4,962 research outputs found
Boiruna maculata
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
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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