The Relationship between Physical Growth and Infant Behavioral Development in Rural Guatemala

The present study investigated the relationship between a number of anthropometric indices and behavioral development during the first 2 years of life in rural Guatemala. Length and weight were the indices most strongly correlated with behavioral development. If the effect of the infant\u27s length and weight was statistically controlled for, none of the other anthropometric variables explained a significant proportion of the variance in behavioral development. Con- trolling for length (or weight) assessed at the same age as the behavioral assessment, length (or weight) for younger ages was not significantly correlated with behavioral development. Changes in length or weight over time were correlated with changes in behavioral performance. We were unable to explain the association between physical growth and behavioral development by a number of variables including gestational age, nutrient intake, prevalence of disease, and familial characteristics

Aggregation Rules in Committee Procedures.

Very often, decision procedures in a committee compensate potential manipulations by taking into account the ordered profile of qualifications. It is therefore rejected the standard assumption of an underlying associative binary connective allowing the evaluation of arbitrary finite sequences of items by means of a one-by-one sequential process. In this paper we develop a mathematical approach for non-associative connectives allowing a sequential definition by means of binary fuzzy connectives. It will be then stressed that a connective rule should be understood as a consistent sequence of binary connective operators. Committees should previously decide about which connective rule they will be condidering, not just about a single operator

Aggregation Operators for Fuzzy Rationality Measures.

Fuzzy rationality measures represent a particular class of aggregation operators. Following the axiomatic approach developed in [1,3,4,5] rationality of fuzzy preferences may be seen as a fuzzy property of fuzzy preferences. Moreover, several rationality measures can be aggregated into a global rationality measure. We will see when and how this can be done. We will also comment upon the feasibility of their use in real life applications. Indeed, some of the rationality measures proposed, though intuitively (and axiomatically) sound, appear to be quite complex from a computational point of view

Applying suitability distributions in a geological context

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Statistical relational learning with soft quantifiers

Quantification in statistical relational learning (SRL) is either existential or universal, however humans might be more inclined to express knowledge using soft quantifiers, such as ``most'' and ``a few''. In this paper, we define the syntax and semantics of PSL^Q, a new SRL framework that supports reasoning with soft quantifiers, and present its most probable explanation (MPE) inference algorithm. To the best of our knowledge, PSL^Q is the first SRL framework that combines soft quantifiers with first-order logic rules for modelling uncertain relational data. Our experimental results for link prediction in social trust networks demonstrate that the use of soft quantifiers not only allows for a natural and intuitive formulation of domain knowledge, but also improves the accuracy of inferred results

Evidence Propagation and Consensus Formation in Noisy Environments

We study the effectiveness of consensus formation in multi-agent systems where there is both belief updating based on direct evidence and also belief combination between agents. In particular, we consider the scenario in which a population of agents collaborate on the best-of-n problem where the aim is to reach a consensus about which is the best (alternatively, true) state from amongst a set of states, each with a different quality value (or level of evidence). Agents' beliefs are represented within Dempster-Shafer theory by mass functions and we investigate the macro-level properties of four well-known belief combination operators for this multi-agent consensus formation problem: Dempster's rule, Yager's rule, Dubois & Prade's operator and the averaging operator. The convergence properties of the operators are considered and simulation experiments are conducted for different evidence rates and noise levels. Results show that a combination of updating on direct evidence and belief combination between agents results in better consensus to the best state than does evidence updating alone. We also find that in this framework the operators are robust to noise. Broadly, Yager's rule is shown to be the better operator under various parameter values, i.e. convergence to the best state, robustness to noise, and scalability.Comment: 13th international conference on Scalable Uncertainty Managemen

Characterizations of quasitrivial symmetric nondecreasing associative operations

We provide a description of the class of n-ary operations on an arbitrary chain that are quasitrivial, symmetric, nondecreasing, and associative. We also prove that associativity can be replaced with bisymmetry in the definition of this class. Finally we investigate the special situation where the chain is finite

GDM-VieweR: A new tool in R to visualize the evolution of fuzzy consensus processes

With the incorporation of web 2.0 frameworks the complexity of decision making situations has exponentially increased, involving in many cases many experts, and a huge number of different alternatives. In the literature we can find a great deal of methodologies to assist multi-person decision making. However these classical approaches are not prepared to deal with such a huge complexity and there is a lack of tools that support the decision processes providing some graphical information. Therefore the main objective of this contribution is to present an open source tool developed in R to provide a quick insight of the evolution of the decision making by means of meaningful graphical representations. Thanks to the modular architecture of this solution this tool can be easily adapted to work with various Group decision making methodologies

Fuzzy integral for rule aggregation in fuzzy inference systems

The fuzzy inference system (FIS) has been tuned and re-vamped many times over and applied to numerous domains. New and improved techniques have been presented for fuzzification, implication, rule composition and defuzzification, leaving one key component relatively underrepresented, rule aggregation. Current FIS aggregation operators are relatively simple and have remained more-or-less unchanged over the years. For many problems, these simple aggregation operators produce intuitive, useful and meaningful results. However, there exists a wide class of problems for which quality aggregation requires non- additivity and exploitation of interactions between rules. Herein, we show how the fuzzy integral, a parametric non-linear aggregation operator, can be used to fill this gap. Specifically, recent advancements in extensions of the fuzzy integral to \unrestricted" fuzzy sets, i.e., subnormal and non- convex, makes this now possible. We explore the role of two extensions, the gFI and the NDFI, discuss when and where to apply these aggregations, and present efficient algorithms to approximate their solutions