1,557 research outputs found
Multifractal analysis of memory usage patterns
The discovery of fractal phenomenon in computer-related areas such as network traffic flow leads to the hypothesis that many computer resources display fractal characteristics. The goal of this study is to apply fractal analysis to computer memory usage patterns. We devise methods for calculating the Holder exponent of a time series and calculating the fractal dimension of a plot of a time series. These methods are then applied to memory-related data collected from a Unix server. We find that our methods for calculating the Holder exponent of a time series yield results that are independently confirmed through calculation of the fractal dimension of the time series, and that computer memory use does indeed display multifractal behavior. In addition, it is hypothesized that this multifractal behavior may be useful in making certain predictions about the future behavior of an operating system
Network extraction by routing optimization
Routing optimization is a relevant problem in many contexts. Solving directly
this type of optimization problem is often computationally unfeasible. Recent
studies suggest that one can instead turn this problem into one of solving a
dynamical system of equations, which can instead be solved efficiently using
numerical methods. This results in enabling the acquisition of optimal network
topologies from a variety of routing problems. However, the actual extraction
of the solution in terms of a final network topology relies on numerical
details which can prevent an accurate investigation of their topological
properties. In this context, theoretical results are fully accessible only to
an expert audience and ready-to-use implementations for non-experts are rarely
available or insufficiently documented. In particular, in this framework, final
graph acquisition is a challenging problem in-and-of-itself. Here we introduce
a method to extract networks topologies from dynamical equations related to
routing optimization under various parameters' settings. Our method is made of
three steps: first, it extracts an optimal trajectory by solving a dynamical
system, then it pre-extracts a network and finally, it filters out potential
redundancies. Remarkably, we propose a principled model to address the
filtering in the last step, and give a quantitative interpretation in terms of
a transport-related cost function. This principled filtering can be applied to
more general problems such as network extraction from images, thus going beyond
the scenarios envisioned in the first step. Overall, this novel algorithm
allows practitioners to easily extract optimal network topologies by combining
basic tools from numerical methods, optimization and network theory. Thus, we
provide an alternative to manual graph extraction which allows a grounded
extraction from a large variety of optimal topologies.Comment: 17 pages, 7 main Figures, 3 SI figure
Data Science and Knowledge Discovery
Data Science (DS) is gaining significant importance in the decision process due to a mix of various areas, including Computer Science, Machine Learning, Math and Statistics, domain/business knowledge, software development, and traditional research. In the business field, DS's application allows using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data to support the decision process. After collecting the data, it is crucial to discover the knowledge. In this step, Knowledge Discovery (KD) tasks are used to create knowledge from structured and unstructured sources (e.g., text, data, and images). The output needs to be in a readable and interpretable format. It must represent knowledge in a manner that facilitates inferencing. KD is applied in several areas, such as education, health, accounting, energy, and public administration. This book includes fourteen excellent articles which discuss this trending topic and present innovative solutions to show the importance of Data Science and Knowledge Discovery to researchers, managers, industry, society, and other communities. The chapters address several topics like Data mining, Deep Learning, Data Visualization and Analytics, Semantic data, Geospatial and Spatio-Temporal Data, Data Augmentation and Text Mining
Hydrodynamics
The phenomena related to the flow of fluids are generally complex, and difficult to quantify. New approaches - considering points of view still not explored - may introduce useful tools in the study of Hydrodynamics and the related transport phenomena. The details of the flows and the properties of the fluids must be considered on a very small scale perspective. Consequently, new concepts and tools are generated to better describe the fluids and their properties. This volume presents conclusions about advanced topics of calculated and observed flows. It contains eighteen chapters, organized in five sections: 1) Mathematical Models in Fluid Mechanics, 2) Biological Applications and Biohydrodynamics, 3) Detailed Experimental Analyses of Fluids and Flows, 4) Radiation-, Electro-, Magnetohydrodynamics, and Magnetorheology, 5) Special Topics on Simulations and Experimental Data. These chapters present new points of view about methods and tools used in Hydrodynamics
Pedestrian, Crowd, and Evacuation Dynamics
This contribution describes efforts to model the behavior of individual
pedestrians and their interactions in crowds, which generate certain kinds of
self-organized patterns of motion. Moreover, this article focusses on the
dynamics of crowds in panic or evacuation situations, methods to optimize
building designs for egress, and factors potentially causing the breakdown of
orderly motion.Comment: This is a review paper. For related work see http://www.soms.ethz.c
Fractal Analysis and Chaos in Geosciences
The fractal analysis is becoming a very useful tool to process obtained data from chaotic systems in geosciences. It can be used to resolve many ambiguities in this domain. This book contains eight chapters showing the recent applications of the fractal/mutifractal analysis in geosciences. Two chapters are devoted to applications of the fractal analysis in climatology, two of them to data of cosmic and solar geomagnetic data from observatories. Four chapters of the book contain some applications of the (multi-) fractal analysis in exploration geophysics. I believe that the current book is an important source for researchers and students from universities
ALERT Doctoral School 2012: advanced experimental techniques in geomechanics
The twenty-second session of the European Graduate School 2012 (called usually ALERT Doctoral School) entitled Advanced experimental techniques in geomechanics is organized by Cino Viggiani, Steve Hall and Enrique Romero.Postprint (published version
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