Analyzing the Complexity and Reliability of Switch-Frequency-Reconfigurable Antennas Using Graph Models

This paper addresses the functional reliability and the complexity of reconfigurable antennas using graph models. The correlation between complexity and reliability for any given reconfigurable antenna is defined. Two methods are proposed to reduce failures and improve the reliability of reconfigurable antennas. The failures are caused by the reconfiguration technique or by the surrounding environment. These failure reduction methods proposed are tested and examples are given which verify these methods.

Complex networks analysis of language complexity

Methods from statistical physics, such as those involving complex networks, have been increasingly used in the quantitative analysis of linguistic phenomena. In this paper, we represented pieces of text with different levels of simplification in co-occurrence networks and found that topological regularity correlated negatively with textual complexity. Furthermore, in less complex texts the distance between concepts, represented as nodes, tended to decrease. The complex networks metrics were treated with multivariate pattern recognition techniques, which allowed us to distinguish between original texts and their simplified versions. For each original text, two simplified versions were generated manually with increasing number of simplification operations. As expected, distinction was easier for the strongly simplified versions, where the most relevant metrics were node strength, shortest paths and diversity. Also, the discrimination of complex texts was improved with higher hierarchical network metrics, thus pointing to the usefulness of considering wider contexts around the concepts. Though the accuracy rate in the distinction was not as high as in methods using deep linguistic knowledge, the complex network approach is still useful for a rapid screening of texts whenever assessing complexity is essential to guarantee accessibility to readers with limited reading ability. Copyright (c) EPLA, 2012

The complexity of motion in the Aristotelian Physics

The intention of this paper is to present some Aristotelian arguments regarding the motion on local terrestrial region. Because it is a highly sophisticated and complex explanation dealt with, briefly, the principles and causes that based theoretic sciences in general and in particular physics. Subdivided into eight topics this article in order to facilitate the understanding of these concepts for the reader not familiar with the Aristotelian texts. With intent to avoid an innocent view, anachronistic and linear the citations are of primary sources or commentators of Aristotle's works.

Automatic aspect discrimination in data clustering

The attributes describing a data set may often be arranged in meaningful subsets, each of which corresponds to a different aspect of the data. An unsupervised algorithm (SCAD) that simultaneously performs fuzzy clustering and aspects weighting was proposed in the literature. However, SCAD may fail and halt given certain conditions. To fix this problem, its steps are modified and then reordered to reduce the number of parameters required to be set by the user. In this paper we prove that each step of the resulting algorithm, named ASCAD, globally minimizes its cost-function with respect to the argument being optimized. The asymptotic analysis of ASCAD leads to a time complexity which is the same as that of fuzzy c-means. A hard version of the algorithm and a novel validity criterion that considers aspect weights in order to estimate the number of clusters are also described. The proposed method is assessed over several artificial and real data sets.

Evaluation of physiologic complexity in time series using generalized sample entropy and surrogate data analysis

Complexity in time series is an intriguing feature of living dynamical systems, with potential use for identification of system state. Although various methods have been proposed for measuring physiologic complexity, uncorrelated time series are often assigned high values of complexity, errouneously classifying them as a complex physiological signals. Here, we propose and discuss a method for complex system analysis based on generalized statistical formalism and surrogate time series. Sample entropy (SampEn) was rewritten inspired in Tsallis generalized entropy, as function of q parameter (qSampEn). qSDiff curves were calculated, which consist of differences between original and surrogate series qSampEn. We evaluated qSDiff for 125 real heart rate variability (HRV) dynamics, divided into groups of 70 healthy, 44 congestive heart failure (CHF), and 11 atrial fibrillation (AF) subjects, and for simulated series of stochastic and chaotic process. The evaluations showed that, for nonperiodic signals, qSDiff curves have a maximum point (qSDiff(max)) for q not equal 1. Values of q where the maximum point occurs and where qSDiff is zero were also evaluated. Only qSDiff(max) values were capable of distinguish HRV groups (p-values 5.10 x 10(-3); 1.11 x 10(-7), and 5.50 x 10(-7) for healthy vs. CHF, healthy vs. AF, and CHF vs. AF, respectively), consistently with the concept of physiologic complexity, and suggests a potential use for chaotic system analysis. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4758815]

COMPLEXITY MEASURE: A QUANTUM INFORMATION APPROACH

In the past decades, all of the efforts at quantifying systems complexity with a general tool has usually relied on using Shannon's classical information framework to address the disorder of the system through the Boltzmann-Gibbs-Shannon entropy, or one of its extensions. However, in recent years, there were some attempts to tackle the quantification of algorithmic complexities in quantum systems based on the Kolmogorov algorithmic complexity, obtaining some discrepant results against the classical approach. Therefore, an approach to the complexity measure is proposed here, using the quantum information formalism, taking advantage of the generality of the classical-based complexities, and being capable of expressing these systems' complexity on other framework than its algorithmic counterparts. To do so, the Shiner-Davison-Landsberg (SDL) complexity framework is considered jointly with linear entropy for the density operators representing the analyzed systems formalism along with the tangle for the entanglement measure. The proposed measure is then applied in a family of maximally entangled mixed state.

Brazilian exchange rate complexity: Financial crisis effects

With the financial market globalization, foreign investments became vital for the economies, mainly in emerging countries. In the last decades, Brazilian exchange rates appeared as a good indicator to measure either investors' confidence or risk aversion. Here, some events of global or national financial crisis are analyzed, trying to understand how they influenced the "dollar-real" rate evolution. The theoretical tool to be used is the Lopez-Mancini-Calbet (LMC) complexity measure that, applied to real exchange rate data, has shown good fitness between critical events and measured patterns. (C) 2011 Elsevier B.V. All rights reserved.

Modelling shared attention through relational reinforcement learning

Shared attention is a type of communication very important among human beings. It is sometimes reserved for the more complex form of communication being constituted by a sequence of four steps: mutual gaze, gaze following, imperative pointing and declarative pointing. Some approaches have been proposed in Human-Robot Interaction area to solve part of shared attention process, that is, the most of works proposed try to solve the first two steps. Models based on temporal difference, neural networks, probabilistic and reinforcement learning are methods used in several works. In this article, we are presenting a robotic architecture that provides a robot or agent, the capacity of learning mutual gaze, gaze following and declarative pointing using a robotic head interacting with a caregiver. Three learning methods have been incorporated to this architecture and a comparison of their performance has been done to find the most adequate to be used in real experiment. The learning capabilities of this architecture have been analyzed by observing the robot interacting with the human in a controlled environment. The experimental results show that the robotic head is able to produce appropriate behavior and to learn from sociable interaction.

CLEANING ASSOCIATIONS BETWEEN BIRDS AND HERBIVOROUS MAMMALS IN BRAZIL: STRUCTURE AND COMPLEXITY

Birds that remove ectoparasites and other food material from their hosts are iconic illustrations of mutualistic-commensalistic cleaning associations. To assess the complex pattern of food resource use embedded in cleaning interactions of an assemblage of birds and their herbivorous mammal hosts in open habitats in Brazil, we used a network approach that characterized their patterns of association. Cleaning interactions showed a distinctly nested pattern, related to the number of interactions of cleaners and hosts and to the range of food types that each host species provided. Hosts that provided a wide range of food types (flies, ticks, tissue and blood, and organic debris) were attended by more species of cleaners and formed the core of the web. On the other hand, core cleaner species did not exploit the full range of available food resources, but used a variety of host species to exploit these resources instead. The structure that we found indicates that cleaners rely on cleaning interactions to obtain food types that would not be available otherwise (e.g., blood-engorged ticks or horseflies, wounded tissue). Additionally, a nested organization for the cleaner bird mammalian herbivore association means that both generalist and selective species take part in the interactions and that partners of selective species form an ordered subset of the partners of generalist species. The availability of predictable protein-rich food sources for birds provided by cleaning interactions may lead to an evolutionary pathway favoring their increased use by birds that forage opportunistically. Received 30 June 2011, accepted 10 November 2011.

Aging reduces complexity of heart rate variability assessed by conditional entropy and symbolic analysis

Increasing age is associated with a reduction in overall heart rate variability as well as changes in complexity of physiologic dynamics. The aim of this study was to verify if the alterations in autonomic modulation of heart rate caused by the aging process could be detected by Shannon entropy (SE), conditional entropy (CE) and symbolic analysis (SA). Complexity analysis was carried out in 44 healthy subjects divided into two groups: old (n = 23, 63 +/- A 3 years) and young group (n = 21, 23 +/- A 2). It was analyzed SE, CE [complexity index (CI) and normalized CI (NCI)] and SA (0V, 1V, 2LV and 2ULV patterns) during short heart period series (200 cardiac beats) derived from ECG recordings during 15 min of rest in a supine position. The sequences characterized by three heart periods with no significant variations (0V), and that with two significant unlike variations (2ULV) reflect changes in sympathetic and vagal modulation, respectively. The unpaired t test (or Mann-Whitney rank sum test when appropriate) was used in the statistical analysis. In the aging process, the distributions of patterns (SE) remain similar to young subjects. However, the regularity is significantly different; the patterns are more repetitive in the old group (a decrease of CI and NCI). The amounts of pattern types are different: 0V is increased and 2LV and 2ULV are reduced in the old group. These differences indicate marked change of autonomic regulation. The CE and SA are feasible techniques to detect alteration in autonomic control of heart rate in the old group.

Entropy, complexity and disequilibrium in compact stars

We used the statistical measurements of information entropy, disequilibrium and complexity to infer a hierarchy of equations of state for two types of compact stars from the broad class of neutron stars, namely, with hadronic composition and with strange quark composition. Our results show that, since order costs energy. Nature would favor the exotic strange stars even though the question of how to form the strange stars cannot be answered within this approach. (C) 2012 Elsevier B.V. All rights reserved.

Mini-Mental State Examination sentence writing among community-dwelling elderly adults in Brazil: text fluency and grammar complexity

Background: In normal aging, the decrease in the syntactic complexity of written production is usually associated with cognitive deficits. This study was aimed to analyze the quality of older adults' textual production indicated by verbal fluency (number of words) and grammatical complexity (number of ideas) in relation to gender, age, schooling, and cognitive status. Methods: From a probabilistic sample of community-dwelling people aged 65 years and above (n = 900), 577 were selected on basis of their responses to the Mini-Mental State Examination (MMSE) sentence writing, which were submitted to content analysis; 323 were excluded as they left the item blank or performed illegible or not meaningful responses. Education adjusted cut-off scores for the MMSE were used to classify the participants as cognitively impaired or unimpaired. Total and subdomain MMSE scores were computed. Results: 40.56% of participants whose answers to the MMSE sentence were excluded from the analyses had cognitive impairment compared to 13.86% among those whose answers were included. The excluded participants were older and less educated. Women and those older than 80 years had the lowest scores in the MMSE. There was no statistically significant relationship between gender, age, schooling, and textual performance. There was a modest but significant correlation between number of words written and the scores in the Language subdomain. Conclusions: Results suggest the strong influence of schooling and age over MMSE sentence performance. Failing to write a sentence may suggest cognitive impairment, yet, instructions for the MMSE sentence, i.e. to produce a simple sentence, may limit its clinical interpretation.

IDM - A New Parallel Methodology to Calculate the Determinant of Matrices of the Order n, with Computational Complexity O(n)

This paper presents a new parallel methodology for calculating the determinant of matrices of the order n, with computational complexity O(n), using the Gauss-Jordan Elimination Method and Chio's Rule as references. We intend to present our step-by-step methodology using clear mathematical language, where we will demonstrate how to calculate the determinant of a matrix of the order n in an analytical format. We will also present a computational model with one sequential algorithm and one parallel algorithm using a pseudo-code.

MR-Radix: a multi-relational data mining algorithm

Abstract Background Once multi-relational approach has emerged as an alternative for analyzing structured data such as relational databases, since they allow applying data mining in multiple tables directly, thus avoiding expensive joining operations and semantic losses, this work proposes an algorithm with multi-relational approach. Methods Aiming to compare traditional approach performance and multi-relational for mining association rules, this paper discusses an empirical study between PatriciaMine - an traditional algorithm - and its corresponding multi-relational proposed, MR-Radix. Results This work showed advantages of the multi-relational approach in performance over several tables, which avoids the high cost for joining operations from multiple tables and semantic losses. The performance provided by the algorithm MR-Radix shows faster than PatriciaMine, despite handling complex multi-relational patterns. The utilized memory indicates a more conservative growth curve for MR-Radix than PatriciaMine, which shows the increase in demand of frequent items in MR-Radix does not result in a significant growth of utilized memory like in PatriciaMine. Conclusion The comparative study between PatriciaMine and MR-Radix confirmed efficacy of the multi-relational approach in data mining process both in terms of execution time and in relation to memory usage. Besides that, the multi-relational proposed algorithm, unlike other algorithms of this approach, is efficient for use in large relational databases.

The complexity of narrative interferes in the use of conjunctions in children with specific language impairment

PURPOSE: To verify the use of conjunctions in narratives, and to investigate the influence of stimuli's complexity over the type of conjunctions used by children with specific language impairment (SLI) and children with typical language development. METHODS: Participants were 40 children (20 with typical language development and 20 with SLI) with ages between 7 and 10 years, paired by age range. Fifteen stories with increasing of complexity were used to obtain the narratives; stories were classified into mechanical, behavioral and intentional, and each of them was represented by four scenes. Narratives were analyzed according to occurrence and classification of conjunctions. RESULTS: Both groups used more coordinative than subordinate conjunctions, with significant decrease in the use of conjunctions in the discourse of SLI children. The use of conjunctions varied according to the type of narrative: for coordinative conjunctions, both groups differed only between intentional and behavioral narratives, with higher occurrence in behavioral ones; for subordinate conjunctions, typically developing children's performance did not show differences between narratives, while SLI children presented fewer occurrences in intentional narratives, which was different from other narratives. CONCLUSION: Both groups used more coordinative than subordinate conjunctions; however, typically developing children presented more conjunctions than SLI children. The production of children with SLI was influenced by stimulus, since more complex narratives has less use of subordinate conjunctions.