms. The outcome changed significantly over time, indicating that a choice for MHC combinations during fertilization may be influenced by one or several external factors.

Regular grammaticales ([Reprod.]) / Guarini Veronensis

Collection : Italian books before 1601 ; 86.26

Towards robust network based complex systems : from evolutionary cellular automata to biological models

Abstract Sitting between your past and your future doesn't mean you are in the present. Dakota Skye Complex systems science is an interdisciplinary field grouping under the same umbrella dynamical phenomena from social, natural or mathematical sciences. The emergence of a higher order organization or behavior, transcending that expected of the linear addition of the parts, is a key factor shared by all these systems. Most complex systems can be modeled as networks that represent the interactions amongst the system's components. In addition to the actual nature of the part's interactions, the intrinsic topological structure of underlying network is believed to play a crucial role in the remarkable emergent behaviors exhibited by the systems. Moreover, the topology is also a key a factor to explain the extraordinary flexibility and resilience to perturbations when applied to transmission and diffusion phenomena. In this work, we study the effect of different network structures on the performance and on the fault tolerance of systems in two different contexts. In the first part, we study cellular automata, which are a simple paradigm for distributed computation. Cellular automata are made of basic Boolean computational units, the cells; relying on simple rules and information from- the surrounding cells to perform a global task. The limited visibility of the cells can be modeled as a network, where interactions amongst cells are governed by an underlying structure, usually a regular one. In order to increase the performance of cellular automata, we chose to change its topology. We applied computational principles inspired by Darwinian evolution, called evolutionary algorithms, to alter the system's topological structure starting from either a regular or a random one. The outcome is remarkable, as the resulting topologies find themselves sharing properties of both regular and random network, and display similitudes Watts-Strogtz's small-world network found in social systems. Moreover, the performance and tolerance to probabilistic faults of our small-world like cellular automata surpasses that of regular ones. In the second part, we use the context of biological genetic regulatory networks and, in particular, Kauffman's random Boolean networks model. In some ways, this model is close to cellular automata, although is not expected to perform any task. Instead, it simulates the time-evolution of genetic regulation within living organisms under strict conditions. The original model, though very attractive by it's simplicity, suffered from important shortcomings unveiled by the recent advances in genetics and biology. We propose to use these new discoveries to improve the original model. Firstly, we have used artificial topologies believed to be closer to that of gene regulatory networks. We have also studied actual biological organisms, and used parts of their genetic regulatory networks in our models. Secondly, we have addressed the improbable full synchronicity of the event taking place on. Boolean networks and proposed a more biologically plausible cascading scheme. Finally, we tackled the actual Boolean functions of the model, i.e. the specifics of how genes activate according to the activity of upstream genes, and presented a new update function that takes into account the actual promoting and repressing effects of one gene on another. Our improved models demonstrate the expected, biologically sound, behavior of previous GRN model, yet with superior resistance to perturbations. We believe they are one step closer to the biological reality.