4,962 research outputs found

    Boiruna maculata

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    This species has a wide distribution in southern SouthAmerica (Bolivia, Paraguay, Uruguay, Brazil, and Argentina).Boiruna maculata is known in Argentina from 18-39°S and 55-69°W in the provinces of Jujuy, Salta, Formosa, Chaco, Santiagodel Estero, Tucumán, Catamarca, La Rioja, Córdoba, Santa Fe, Misiones,Corrientes, Entre Ríos, Mendoza, San Luis, and La Pampa(Scott et al. 2006. Pap. Avul. Zool. 46:77-105); it was reported fromSan Juan without voucher (Acosta et al. 2017. Los Reptiles de SanJuan. Editorial Universidad de San Juan, Argentina. 132 pp.). Firstvouchered province record, filling the gap between Los Molinos,La Rioja Province (28.80709°S; 66.94130°W; 215 km to the north),Lafinur, San Luis Province (32.06671°S, 65.33335°W, 250 km tothe southeast), and Cerro Bola, Mendoza Province (34.64775°S,68.58387°W, 450 km to the south) the nearest records of the species(Scott et al. 2006, op. cit.). It also represents the first recordfrom the natural protected area Parque Provincial Valle Fértil.Fil: Laspiur, Julio Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ciencias Exactas Físicas y Naturales. Departamento de Biología; ArgentinaFil: Nenda, Santiago Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales “Bernardino Rivadavia”; Argentin

    Axiomatic Construction of Hierarchical Clustering in Asymmetric Networks

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    This paper considers networks where relationships between nodes are represented by directed dissimilarities. The goal is to study methods for the determination of hierarchical clusters, i.e., a family of nested partitions indexed by a connectivity parameter, induced by the given dissimilarity structures. Our construction of hierarchical clustering methods is based on defining admissible methods to be those methods that abide by the axioms of value - nodes in a network with two nodes are clustered together at the maximum of the two dissimilarities between them - and transformation - when dissimilarities are reduced, the network may become more clustered but not less. Several admissible methods are constructed and two particular methods, termed reciprocal and nonreciprocal clustering, are shown to provide upper and lower bounds in the space of admissible methods. Alternative clustering methodologies and axioms are further considered. Allowing the outcome of hierarchical clustering to be asymmetric, so that it matches the asymmetry of the original data, leads to the inception of quasi-clustering methods. The existence of a unique quasi-clustering method is shown. Allowing clustering in a two-node network to proceed at the minimum of the two dissimilarities generates an alternative axiomatic construction. There is a unique clustering method in this case too. The paper also develops algorithms for the computation of hierarchical clusters using matrix powers on a min-max dioid algebra and studies the stability of the methods proposed. We proved that most of the methods introduced in this paper are such that similar networks yield similar hierarchical clustering results. Algorithms are exemplified through their application to networks describing internal migration within states of the United States (U.S.) and the interrelation between sectors of the U.S. economy.Comment: This is a largely extended version of the previous conference submission under the same title. The current version contains the material in the previous version (published in ICASSP 2013) as well as material presented at the Asilomar Conference on Signal, Systems, and Computers 2013, GlobalSIP 2013, and ICML 2014. Also, unpublished material is included in the current versio
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