A fast version of the k-means classification algorithm for astronomical applications

Context. K-means is a clustering algorithm that has been used to classify large datasets in astronomical databases. It is an unsupervised method, able to cope very different types of problems. Aims. We check whether a variant of the algorithm called single-pass k-means can be used as a fast alternative to the traditional k-means. Methods. The execution time of the two algorithms are compared when classifying subsets drawn from the SDSS-DR7 catalog of galaxy spectra. Results. Single-pass k-means turn out to be between 20 % and 40 % faster than k-means and provide statistically equivalent classifications. This conclusion can be scaled up to other larger databases because the execution time of both algorithms increases linearly with the number of objects. Conclusions. Single-pass k-means can be safely used as a fast alternative to k-means

Convectively driven vortex flows in the Sun

We have discovered small whirlpools in the Sun, with a size similar to the terrestrial hurricanes (<~0.5 Mm). The theory of solar convection predicts them, but they had remained elusive so far. The vortex flows are created at the downdrafts where the plasma returns to the solar interior after cooling down, and we detect them because some magnetic bright points (BPs) follow a logarithmic spiral in their way to be engulfed by a downdraft. Our disk center observations show 0.009 vortexes per Mm^2, with a lifetime of the order of 5 min, and with no preferred sense of rotation. They are not evenly spread out over the surface, but they seem to trace the supergranulation and the mesogranulation. These observed properties are strongly biased by our type of measurement, unable to detect vortexes except when they are engulfing magnetic BPs.Comment: Accepted for publication in ApJL. An animation showing one of the whirlpools can be found at http://www.iac.es/proyecto/solarhr/whirlpools.mp

Initial pseudo-steady state & asymptotic KPZ universality in semiconductor on polymer deposition

The Kardar-Parisi-Zhang (KPZ) class is a paradigmatic example of universality in nonequilibrium phenomena, but clear experimental evidences of asymptotic 2D-KPZ statistics are still very rare, and far less understanding stems from its short-time behavior. We tackle such issues by analyzing surface fluctuations of CdTe films deposited on polymeric substrates, based on a huge spatio-temporal surface sampling acquired through atomic force microscopy. A \textit{pseudo}-steady state (where average surface roughness and spatial correlations stay constant in time) is observed at initial times, persisting up to deposition of $\sim 10^{4}$ monolayers. This state results from a fine balance between roughening and smoothening, as supported by a phenomenological growth model. KPZ statistics arises at long times, thoroughly verified by universal exponents, spatial covariance and several distributions. Recent theoretical generalizations of the Family-Vicsek scaling and the emergence of log-normal distributions during interface growth are experimentally confirmed. These results confirm that high vacuum vapor deposition of CdTe constitutes a genuine 2D-KPZ system, and expand our knowledge about possible substrate-induced short-time behaviors.Comment: 13 pages, 8 figures, 2 table

EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices

In recent years, advances in deep learning have resulted in unprecedented leaps in diverse tasks spanning from speech and object recognition to context awareness and health monitoring. As a result, an increasing number of AI-enabled applications are being developed targeting ubiquitous and mobile devices. While deep neural networks (DNNs) are getting bigger and more complex, they also impose a heavy computational and energy burden on the host devices, which has led to the integration of various specialized processors in commodity devices. Given the broad range of competing DNN architectures and the heterogeneity of the target hardware, there is an emerging need to understand the compatibility between DNN-platform pairs and the expected performance benefits on each platform. This work attempts to demystify this landscape by systematically evaluating a collection of state-of-the-art DNNs on a wide variety of commodity devices. In this respect, we identify potential bottlenecks in each architecture and provide important guidelines that can assist the community in the co-design of more efficient DNNs and accelerators.Comment: Accepted at MobiSys 2019: 3rd International Workshop on Embedded and Mobile Deep Learning (EMDL), 201

Kounis Syndrome Associated With Selective Anaphylaxis to Cefazolin.

info:eu-repo/semantics/publishedVersio

Indicação geográfica do pêssego: Oportunidade de negócio em tempos de crise.

bitstream/item/59924/1/IG-Ivan.pd

Fractional Euler-Lagrange differential equations via Caputo derivatives

We review some recent results of the fractional variational calculus. Necessary optimality conditions of Euler-Lagrange type for functionals with a Lagrangian containing left and right Caputo derivatives are given. Several problems are considered: with fixed or free boundary conditions, and in presence of integral constraints that also depend on Caputo derivatives.Comment: This is a preprint of a paper whose final and definite form will appear as Chapter 9 of the book Fractional Dynamics and Control, D. Baleanu et al. (eds.), Springer New York, 2012, DOI:10.1007/978-1-4614-0457-6_9, in pres