European Centre for Soft Computing

Laserpitium s.l. (Apiaceae family) is a taxonomically complex genus comprising 13–35 herbaceous species distributed through the North Hemisphere. Molecular studies based on nuclear markers and focused on the Scandiceae tribe divided this genus into various genera and revealed that their fruit (schizocarp) morphology generated non-monophyletic groups. In this context, some Iberian subspecies of Laserpitium latifolium, Laserpitium gallicum, Thapsia nestleri, Thapsia eliasii, and Silphiodaucus prutenicus subsp. dufourianus have an unclear taxonomic status, while Laserpitium longiradium genetic adscription has not been confirmed. We aim to determine their phylogenetic position within Laserpitium s.l. and their taxonomic status based on nuclear (ITS) and plastid (rpl16, trnL and trnL–trnF) markers, carpological data and biogeographic analyses. Our topologies and biogeographic analyses only validated one of these subspecies. Nevertheless, phylogenetic relationships, niche differences and biogeographic ranges supported the division of Thapsia s.l. into three genera: Iberian-French Thapsia s.s., a new Iberian genus (formed by Thapsia nestleri, Thapsia eliasii and Laserpitium longiradium) and the Mediterranean Elaeoselinum, in which various species have been included for the first time. The Iberian Silphiodaucus prutenicus subsp. dufourianus is a separate species. Finally, overlapping carpological features within Laserpitium s.l. further supports the schizocarp homoplastic character hypothesis.

The successful comparison of human skeletal remains with artistic or photographic replicas has been achieved many times using CFS. From the identification of well-known criminals, such as Adolf Hitler’s chief medical officer Dr. Josef Mengele (Teixeira 1985; Helmer 1987), to the identification of victims of the recent Indian Ocean tsunami (Al-Amad et al. 2006) and in the identification of terrorists (Indriati 2009), this technique has been under continued development since its first application in 1935 (Glaister and Brash 1937). Craniofacial superimposition has become increasingly relevant in forensic scenarios worldwide.

The conclusions of this handbook cannot be separated from the MEPROCS project where the manuscript was first conceived and then progressively developed by the team through a wide variety of tasks contributing to the final document.

Probability has, as a mathematical theory that is an important part of pure mathematics, a long and distinguished history of more than 300 years, with fertile applications in almost all domains of science and technology; but the history of fuzzy sets only lasts 50 years, during which it was theoretically developed and successfully applied in many fields. From the very beginning there was, and there still is, a controversy on the nature of fuzzy sets viewed by its researchers far from randomness, and instead close by probabilists. This paper only goal is nothing else than trying to contribute to the clarification on the differences its authors see between fuzzy sets and probabilities and through the representation, or scientific domestication, of meaning by quantities.

Abstract. We present the toolbox ClimateLearn to tackle problems in climate prediction using machine learning techniques and climate network analysis. The package allows basic operations of data mining, i.e. reading, merging, and cleaning data, and running machine learning algorithms such as multilayer artificial neural networks and symbolic regression with genetic programming. Because spatial temporal information on climate variability can be efficiently represented by complex network measures, such data are considered here as input to the machine-learning algorithms. As an example, the toolbox is applied to the prediction of the occurrence and the development of El Niño in the equatorial Pacific, first concentrating on the occurrence of El Niño events one year ahead and second on the evolution of sea surface temperature anomalies with a lead time of three months.

José Mariá Alonso.

This chapter is a large one. Not only because it introduces lots of additional material about the theory of fuzzy sets with respect to the previous chapter but because it includes too a big share of the Lisp functions that make up FuzzyLisp, so you will maybe find yourself a bit desperate trying to finish the chapter. Please don’t feel so. Try to enjoy it at your own pace; always remembering that in these moments you are just at the core of the book.

A symbolic calculus to evaluate the output signals at the target line(s) of quantum computing subcircuits using controlled negations and controlled‐ Q gates is introduced, where Q represents the k th root of [0 1; 1 0], the unitary matrix of NOT, and k is a power of two. The controlling signals are GF(2) expressions possibly including Boolean expressions. The method does not require operating with complex‐valued matrices. The method may be used to verify the functionality and to check for possible minimisation of a given quantum computing circuit using target lines. The method does not apply for a whole circuit if there are interactions among target lines. In this case the method applies for the independent subcircuits.

The question is, in fact, a philosophical one whose discussion does not correspond to this text, and that received a lot of comments and discussions by philosophers. Instead of such question, there is the more particular,what is a logic?, that can be answered not philosophicaly but in terms of the mathematical definition of what is a consequence’s operator. A definition that corresponds to an abstraction of the term “deduction”.