1,721,690 research outputs found
WIVACE 2008Workshop Italiano di Vita Artificiale e Computazione Evolutiva
No full textArtificial life (commonly Alife or alife) is a field of study and an associated art form which examine systems related to life, its processes, and its evolution through simulations using computer models, robotics, and biochemistry. There are three main kinds of alife, named for their approaches: soft, from software; hard, from hardware; and wet, from biochemistry. Artificial life imitates traditional biology by trying to recreate biological phenomena. The term "artificial life" is often used to specifically refer to soft alife.Artificial life studies the logic of living systems in artificial environments. The goal is to study the phenomena of living systems in order to come to an understanding of the complex information processing that defines such systems.Very often agent based systems are used in artificial life studies to observe the emergent properties of societies of agents
Contributions on evolutionary computation for statistical inference
Full text linkEvolutionary Computation (EC) techniques have been introduced in the 1960s for dealing with complex situations. One possible example is an optimization problems not having an analytical solution or being computationally intractable; in many cases such methods, named Evolutionary Algorithms (EAs), have been successfully implemented. In statistics there are many situations where complex problems arise, in particular concerning optimization. A general example is when the statistician needs to select, inside a prohibitively large discrete set, just one element, which could be a model, a partition, an experiment, or such: this would be the case of model selection, cluster analysis or design of experiment. In other situations there could be an intractable function of data, such as a likelihood, which needs to be maximized, as it happens in model parameter estimation. These kind of problems are naturally well suited for EAs, and in the last 20 years a large number of papers has been concerned with applications of EAs in tackling statistical issues.
The present dissertation is set in this part of literature, as it reports several implementations of EAs in statistics, although being mainly focused on statistical inference problems. Original results are proposed, as well as overviews and surveys on several topics. EAs are employed and analyzed considering various statistical points of view, showing and confirming their efficiency and flexibility.
The first proposal is devoted to parametric estimation problems. When EAs are employed in such analysis a novel form of variability related to their stochastic elements is introduced. We shall analyze both variability due to sampling, associated with selected estimator, and variability due to the EA. This analysis is set in a framework of statistical and computational tradeoff question, crucial in nowadays problems, by introducing cost functions related to both data acquisition and EA iterations. The proposed method will be illustrated by means of model building problem examples.
Subsequent chapter is concerned with EAs employed in Markov Chain Monte Carlo (MCMC) sampling. When sampling from multimodal or highly correlated distribution is concerned, in fact, a possible strategy suggests to run several chains in parallel, in order to improve their mixing. If these chains are allowed to interact with each other then many analogies with EC techniques can be observed, and this has led to research in many fields. The chapter aims at reviewing various methods found in literature which conjugates EC techniques and MCMC sampling, in order to identify specific and common procedures, and unifying them in a framework of EC.
In the last proposal we present a complex time series model and an identification procedure based on Genetic Algorithms (GAs). The model is capable of dealing with seasonality, by Periodic AutoRegressive (PAR) modelling, and structural changes in time, leading to a nonstationary structure. As far as a very large number of parameters and possibilites of change points are concerned, GAs are appropriate for identifying such model. Effectiveness of procedure is shown on both simulated data and real examples, these latter referred to river flow data in hydrology.
The thesis concludes with some final remarks, concerning also future work
Primers used in PCR for <i>POLI</i> gene.
No full text<p>Primers used in PCR for <i>POLI</i> gene.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
c-Jun activates and binds to the human <i>POLI</i> promoter.
No full text<p>(A) Luciferase activity of the deletions of 5′ flanking region of <i>POLI</i> gene was normalized by β-<i>gal</i> activity. Each bar represents the mean±SD for at least three independent experiments. (B) Schematic representations of the distal <i>POLI</i> promoter region. (C) Transcriptional activities of the deletion or mutant of <i>POLI</i> promoter. *Statistically significant difference compared to the pGL3-275/+63 and pGL3–275/+63MU construct (p<0.01; Student's t-test). (D) EMSA Assay. (E) ChIP assay. Mouse IgG as a negative control. The Input DNA or no DNA added was each used as positive and blank control, respectively.</p
ARTIFICIAL LIFE AND EVOLUTIONARY COMPUTATION
No full textPROCEEDINGS OF THE INTERNATIONAL CONFERENCE WIVACE 08, VENICE )ITALY) SEPTEMBER 8-10, 200
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
GANND: A Genetic Algorithm for Predictive Neural Network Design - A Financial Application, Economics & Complexity, 4
No full textIn this preliminary work on the application of Hybrid Algorithms to FinancialForecasting (HAF2) we show a computational approach integrating ArtificialNeural Networks (ANN) and Genetic Algorithms (Gas) facing the problem ofbuilding the optimal model in a multivariate non-linear environment. This is thetypical environment in financial and economical time series where the number ofvariables influencing a given phenomena is really high and moreover thefunctional relations linking them are not a-priori known.Because of their high flexibility Artificial Neural Networks (ANNs) have beenwidely used to build non linear regression models. However, the problem to buildthe best ANN is still open as well as the related problem of the best set ofvariables to be selected as regressors.In this paper we propose an hybrid algorithm (GANND, A Genetic Algorithm forNeural Network Design) integrating Genetic Algorithms and ANN toautomatically build an efficient predictive non linear model starting by theempirical data set. We also show some experimental results obtained by applyingGANND to predict an Italian financial bond (FIB30)
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