4-7-19, stade Pershing, départ du marathon [modifié des Jeux interalliés, en tête n°1103 Auguste Broos et n°973 Carlo Speroni suivi par Fred Faller, puis n°846 Clyde Stout, à sa droite Jean Vermeulen, n°730 J. H. Massey, n°1189 Thomas Sinton Hewitt, n°971 Armando Pagliani, n°1280 Alphonse Van Hoey, n°972 Antenore Negri, n°1274 Jules Holsbeeke] : [photographie de presse] / [Agence Rol]

Référence bibliographique : Rol, 54713

[1-6-19, l'Américain Earl William] Eby gagnant du 400 m [championnats de l'armée américaine à Colombes] : [photographie de presse] / [Agence Rol]

Référence bibliographique : Rol, 54199

[1-6-19, l'Américain Earl William] Eby gagnant du 400 m [championnats d'athlétisme de l'armée américaine à Colombes] : [photographie de presse] / [Agence Rol]

Référence bibliographique : Rol, 54198

[Earl William] Eby, Américain, 2ème, [Daniel Leslie] Mason, Nouvelle-Zélande, 1er du 800 [mètres des Jeux interalliés, stade Pershing, le 4 juillet 1919] : [photographie de presse] / [Agence Rol]

Référence bibliographique : Rol, 54702

Apreçamento de opções sobre taxa de câmbio R$/US$ negociadas no Brasil: uma comparação entre os modelos Black e redes neurais artificiais

No estudo aqui apresentado, aplicou-se um modelo de rede neural multicamadas para o apreçamento de calls sobre taxa de câmbio R$/US$, negociadas na Bolsa de Valores, Mercadorias & Futuros de São Paulo (BM&FBovespa), para o período de janeiro de 2004 a dezembro de 2007. A partir dos preços efetivamente praticados no mercado, comparou-se o desempenho entre essa técnica e o modelo de Black, utilizando-se métricas usuais de erro e testes estatísticos. Os resultados obtidos revelaram, em geral, a melhor adequação do modelo de inteligência artificial, em comparação ao modelo de Black, nos diferentes graus de moneyness.

Implementing statistical learning methods through Bayesian networks (Part 2): bayesian evaluations for results of black toner analyses in forensic document examination

This paper presents and discusses the use of Bayesian procedures - introduced through the use of Bayesian networks in Part I of this series of papers - for 'learning' probabilities from data. The discussion will relate to a set of real data on characteristics of black toners commonly used in printing and copying devices. Particular attention is drawn to the incorporation of the proposed procedures as an integral part in probabilistic inference schemes (notably in the form of Bayesian networks) that are intended to address uncertainties related to particular propositions of interest (e.g., whether or not a sample originates from a particular source). The conceptual tenets of the proposed methodologies are presented along with aspects of their practical implementation using currently available Bayesian network software.

Phase-sensitive black-blood coronary vessel wall imaging.

Black-blood MR coronary vessel wall imaging may become a powerful tool for the quantitative and noninvasive assessment of atherosclerosis and positive arterial remodeling. Although dual-inversion recovery is currently the gold standard, optimal lumen-to-vessel wall contrast is sometimes difficult to obtain, and the time window available for imaging is limited due to competing requirements between blood signal nulling time and period of minimal myocardial motion. Further, atherosclerosis is a spatially heterogeneous disease, and imaging at multiple anatomic levels of the coronary circulation is mandatory. However, this requirement of enhanced volumetric coverage comes at the expense of scanning time. Phase-sensitive inversion recovery has shown to be very valuable for enhancing tissue-tissue contrast and for making inversion recovery imaging less sensitive to tissue signal nulling time. This work enables multislice black-blood coronary vessel wall imaging in a single breath hold by extending phase-sensitive inversion recovery to phase-sensitive dual-inversion recovery, by combining it with spiral imaging and yet relaxing constraints related to blood signal nulling time and period of minimal myocardial motion.

Black Hawk County, June 30, 2002

County Audit Report

Black Hawk County Criminal Justice Information System, June 30, 2002

Other Audit Reports

Black Hawk County Criminal Justice Information System, June 30, 2003

Other Audit Report - 28E Organization

An Analysis of black-box optimization problems in reinsurance: evolutionary-based approaches

Black-box optimization problems (BBOP) are de ned as those optimization problems in which the objective function does not have an algebraic expression, but it is the output of a system (usually a computer program). This paper is focussed on BBOPs that arise in the eld of insurance, and more speci cally in reinsurance problems. In this area, the complexity of the models and assumptions considered to de ne the reinsurance rules and conditions produces hard black-box optimization problems, that must be solved in order to obtain the optimal output of the reinsurance. The application of traditional optimization approaches is not possible in BBOP, so new computational paradigms must be applied to solve these problems. In this paper we show the performance of two evolutionary-based techniques (Evolutionary Programming and Particle Swarm Optimization). We provide an analysis in three BBOP in reinsurance, where the evolutionary-based approaches exhibit an excellent behaviour, nding the optimal solution within a fraction of the computational cost used by inspection or enumeration methods.