Invariant measures for Cherry flows

We investigate the invariant probability measures for Cherry flows, i.e. flows on the two-torus which have a saddle, a source, and no other fixed points, closed orbits or homoclinic orbits. In the case when the saddle is dissipative or conservative we show that the only invariant probability measures are the Dirac measures at the two fixed points, and the Dirac measure at the saddle is the physical measure. In the other case we prove that there exists also an invariant probability measure supported on the quasi-minimal set, we discuss some situations when this other invariant measure is the physical measure, and conjecture that this is always the case. The main techniques used are the study of the integrability of the return time with respect to the invariant measure of the return map to a closed transversal to the flow, and the study of the close returns near the saddle.Comment: 12 pages; updated versio

Matching fields of a long superconducting film

We obtain the vortex configurations, the matching fields and the magnetization of a superconducting film with a finite cross section. The applied magnetic field is normal to this cross section, and we use London theory to calculate many of its properties, such as the local magnetic field, the free energy and the induction for the mixed state. Thus previous similar theoretical works, done for an infinitely long superconducting film, are recovered here, in the special limit of a very long cross section.Comment: Contains a REVTeX file and 4 figure

Sensibilidade e especificidade dos classificadores de aprendizagem de máquina para o diagnóstico de glaucoma usando Spectral Domain OCT e perimetria automatizada acromática

PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.OBJETIVO: Avaliar a sensibilidade e especificidade dos classificadores de aprendizagem de máquina no diagnóstico de glaucoma usando Spectral Domain OCT (SD-OCT) e perimetria automatizada acromática (PAA). MÉTODOS: Estudo transversal observacional. Sessenta e dois pacientes com glaucoma e 48 indivíduos normais foram incluídos. Todos os pacientes foram submetidos a exame oftalmológico completo, e perimetria automatizada acromática (24-2 SITA; Humphrey Field Analyzer II, Carl Zeiss Meditec, Inc., Dublin, CA) e exame de imagem da camada de fibras nervosas utilizando SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Curvas ROC (Receiver operator characteristic) foram obtidas para todos os parâmetros do SD-OCT e índices globais do campo visual (MD, PSD, GHT). Subsequentemente, os seguintes classificadores de aprendizagem de máquina (CAMs) foram testados usando parâmetros do OCT e CV: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA), Support Vector Machine Linear (SVML) e Support Vector Machine Gaussian (SVMG). Áreas abaixo da curva ROC (aROC) obtidas com os parâmetros isolados do campo visual (CV) e OCT foram comparados com os CAMs usando dados associados do OCT e CV. RESULTADOS: Combinando os dados do OCT e do CV, aROCs dos CAMs variaram entre 0,777(CTREE) e 0,946 (RAN). A maior aROC dos CAMs OCT+CV obtida com RAN (0,946) foi significativamente maior que o melhor parâmetro do OCT (p<0,05), mas não houve diferença estatística significativa com o melhor parâmetro do CV (p=0,19). CONCLUSÃO: Os classificadores de aprendizagem de máquina treinados com dados do OCT e CV podem discriminar entre olhos normais e glaucomatosos com sucesso. A combinação das medidas do OCT e CV melhoraram a acurácia diagnóstica comparados aos parâmetros do OCT.17017

Sensitivity And Specificity Of Machine Learning Classifiers For Glaucoma Diagnosis Using Spectral Domain Oct And Standard Automated Perimetry.

To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.76170-

On the statistical and transport properties of a non-dissipative Fermi-Ulam model

The transport and diffusion properties for the velocity of a Fermi-Ulam model were characterized using the decay rate of the survival probability. The system consists of an ensemble of non-interacting particles confined to move along and experience elastic collisions with two infinitely heavy walls. One is fixed, working as a returning mechanism of the colliding particles, while the other one moves periodically in time. The diffusion equation is solved, and the diffusion coefficient is numerically estimated by means of the averaged square velocity. Our results show remarkably good agreement of the theory and simulation for the chaotic sea below the first elliptic island in the phase space. From the decay rates of the survival probability, we obtained transport properties that can be extended to other nonlinear mappings, as well to billiard problems.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES

Tolerance to water deficit of cowpea genotypes

Objetivou-se, neste trabalho, avaliar o efeito do déficit hídrico sobre as características fisiológicas e produtivas do feijão-caupi e selecionar genótipos tolerantes à seca. Avaliaram-se a condutância estomática, o potencial hídrico foliar, a temperatura das folhas e a produtividade de grãos de 20 genótipos de feijão-caupi nas condições de solo e clima de Teresina, Piauí, no ano de 2008 e se conduziram dois experimentos em delineamento de blocos ao acaso com 20 tratamentos e quatro repetições, um sob déficit hídrico durante a fase reprodutiva e outro sob irrigação plena, para fins de comparação. O déficit hídrico, que foi obtido aplicando-se aproximadamente metade da lâmina requerida pelo feijão-caupi, reduziu em 72% a condutância estomática, 62% o potencial de água nas folhas, 60% a produção de grãos e aumentou em 11,7% a temperatura foliar. Nas condições de déficit hídrico treze genótipos produziram acima da média (466 kg ha-1), com destaque para o BRS-Paraguaçu, Pingo-de-ouro-1-2 e Pingo-de-ouro-2, que produziram 712 kg ha-1, 667 kg ha-1 e 642 kg ha-1, respectivamente. Em média, a produtividade de grãos dos genótipos sob irrigação plena foi 150% superior.The objective of this study was to evaluate physiological and productive characteristics of cowpea under water deficit and total irrigation, under soil and climate conditions of Teresina, Piauí State, in 2008. The stomatal conductance, leaf water potential, leaf temperature and grain yield of twenty cowpea cultivars were evaluated. Two experiments were carried out in a randomized block design with 20 treatments and four replications, one under water deficit during the reproductive phase and another one under total irrigation. The water deficit was obtained applying half of the water depth required by cowpea. The water deficit reduced 72% of the stomatal conductance, 40% the leaf transpiration, 62% of the leaf water potential, 60% of the grain yield and increased 11.7% the leaf temperature. Under water deficit, 13 genotypes produced above average (466 kg ha-1), and BRS-Paraguaçu, Pingo-de-ouro-1-2 and Pingo-de-ouro-2 presented the best grain yield with712 kg ha-1, 667 kg ha-1 and 642 kg ha-1, respectively. The average grain yield of the experiment under total irrigation was 150% higher

Coastal mixing and optics experiment moored array data report

To investigate vertical mixing processes influencing the evolution of the stratification over continental shelves a moored array was deployed on the New England shelf from August 1996 to June 1997 as part of the Office of Naval Research's Coastal Mixing and Optics program. The array consisted of four mid-shelf sites instrumented to measure oceanic (currents, temperature, salinity, pressure, and surface gravity wave spectra) and meteorological (winds, surface heat flux, precipitation) variables. This report presents a description of the moored array, a summary of the data processing, and statistics and time-series plots summarizing the data. A report on the mooring recovery cruise and a summary of shipboard CTD surveys taken during the mooring deployment are also included.Funding was provided by the Office of Naval Research under Contract No. N00014-95-1-0339

Estudo da CLP Urbana por Meio de um Modelo de Fechamento de Segunda Ordem Unidimensional

Um modelo de fechamento de segunda ordem (MFSO) acopladoa um modelo de dossel urbano (MDU) é utilizado para estudar o papel daocupação do solo de uma região urbana sobre o balanço de energia na superfíciee sobre a estrutura vertical da camada limite planetária (CLP). Emtodos os casos o dossel é considerado homogêneo e sem topografia. Aevolução temporal da altura da CLP e da temperatura na superfície e osperfis verticais de temperatura potencial e energia cinética turbulenta (ECT)são discutidos