28,176 research outputs found
Detection of hidden structures on all scales in amorphous materials and complex physical systems: basic notions and applications to networks, lattice systems, and glasses
Recent decades have seen the discovery of numerous complex materials. At the
root of the complexity underlying many of these materials lies a large number
of possible contending atomic- and larger-scale configurations and the
intricate correlations between their constituents. For a detailed
understanding, there is a need for tools that enable the detection of pertinent
structures on all spatial and temporal scales. Towards this end, we suggest a
new method by invoking ideas from network analysis and information theory. Our
method efficiently identifies basic unit cells and topological defects in
systems with low disorder and may analyze general amorphous structures to
identify candidate natural structures where a clear definition of order is
lacking. This general unbiased detection of physical structure does not require
a guess as to which of the system properties should be deemed as important and
may constitute a natural point of departure for further analysis. The method
applies to both static and dynamic systems.Comment: (23 pages, 9 figures
Ono: an open platform for social robotics
In recent times, the focal point of research in robotics has shifted from industrial ro- bots toward robots that interact with humans in an intuitive and safe manner. This evolution has resulted in the subfield of social robotics, which pertains to robots that function in a human environment and that can communicate with humans in an int- uitive way, e.g. with facial expressions. Social robots have the potential to impact many different aspects of our lives, but one particularly promising application is the use of robots in therapy, such as the treatment of children with autism. Unfortunately, many of the existing social robots are neither suited for practical use in therapy nor for large scale studies, mainly because they are expensive, one-of-a-kind robots that are hard to modify to suit a specific need. We created Ono, a social robotics platform, to tackle these issues. Ono is composed entirely from off-the-shelf components and cheap materials, and can be built at a local FabLab at the fraction of the cost of other robots. Ono is also entirely open source and the modular design further encourages modification and reuse of parts of the platform
AMADEUS: Towards the AutoMAteD secUrity teSting
The proper configuration of systems has become a fundamental
factor to avoid cybersecurity risks. Thereby, the analysis of cyber security vulnerabilities is a mandatory task, but the number of vul nerabilities and system configurations that can be threatened is ex tremely high. In this paper, we propose a method that uses software
product line techniques to analyse the vulnerable configuration of
the systems. We propose a solution, entitled AMADEUS, to enable
and support the automatic analysis and testing of cybersecurity
vulnerabilities of configuration systems based on feature models.
AMADEUS is a holistic solution that is able to automate the analy sis of the specific infrastructures in the organisations, the existing
vulnerabilities, and the possible configurations extracted from the
vulnerability repositories. By using this information, AMADEUS
generates automatically the feature models, that are used for rea soning capabilities to extract knowledge, such as to determine
attack vectors with certain features. AMADEUS has been validated
by demonstrating the capacities of feature models to support the
threat scenario, in which a wide variety of vulnerabilities extracted
from a real repository are involved. Furthermore, we open the door
to new applications where software product line engineering and
cybersecurity can be empowered.Ministerio de Ciencia, Innovación y Universidades RTI2018-094283-B-C33 (ECLIPSE)Junta de Andalucía P20-01224 (COPERNICA)Junta de Andalucía US-1381375 (METAMORFOSIS
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Automated generation of computationally hard feature models using evolutionary algorithms
This is the post-print version of the final paper published in Expert Systems with Applications. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2014 Elsevier B.V.A feature model is a compact representation of the products of a software product line. The automated extraction of information from feature models is a thriving topic involving numerous analysis operations, techniques and tools. Performance evaluations in this domain mainly rely on the use of random feature models. However, these only provide a rough idea of the behaviour of the tools with average problems and are not sufficient to reveal their real strengths and weaknesses. In this article, we propose to model the problem of finding computationally hard feature models as an optimization problem and we solve it using a novel evolutionary algorithm for optimized feature models (ETHOM). Given a tool and an analysis operation, ETHOM generates input models of a predefined size maximizing aspects such as the execution time or the memory consumption of the tool when performing the operation over the model. This allows users and developers to know the performance of tools in pessimistic cases providing a better idea of their real power and revealing performance bugs. Experiments using ETHOM on a number of analyses and tools have successfully identified models producing much longer executions times and higher memory consumption than those obtained with random models of identical or even larger size.European Commission (FEDER), the Spanish Government and
the Andalusian Government
Generalized Geometric Cluster Algorithm for Fluid Simulation
We present a detailed description of the generalized geometric cluster
algorithm for the efficient simulation of continuum fluids. The connection with
well-known cluster algorithms for lattice spin models is discussed, and an
explicit full cluster decomposition is derived for a particle configuration in
a fluid. We investigate a number of basic properties of the geometric cluster
algorithm, including the dependence of the cluster-size distribution on density
and temperature. Practical aspects of its implementation and possible
extensions are discussed. The capabilities and efficiency of our approach are
illustrated by means of two example studies.Comment: Accepted for publication in Phys. Rev. E. Follow-up to
cond-mat/041274
Large eddy simulations and direct numerical simulations of high speed turbulent reacting flows
This research is involved with the implementations of advanced computational schemes based on large eddy simulations (LES) and direct numerical simulations (DNS) to study the phenomenon of mixing and its coupling with chemical reactions in compressible turbulent flows. In the efforts related to LES, a research program was initiated to extend the present capabilities of this method for the treatment of chemically reacting flows, whereas in the DNS efforts, focus was on detailed investigations of the effects of compressibility, heat release, and nonequilibrium kinetics modeling in high speed reacting flows. The efforts to date were primarily focussed on simulations of simple flows, namely, homogeneous compressible flows and temporally developing hign speed mixing layers. A summary of the accomplishments is provided
Monitoring a reverse osmosis process with kernel principal component analysis: A preliminary approach
The water purification process is becoming increasingly important to ensure the continuity and quality of subsequent production processes, and it is particularly relevant in pharmaceutical contexts. However, in this context, the difficulties arising during the monitoring process are manifold. On the one hand, the monitoring process reveals various discontinuities due to different characteristics of the input water. On the other hand, the monitoring process is discontinuous and random itself, thus not guaranteeing continuity of the parameters and hindering a straightforward analysis. Consequently, further research on water purification processes is paramount to identify the most suitable techniques able to guarantee good performance. Against this background, this paper proposes an application of kernel principal component analysis for fault detection in a process with the above-mentioned characteristics. Based on the temporal variability of the process, the paper suggests the use of past and future matrices as input for fault detection as an alternative to the original dataset. In this manner, the temporal correlation between process parameters and machine health is accounted for. The proposed approach confirms the possibility of obtaining very good monitoring results in the analyzed context
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