Taxonomy and nomenclature of the bronzed cowbird, currently called Tangavius aeneus [by] Kenneth C. Parkes and Emmet R. Blake.

v.44:no.22(1964)

A generic and tribal revision of the North American Aleocharinae (Coleoptera, Staphylinidae) / Charles H. Seevers ; with additions and annotations by Lee H. Herman.

v.71(1978)

Taxonomic guide to the polychaetes of the northern Gulf of Mexico / prepared by Joan M. Uebelacker and Paul G. Johnson, editors ; prepared under MMS contract 14-12-001-29091 for Minerals Management Service, U.S. Dept. of the Interior.

4

New genera and subgenera of augochlorine bees (Hymenoptera: Halictidae) / by Michael S. Engel, Robert W. Brooks, and Douglas Yanega.

no.5

New species of the genera Lispinus and Neolosus (Staphylinidae: Osoriinae) from the Neotropics / by Ulrich Irmler.

no.21

Genus-group names of bees and supplemental family-group names / by Charles D. Michener.

no.1

A revision of the genus Elmas Blackwelder, 1952 (Coleoptera:Staphylinidae:Staphylininae:Xanthopygina) : with a preliminary reconstructed phylogeny of the species / by James S. Ashe and Stylianos Chatzimanolis.

no.28

A nonlinear threshold model for the dependence of extremes of stationary sequences

One of the main implications of the efficient market hypothesis (EMH) is that expected future returns on financial assets are not predictable if investors are risk neutral. In this paper we argue that financial time series offer more information than that this hypothesis seems to supply. In particular we postulate that runs of very large returns can be predictable for small time periods. In order to prove this we propose a TAR(3,1)-GARCH(1,1) model that is able to describe two different types of extreme events: a first type generated by large uncertainty regimes where runs of extremes are not predictable and a second type where extremes come from isolated dread/joy events. This model is new in the literature in nonlinear processes. Its novelty resides on two features of the model that make it different from previous TAR methodologies. The regimes are motivated by the occurrence of extreme values and the threshold variable is defined by the shock affecting the process in the preceding period. In this way this model is able to uncover dependence and clustering of extremes in high as well as in low volatility periods. This model is tested with data from General Motors stocks prices corresponding to two crises that had a substantial impact in financial markets worldwide; the Black Monday of October 1987 and September 11th, 2001. By analyzing the periods around these crises we find evidence of statistical significance of our model and thereby of predictability of extremes for September 11th but not for Black Monday. These findings support the hypotheses of a big negative event producing runs of negative returns in the first case, and of the burst of a worldwide stock market bubble in the second example. JEL classification: C12; C15; C22; C51 Keywords and Phrases: asymmetries, crises, extreme values, hypothesis testing, leverage effect, nonlinearities, threshold models

Generic object classification for autonomous robots

Un dels principals problemes de la interacció dels robots autònoms és el coneixement de l'escena. El reconeixement és fonamental per a solucionar aquest problema i permetre als robots interactuar en un escenari no controlat. En aquest document presentem una aplicació pràctica de la captura d'objectes, de la normalització i de la classificació de senyals triangulars i circulars. El sistema s'introdueix en el robot Aibo de Sony per a millorar-ne la interacció. La metodologia presentada s'ha comprobat en simulacions i problemes de categorització reals, com ara la classificació de senyals de trànsit, amb resultats molt prometedors.

Segmentation and Clustering of Textual Sequences: a Typological Approach

The long term goal of this research is to develop a program able to produce an automatic segmentation and categorization of textual sequences into discourse types. In this preliminary contribution, we present the construction of an algorithm which takes a segmented text as input and attempts to produce a categorization of sequences, such as narrative, argumentative, descriptive and so on. Also, this work aims at investigating a possible convergence between the typological approach developed in particular in the field of text and discourse analysis in French by Adam (2008) and Bronckart (1997) and unsupervised statistical learning.

Jatobal virus antigenic characterization by ELISA and neutralization test using EIA as indicator, on tissue culture

A virus antigenic characterization methodology using an indirect method of antibody detection ELISA with virus-infected cultured cells as antigen and a micro virus neutralisation test using EIA (NT-EIA) as an aid to reading were used for antigenic characterization of Jatobal (BeAn 423380). Jatobal virus was characterized as a Bunyaviridae, Bunyavirus genus, Simbu serogroup virus. ELISA using infected cultured cells as antigen is a sensitive and reliable method for identification of viruses and has many advantages over conventional antibody capture ELISA's and other tests: it eliminates solid phase coating with virus and laborious antigen preparation; it permits screening of large numbers of virus antisera faster and more easily than by CF, HAI, or plaque reduction NT. ELISA and NT using EIA as an aid to reading can be applicable to viruses which do not produce cytopathogenic effect. Both techniques are applicable to identification of viruses which grow in mosquito cells.

Urban image classification with semisupervised multiscale cluster kernels

This paper presents a semisupervised support vector machine (SVM) that integrates the information of both labeled and unlabeled pixels efficiently. Method's performance is illustrated in the relevant problem of very high resolution image classification of urban areas. The SVM is trained with the linear combination of two kernels: a base kernel working only with labeled examples is deformed by a likelihood kernel encoding similarities between labeled and unlabeled examples. Results obtained on very high resolution (VHR) multispectral and hyperspectral images show the relevance of the method in the context of urban image classification. Also, its simplicity and the few parameters involved make the method versatile and workable by unexperienced users.