13,556 research outputs found

    Improved solution of the lidar equation utilizing particle counter measurements

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    The extraction of particle backscattering from incoherent lidar measurements poses some problems. In the case of measurements of the stratospheric aerosol layer the solution of the lidar equation is based on two assumptions which are necessary to normalize the measured signal and to correct it with the two-way transmission of the laser pulse. Normalization and transmission are tackled by adding the information contained in aerosol particle counter measurements of the University of Wyoming to the ruby lidar measurements at Garmisch-Partenkirchen. Calculated backscattering from height levels above 25 km for the El Chichon period will be compared with lidar measurements and necessary corrections. The calculated backscatter-to-extinction ratios are compared to those, which were derived from a comparison of published extinction values to measured lidar backscattering at Garmisch. These ratios were used to calculate the Garmisch lidar returns. For the period 4 to 12 months after the El Chichon eruption a backscater-to-extinction ratio of 0.026 1/sr was applied with smaller values before and after that time

    Memory effects in vibrated granular systems

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    Granular materials present memory effects when submitted to tapping processes. These effects have been observed experimentally and are discussed here in the context of a general kind of model systems for compaction formulated at a mesoscopic level. The theoretical predictions qualitatively agree with the experimental results. As an example, a particular simple model is used for detailed calculations.Comment: 12 pages, 5 figures; to appear in Journal of Physics: Condensed Matter (Special Issue: Proceedings of ESF SPHINX Workshop on ``Glassy behaviour of kinetically constrained models.''

    Metastability of a granular surface in a spinning bucket

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    The surface shape of a spinning bucket of granular material is studied using a continuum model of surface flow developed by Bouchaud et al. and Mehta et al. An experimentally observed central subcritical region is reproduced by the model. The subcritical region occurs when a metastable surface becomes unstable via a nonlinear instability mechanism. The nonlinear instability mechanism destabilizes the surface in large systems while a linear instability mechanism is relevant for smaller systems. The range of angles in which the granular surface is metastable vanishes with increasing system size.Comment: 8 pages with postscript figures, RevTex, to appear in Phys. Rev.

    Thermal convection in mono-disperse and bi-disperse granular gases: A simulation study

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    We present results of a simulation study of inelastic hard-disks vibrated in a vertical container. An Event-Driven Molecular Dynamics method is developed for studying the onset of convection. Varying the relevant parameters (inelasticity, number of layers at rest, intensity of the gravity) we are able to obtain a qualitative agreement of our results with recent hydrodynamical predictions. Increasing the inelasticity, a first continuous transition from the absence of convection to one convective roll is observed, followed by a discontinuous transition to two convective rolls, with hysteretic behavior. At fixed inelasticity and increasing gravity, a transition from no convection to one roll can be evidenced. If the gravity is further increased, the roll is eventually suppressed. Increasing the number of monolayers the system eventually localizes mostly at the bottom of the box: in this case multiple convective rolls as well as surface waves appear. We analyze the density and temperature fields and study the existence of symmetry breaking in these fields in the direction perpendicular to the injection of energy. We also study a binary mixture of grains with different properties (inelasticity or diameters). The effect of changing the properties of one of the components is analyzed, together with density, temperature and temperature ratio fields. Finally, the presence of a low-fraction of quasi-elastic impurities is shown to determine a sharp transition between convective and non-convective steady states.Comment: 11 pages, 12 figures, accepted for publication on Physical Review

    Hierarchical Temporal Representation in Linear Reservoir Computing

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    Recently, studies on deep Reservoir Computing (RC) highlighted the role of layering in deep recurrent neural networks (RNNs). In this paper, the use of linear recurrent units allows us to bring more evidence on the intrinsic hierarchical temporal representation in deep RNNs through frequency analysis applied to the state signals. The potentiality of our approach is assessed on the class of Multiple Superimposed Oscillator tasks. Furthermore, our investigation provides useful insights to open a discussion on the main aspects that characterize the deep learning framework in the temporal domain.Comment: This is a pre-print of the paper submitted to the 27th Italian Workshop on Neural Networks, WIRN 201

    Sleep deprivation (SD) on focal brain ischemia in the rat : effects of different SD protocols

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    Sleep-wake disturbances are frequently observed in stroke patients and are associated with poorer functional outcome. Until now the effects of sleep on stroke evolution are unknown. The purpose of the present study was to evaluate the effects of three sleep deprivation (SD) protocols on brain damages after focal cerebral ischemia in a rat model. Permanent occlusion of distal branches of the middle cerebral artery was induced in adult rats. The animals were then subjected to 6h SD, 12h SD or sleep disturbances (SDis) in which 3 x 12h sleep deprivation were performed by gentle handling. Infarct size and brain swelling were assessed by Cresyl violet staining, and the number of damaged cells was measured by terminal deoxynucleotidyl transferase mediated dUTP nick end labeling (TUNEL) staining. Behavioral tests, namely tape removal and cylinder tests, were performed for assessing sensorimotor function. In the 6h SD protocol, no significant difference (P > 0.05) was found either in infarct size (42.5 ± 30.4 mm3 in sleep deprived animals vs. 44.5 ± 20.5 mm3 in controls, mean ± s.d.), in brain swelling (10.2 ± 3.8 % in sleep deprived animals vs. 11.3 ± 2.0 % in controls) or in number of TUNEL-positive cells (21.7 ± 2.0/mm2 in sleep deprived animals vs. 23.0 ± 1.1/mm2 in controls). In contrast, 12h sleep deprivation increased infarct size by 40 % (82.8 ± 10.9 mm3 in SD group vs. 59.2 ± 13.9 mm3 in control group, P = 0.008) and number of TUNEL-positive cells by 137 % (46.8 ± 15/mm in SD group vs. 19.7 ± 7.7/mm2 in control group, P = 0.003). There was no significant difference (P > 0.05) in brain swelling (12.9 ± 6.3 % in sleep deprived animals vs. 11.6 ± 6.0 % in controls). The SDis protocol also increased infarct size by 76 % (3 x 12h SD 58.8 ± 20.4 mm3 vs. no SD 33.8 ± 6.3 mm3, P = 0.017) and number of TUNEL-positive cells by 219 % (32.9 ± 13.2/mm2 vs. 10.3 ± 2.5/mm2, P = 0.008). Brain swelling did not show any difference between the two groups (24.5 ± 8.4 % in SD group vs. 16.7 ± 8.9 % in control group, p > 0.05). Both behavioral tests did not show any concluding results. In summary, we demonstrate that sleep deprivation aggravates brain damages in a rat model of stroke. Further experiments are needed to unveil the mechanisms underlying these effects

    Extreme Value Distribution Based Gene Selection Criteria for Discriminant Microarray Data Analysis Using Logistic Regression

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    One important issue commonly encountered in the analysis of microarray data is to decide which and how many genes should be selected for further studies. For discriminant microarray data analyses based on statistical models, such as the logistic regression models, gene selection can be accomplished by a comparison of the maximum likelihood of the model given the real data, L^(DM)\hat{L}(D|M), and the expected maximum likelihood of the model given an ensemble of surrogate data with randomly permuted label, L^(D0M)\hat{L}(D_0|M). Typically, the computational burden for obtaining L^(D0M)\hat{L}(D_0|M) is immense, often exceeding the limits of computing available resources by orders of magnitude. Here, we propose an approach that circumvents such heavy computations by mapping the simulation problem to an extreme-value problem. We present the derivation of an asymptotic distribution of the extreme-value as well as its mean, median, and variance. Using this distribution, we propose two gene selection criteria, and we apply them to two microarray datasets and three classification tasks for illustration.Comment: to be published in Journal of Computational Biology (2004
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