26 resultados para Structure learning
Resumo:
Size distributions in woody plant populations have been used to assess their regeneration status, assuming that size structures with reverse-J shapes represent stable populations. We present an empirical approach of this issue using five woody species from the Cerrado. Considering count data for all plants of these five species over a 12-year period, we analyzed size distribution by: a) plotting frequency distributions and their adjustment to the negative exponential curve and b) calculating the Gini coefficient. To look for a relationship between size structure and future trends, we considered the size structures from the first census year. We analyzed changes in number over time and performed a simple population viability analysis, which gives the mean population growth rate, its variance and the probability of extinction in a given time period. Frequency distributions and the Gini coefficient were not able to predict future trends in population numbers. We recommend that managers should not use measures of size structure as a basis for management decisions without applying more appropriate demographic studies.
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We analyzed the structure of the understory community in the Atlantic Forest sensu lato, for which phytosociological descriptions of the understory are lacking. We delineated 50 plots of 10 × 20 m each at four sites within an Araucaria forest (a subtype of Atlantic Forest), located in the municipalities of Bananal, Campos do Jordão, Itaberá and Barra do Chapéu, all of which are in the state of São Paulo, Brazil. To sample the resident species of the understory, we randomly selected five 1 × 1 m subplots within each plot, resulting in a total sampling area of 250 m² at each site. We identified differences among the locations, mostly due to proportional differences in growth forms, in terms of species richness and the importance values within the community. Factors potentially influencing the understory structure include macroclimatic and microclimatic conditions, as well as forest fragmentation, the abundance of deciduous trees in the canopy, the surrounding vegetation and geographic location.
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This paper discusses theoretical results of the research project Linguistic Identity and Identification: A Study of Functions of Second Language in Enunciating Subject Constitution. Non-cognitive factors that have a crucial incidence in the degree of success and ways of accomplishment of second language acquisition process are focused. A transdisciplinary perspective is adopted, mobilising categories from Discourse Analysis and Psychoanalysis. The most relevant ones are: discursive formation, intradiscourse, interdiscourse, forgetting n° 1, forgetting n° 2 (Pêcheux, 1982), identity, identification (Freud, 1966; Lacan, 1977; Nasio, 1995). Revuz s views (1991) are discussed. Her main claim is that during the process of learning a foreign language, the foundations of psychical structure, and consequently first language, are required. After examining how nomination and predication processes work in first and second languages, components of identity and identification processes are focused on, in an attempt to show how second language acquisition strategies depend on them. It is stated that methodological affairs of language teaching, learner s explicit motivation and the like are subordinated to the comprehension of deeper non-cognitive factors that determine the accomplishment of the second language acquisition process. It is also pointed out that those factors are to be approached, questioning the bipolar biological-social conception of subjectivity in the study of language acquisition and use and including in the analysis symbolic and significant dimensions of the discourse constitution process.
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PURPOSE: To determine the mean critical fusion frequency and the short-term fluctuation, to analyze the influence of age, gender, and the learning effect in healthy subjects undergoing flicker perimetry. METHODS: Study 1 - 95 healthy subjects underwent flicker perimetry once in one eye. Mean critical fusion frequency values were compared between genders, and the influence of age was evaluated using linear regression analysis. Study 2 - 20 healthy subjects underwent flicker perimetry 5 times in one eye. The first 3 sessions were separated by an interval of 1 to 30 days, whereas the last 3 sessions were performed within the same day. The first 3 sessions were used to investigate the presence of a learning effect, whereas the last 3 tests were used to calculate short-term fluctuation. RESULTS: Study 1 - Linear regression analysis demonstrated that mean global, foveal, central, and critical fusion frequency per quadrant significantly decreased with age (p<0.05).There were no statistically significant differences in mean critical fusion frequency values between males and females (p>0.05), with the exception of the central area and inferonasal quadrant (p=0.049 and p=0.011, respectively), where the values were lower in females. Study 2 - Mean global (p=0.014), central (p=0.008), and peripheral (p=0.03) critical fusion frequency were significantly lower in the first session compared to the second and third sessions. The mean global short-term fluctuation was 5.06±1.13 Hz, the mean interindividual and intraindividual variabilities were 11.2±2.8% and 6.4±1.5%, respectively. CONCLUSION: This study suggests that, in healthy subjects, critical fusion frequency decreases with age, that flicker perimetry is associated with a learning effect, and that a moderately high short-term fluctuation is expected.
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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.
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Universidade Estadual de Campinas. Faculdade de Educação Física
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Universidade Estadual de Campinas . Faculdade de Educação Física
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Universidade Estadual de Campinas. Faculdade de Educação Física
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Universidade Estadual de Campinas . Faculdade de Educação Física
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Universidade Estadual de Campinas . Faculdade de Educação Física
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Universidade Estadual de Campinas . Faculdade de Educação Física