Ising models of deep neural networks

This work maps deep neural networks to classical Ising spin models, allowing them to be described using statistical thermodynamics. The density of states shows that structures emerge in the weights after they have been trained -- well-trained networks span a much wider range of realizable energies compared to poorly trained ones. These structures propagate throughout the entire network and are not observed in individual layers. The energy values correlate to performance on tasks, making it possible to distinguish networks based on quality without access to data. Thermodynamic properties such as specific heat are also studied, revealing a higher critical temperature in trained networks

Long-term correlations in hourly wind speed records in Pernambuco, Brazil

AbstractWe study the statistical properties of hourly wind speed time series detected at four weather stations in the state of Pernambuco, Brazil, in the period 2008–2009. We find that the average and maximum hourly wind speeds deviate from a mutual linear relationship, and that they may be well explained individually by a Weibull distribution, however, with different shape parameter values. On the other hand, the long-term correlations of both of these observables obey the same power-law behavior, with two distinct scaling regimes. Our results agree with previous studies on wind speed series correlations in other regions of the world, which is suggestive of universal behavior

Peeling the Otolith of Fish: Optimal Parameterization for Micro-CT Scanning

In this paper, we aim to provide optimal parameters for micro-computed tomography scans of fish otoliths. We tested fifteen different combinations to sagittae. The images were scaled to Hounsfield units, and segmented in two distinct volumes-of-interest (external and internal). The strategy we applied, for identifying optimum scan settings for otoliths, included analyses of the sinogram, the distribution of the Hounsfield units and the signal-to-noise ratio. Based on these tests, the optimum sets of parameters for the acquisition of tomographic images of sagittal otoilths were 80 kV, 220 μA, and 0.5 mm aluminum filter. The method allowed 3D shape analysis, internal and external density distribution, layer-by-layer density segmentation, and a potential objective method to count growth rings in otoliths. It was possible to compare mean densities between species, and we observed a significant difference among them. In addition, there are ontogenic changes, which could be increasing or decreasing the density. In this study, we applied tomography for several otolith analysis, that could be of great interest for future studies in diverse areas that use otoliths as the basic structure of analysis, or represents a new research line called eco-densitometry of otoliths, where tomography could be applied to explore the density within an ecological perspective.Fil: Vasconcelos Filho, Jonas. Universidad Federal Rural Pernambuco; BrasilFil: Thomsen, Felix Sebastian Leo. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; ArgentinaFil: Stosic, Borko. Universidad Federal Rural Pernambuco; BrasilFil: Antonino, Antonio C. D.. Universidad Federal Rural Pernambuco; BrasilFil: Duarte, Daniel A.. Universidad Federal Rural Pernambuco; BrasilFil: Heck, Richard J.. University of Guelph; CanadáFil: Lessa, Rosangela P. T.. Universidad Federal Rural Pernambuco; BrasilFil: Santana, Francisco M.. Universidad Federal Rural Pernambuco; BrasilFil: Ferreira, Beatrice P.. Universidad Federal Rural Pernambuco; BrasilFil: Duarte Neto, Paulo J.. Universidad Federal Rural Pernambuco; Brasi