847 research outputs found

    Exploring migration, diversification and urban transformation in contemporary Istanbul: The case of Kumkapı

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    The growing flows of international migration to Turkey have significant implications for the social, economic and spatial transformation of recipient cities across the country. This exploratory paper aims to highlight some of these implications, through discussing empirical findings from an ethnographic study carried out in an inner-city locality of Istanbul known as Kumkapı, which today has become a central hub of arrival, settlement and transit for various migrant groups. It raises four main points: 1) the emergence of migrant hubs are not accidental and the determining factors are multiple 2) they are highly diverse in new and complex ways, 3) within such areas multiple differences emerge and converge in shaping how space is made accessible, useful and meaningful, 4) diversification and informalities in migrant hubs are perceived and responded to in varying and conflicted ways

    Characterization of vegetation by microwave and optical remote sensing

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    Two series of carefully controlled experiments were conducted. First, plots of important crops (corn, soybeans, and sorghum), prairie grasses (big bluestem, switchgrass, tal fescue, orchardgrass, bromegrass), and forage legumes (alfalfa, red clover, and crown vetch) were manipulated to produce wide ranges of phytomass, leaf area index, and canopy architecture. Second, coniferous forest canopies were simulated using small balsam fir trees grown in large pots of soil and arranged systematically on a large (5 m) platform. Rotating the platform produced many new canopies for frequency and spatial averaging of the backscatter signal. In both series of experiments, backscatter of 5.0 GHz (C-Band) was measured as a function of view angle and polarization. Biophysical measurements included leaf area index, fresh and dry phytomass, water content of canopy elements, canopy height, and soil roughness and moisture content. For a subset of the above plots, additional measurements were acquired to exercise microwave backscatter models. These measurements included size and shape of leaves, stems, and fruit and the probability density function of leaf and stem angles. The relationships of the backscattering coefficients and the biophysical properties of the canopies were evaluated using statistical correlations, analysis of variance, and regression analysis. Results from the corn density and balsam fir experiments are discussed and analyses of data from the other experiments are summarized

    Soybean canopy reflectance modeling data sets

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    Numerous mathematical models of the interaction of radiation with vegetation canopies have been developed over the last two decades. However, data with which to exercise and validate these models are scarce. During three days in the summer of 1980, experiments are conducted with the objective of gaining insight about the effects of solar illumination and view angles on soybean canopy reflectance. In concert with these experiment, extensive measurements of the soybean canopies are obtained. This document is a compilation of the bidirectional reflectance factors, agronomic, characteristics, canopy geometry, and leaf, stem, and pod optical properties of the soybean canopies. These data sets should be suitable for use with most vegetation canopy reflectance models

    Supervised dimension reduction mappings

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    Some steps towards a general principle for dimensionality reduction mappings

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    Supervised dimension reduction mappings

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    Some steps towards a general principle for dimensionality reduction mappings

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    Analysis of dropout learning regarded as ensemble learning

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    Deep learning is the state-of-the-art in fields such as visual object recognition and speech recognition. This learning uses a large number of layers, huge number of units, and connections. Therefore, overfitting is a serious problem. To avoid this problem, dropout learning is proposed. Dropout learning neglects some inputs and hidden units in the learning process with a probability, p, and then, the neglected inputs and hidden units are combined with the learned network to express the final output. We find that the process of combining the neglected hidden units with the learned network can be regarded as ensemble learning, so we analyze dropout learning from this point of view.Comment: 9 pages, 8 figures, submitted to Conferenc
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