211 research outputs found
Localized Faraday patterns under heterogeneous parametric excitation
Faraday waves are a classic example of a system in which an extended pattern
emerges under spatially uniform forcing. Motivated by systems in which uniform
excitation is not plausible, we study both experimentally and theoretically the
effect of heterogeneous forcing on Faraday waves. Our experiments show that
vibrations restricted to finite regions lead to the formation of localized
subharmonic wave patterns and change the onset of the instability. The
prototype model used for the theoretical calculations is the parametrically
driven and damped nonlinear Schr\"odinger equation, which is known to describe
well Faraday-instability regimes. For an energy injection with a Gaussian
spatial profile, we show that the evolution of the envelope of the wave pattern
can be reduced to a Weber-equation eigenvalue problem. Our theoretical results
provide very good predictions of our experimental observations provided that
the decay length scale of the Gaussian profile is much larger than the pattern
wavelength.Comment: 10 pages, 9 figures, Accepte
Modeling Lexicon Emergence as Concept Emergence in Networks
Amodel for lexicon emergence in social networks is presented. The model
is based on a modified version of classic Naming Games, where agents’ knowledge
is represented by means of formal contexts. That way it is possible to represent the
effect interactions have on individual knowledge as well as the dynamics of global
knowledge in the network.Ministerio de Economía y Competitividad TIN2013-41086-PJunta de Andalucía TIC-606
Qualitative Reasoning on Complex Systems from Observations
A hybrid approach to phenomenological reconstruction of Complex
Systems (CS), using Formal Concept Analysis (FCA) as main tool for conceptual
data mining, is proposed. To illustrate the method, a classic CS is selected
(cellular automata), to show how FCA can assist to predict CS evolution under
different conceptual descriptions (from different observable features of the CS).Junta de Andalucía TIC-606
Complex concept lattices for simulating human prediction in sport
In order to address the study of complex systems, the detection of patterns in their dynamics
could play a key role in understanding their evolution. In particular, global patterns are required
to detect emergent concepts and trends, some of them of a qualitative nature. Formal concept analysis
(FCA) is a theory whose goal is to discover and extract knowledge from qualitative data (organized
in concept lattices). In complex environments, such as sport competitions, the large amount of information
currently available turns concept lattices into complex networks. The authors analyze how to
apply FCA reasoning in order to increase confidence in sports predictions by means of detecting regularities
from data through the management of intuitive and natural attributes extracted from publicly
available information. The complexity of concept lattices -considered as networks with complex topological
structure- is analyzed. It is applied to building a knowledge based system for confidence-based
reasoning, which simulates how humans tend to avoid the complexity of concept networks by means of
bounded reasoning skills.Ministerio de Ciencia e Innovación TIN2009-09492Junta de Andalucía TIC-606
Simulating Language Dynamics by Means of Concept Reasoning
A problem in the phenomenological reconstruction of Complex
Systems (CS) is the extraction of the knowledge that elements playing
in CS use during its evolution. This problem is important because
such a knowledge would allow the researcher to understand the global
behavior of the system [1,2]. In this paper an approach to partially solve
this problem by means of Formal Concept Analysis (FCA) is described in
a particular case, namely Language Dynamics. The main idea lies in the
fact that global knowledge in CS is naturally built by local interactions
among agents, and FCA could be useful to represent their own knowledge.
In this way it is possible to represent the effect of interactions
on individual knowledge as well as the dynamics of global knowledge.
Experiments in order to show this approach are given using WordNet.Ministerio de Ciencia e Innovación TIN2009-09492Junta de Andalucía TIC-606
Confidence-Based Reasoning with Local Temporal Formal Contexts
Formal Concept Analysis (FCA) is a theory whose goal is to discover and to extract Knowledge from qualitative data. It provides tools for reasoning with implication basis (and association rules). In this paper we analyse how to apply FCA reasoning to increase confidence in sports betting, by means of detecting temporal regularities from data. It is applied to build a Knowledge based system for confidence reasoning.Ministerio de Ciencia e Innovación TIN2009-09492Junta de Andalucía TIC-606
Selecting Attributes for Sport Forecasting using Formal Concept Analysis
In order to address complex systems, apply pattern recongnition on their
evolution could play an key role to understand their dynamics. Global patterns
are required to detect emergent concepts and trends, some of them with
qualitative nature. Formal Concept Analysis (FCA) is a theory whose goal is to
discover and to extract Knowledge from qualitative data. It provides tools for
reasoning with implication basis (and association rules). Implications and
association rules are usefull to reasoning on previously selected attributes,
providing a formal foundation for logical reasoning. In this paper we analyse
how to apply FCA reasoning to increase confidence in sports betting, by means
of detecting temporal regularities from data. It is applied to build a
Knowledge-Based system for confidence reasoning.Comment: Paper 3 for the Complex Systems in Sports Workshop 2011 (CS-Sports
2011
Bounded Rationality for Data Reasoning based on Formal Concept Analysis
Formal Concept Analysis (FCA) is a theory whose goal is to discover and extract Knowledge from qualitative data. It also provides tools for sound reasoning (implication basis and association rules). The aim of this paper is to apply FCA to a new model for bounded rationality based on the implicational reasoning over contextual knowledge bases which are obtained from contextual selections. A contextual selection is a selection of events and attributes about them which induces partial contexts from a global formal context. In order to avoid inconsistencies, association rules are selected as reasoning engine. The model is applied to forecast sport results.Ministerio de Ciencia e Innovación TIN2009-09492Junta de Andalucía TIC-606
Scale-Free Structure in Concept Lattices Associated to Complex Systems
Qualitative representation and reasoning on Complex
Systems (CS) is important for a number of human activities
on CS, mainly for the understanding of both, our
perception about their structure as well as their dynamics.
Formal Concept Analysis can help understanding the conceptual
structure behind these qualitative representations by means of
the called concept lattices (CL). In this paper the scale free
conceptualization hypothesis, (SFCH) is asserted. SFCH claims
that a scale-free distribution in node’s connectivity appears on
the CL associated to complex systems (CLCS) only when two
requirements holds: CLCS is useful both to represent qualitative
and reliable attributes on the CS, and to provide a basis for
(qualitative) successfully reasoning about the CS. Experiments
revealed that the topologies of CLCS are similar when the amount
of information on the CS is sufficient, while it is different in other
concept lattices associated to random formal contexts or to other
systems in which some of the above requirements do not hold
On Experimental Efficiency for Retraction Operator to Stem Basis
In this paper, we introduce an implementation of an inference rule called
“Independence Rule” which lets us reduce the size of knowledge basis based on the
retraction problem. This implementation is made in a functional language, Scala, and
specialized on attribute implications. We evaluate its efficiency related to the Stem
Base generation
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