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