Non-stationary Dynamics in the Bouncing Ball: A Wavelet perspective

The non-stationary dynamics of a bouncing ball, comprising of both periodic as well as chaotic behavior, is studied through wavelet transform. The multi-scale characterization of the time series displays clear signature of self-similarity, complex scaling behavior and periodicity. Self-similar behavior is quantified by the generalized Hurst exponent, obtained through both wavelet based multi-fractal detrended fluctuation analysis and Fourier methods. The scale dependent variable window size of the wavelets aptly captures both the transients and non-stationary periodic behavior, including the phase synchronization of different modes. The optimal time-frequency localization of the continuous Morlet wavelet is found to delineate the scales corresponding to neutral turbulence, viscous dissipation regions and different time varying periodic modulations.Comment: 17 pages, 10 figures, 1 tabl

Multi-scale characterization of monument limestones.

Among the parameters influencing stone deterioration, moisture and water movements through the pore network are essential. This communication presents differents methods to characterize stones and to determinate the water transfer properties. Results are analysed for two limestones having similar total porosity, but characterized by different pore networks. These different porous systems govern dissimilar water properties

Multi-scale approach for analyzing convective heat transfer flow in background-oriented Schlieren technique

The paper introduces a multi-scale processing method for quantitative study and visualization of convective heat transfer using diffractive optical element based background-oriented schlieren technique. The method relies on robust estimation of phase encoded in the fringe pattern using windowed Fourier transform and subsequent multi-scale characterization of the obtained phase using continuous wavelet transform. As the phase is directly mapped to the refractive index fluctuations caused by the temperature gradients, the multi-scale inspection provides interesting insights about the underlying heat flow phenomenon. The performance of the proposed method is demonstrated for quantitative flow visualization

Quantitative Nanofriction Characterization of Corrugated Surfaces by Atomic Force Microscopy

Atomic Force Microscopy (AFM) is a suitable tool to perform tribological characterization of materials down to the nanometer scale. An important aspect in nanofriction measurements of corrugated samples is the local tilt of the surface, which affects the lateral force maps acquired with the AFM. This is one of the most important problems of state-of-the-art nanotribology, making difficult a reliable and quantitative characterization of real corrugated surfaces. A correction of topographic spurious contributions to lateral force maps is thus needed for corrugated samples. In this paper we present a general approach to the topographic correction of AFM lateral force maps and we apply it in the case of multi-asperity adhesive contact. We describe a complete protocol for the quantitative characterization of the frictional properties of corrugated systems in the presence of surface adhesion using the AFM.Comment: 33 pages, 9 figures, RevTex 4, submitted to Journal of Applied Physic

A Holistic Approach to Log Data Analysis in High-Performance Computing Systems: The Case of IBM Blue Gene/Q

The complexity and cost of managing high-performance computing infrastructures are on the rise. Automating management and repair through predictive models to minimize human interventions is an attempt to increase system availability and contain these costs. Building predictive models that are accurate enough to be useful in automatic management cannot be based on restricted log data from subsystems but requires a holistic approach to data analysis from disparate sources. Here we provide a detailed multi-scale characterization study based on four datasets reporting power consumption, temperature, workload, and hardware/software events for an IBM Blue Gene/Q installation. We show that the system runs a rich parallel workload, with low correlation among its components in terms of temperature and power, but higher correlation in terms of events. As expected, power and temperature correlate strongly, while events display negative correlations with load and power. Power and workload show moderate correlations, and only at the scale of components. The aim of the study is a systematic, integrated characterization of the computing infrastructure and discovery of correlation sources and levels to serve as basis for future predictive modeling efforts.Comment: 12 pages, 7 Figure

The Mass-Size Relation from Clouds to Cores. I. A new Probe of Structure in Molecular Clouds

We use a new contour-based map analysis technique to measure the mass and size of molecular cloud fragments continuously over a wide range of spatial scales (0.05 < r / pc < 10), i.e., from the scale of dense cores to those of entire clouds. The present paper presents the method via a detailed exploration of the Perseus Molecular Cloud. Dust extinction and emission data are combined to yield reliable scale-dependent measurements of mass. This scale-independent analysis approach is useful for several reasons. First, it provides a more comprehensive characterization of a map (i.e., not biased towards a particular spatial scale). Such a lack of bias is extremely useful for the joint analysis of many data sets taken with different spatial resolution. This includes comparisons between different cloud complexes. Second, the multi-scale mass-size data constitutes a unique resource to derive slopes of mass-size laws (via power-law fits). Such slopes provide singular constraints on large-scale density gradients in clouds.Comment: accepted to ApJ; references updated in new versio

Inverse Reinforcement Learning in Swarm Systems

Inverse reinforcement learning (IRL) has become a useful tool for learning behavioral models from demonstration data. However, IRL remains mostly unexplored for multi-agent systems. In this paper, we show how the principle of IRL can be extended to homogeneous large-scale problems, inspired by the collective swarming behavior of natural systems. In particular, we make the following contributions to the field: 1) We introduce the swarMDP framework, a sub-class of decentralized partially observable Markov decision processes endowed with a swarm characterization. 2) Exploiting the inherent homogeneity of this framework, we reduce the resulting multi-agent IRL problem to a single-agent one by proving that the agent-specific value functions in this model coincide. 3) To solve the corresponding control problem, we propose a novel heterogeneous learning scheme that is particularly tailored to the swarm setting. Results on two example systems demonstrate that our framework is able to produce meaningful local reward models from which we can replicate the observed global system dynamics.Comment: 9 pages, 8 figures; ### Version 2 ### version accepted at AAMAS 201