The SB-index and the HSB-index: efficient indices for spatial data warehouses

Spatial data warehouses (SDWs) allow for spatial analysis together with analytical multidimensional queries over huge volumes of data. The challenge is to retrieve data related to ad hoc spatial query windows according to spatial predicates, avoiding the high cost of joining large tables. Therefore, mechanisms to provide efficient query processing over SDWs are essential. In this paper, we propose two efficient indices for SDW: the SB-index and the HSB-index. The proposed indices share the following characteristics. They enable multidimensional queries with spatial predicate for SDW and also support predefined spatial hierarchies. Furthermore, they compute the spatial predicate and transform it into a conventional one, which can be evaluated together with other conventional predicates by accessing a star-join Bitmap index. While the SB-index has a sequential data structure, the HSB-index uses a hierarchical data structure to enable spatial objects clustering and a specialized buffer-pool to decrease the number of disk accesses. The advantages of the SB-index and the HSB-index over the DBMS resources for SDW indexing (i.e. star-join computation and materialized views) were investigated through performance tests, which issued roll-up operations extended with containment and intersection range queries. The performance results showed that improvements ranged from 68% up to 99% over both the star-join computation and the materialized view. Furthermore, the proposed indices proved to be very compact, adding only less than 1% to the storage requirements. Therefore, both the SB-index and the HSB-index are excellent choices for SDW indexing. Choosing between the SB-index and the HSB-index mainly depends on the query selectivity of spatial predicates. While low query selectivity benefits the HSB-index, the SB-index provides better performance for higher query selectivity.

Mating system parameters at hierarchical levels of fruits, individuals and populations in the Brazilian insect-pollinated tropical tree, Tabebuia roseo-alba (Bignoniaceae)

For many tree species, mating system analyses have indicated potential variations in the selfing rate and paternity correlation among fruits within individuals, among individuals within populations, among populations, and from one flowering event to another. In this study, we used eight microsatellite markers to investigate mating systems at two hierarchical levels (fruits within individuals and individuals within populations) for the insect pollinated Neotropical tree Tabebuia roseo-alba. We found that T. roseo-alba has a mixed mating system with predominantly outcrossed mating. The outcrossing rates at the population level were similar across two T. roseo-alba populations; however, the rates varied considerably among individuals within populations. The correlated paternity results at different hierarchical levels showed that there is a high probability of shared paternal parentage when comparing seeds within fruits and among fruits within plants and full-sibs occur in much higher proportion within fruits than among fruits. Significant levels of fixation index were found in both populations and biparental inbreeding is believed to be the main cause of the observed inbreeding. The number of pollen donors contributing to mating was low. Furthermore, open-pollinated seeds varied according to relatedness, including half-sibs, full-sibs, self-sibs and self- half-sibs. In both populations, the effective population size within a family (seed-tree and its offspring) was lower than expected for panmictic populations. Thus, seeds for ex situ conservation genetics, progeny tests and reforestation must be collected from a large number of seed-trees to guarantee an adequate effective population in the sample.

A novel XML document structure comparison framework based-on sub-tree commonalities and label semantics

XML similarity evaluation has become a central issue in the database and information communities, its applications ranging over document clustering, version control, data integration and ranked retrieval. Various algorithms for comparing hierarchically structured data, XML documents in particular, have been proposed in the literature. Most of them make use of techniques for finding the edit distance between tree structures, XML documents being commonly modeled as Ordered Labeled Trees. Yet, a thorough investigation of current approaches led us to identify several similarity aspects, i.e., sub-tree related structural and semantic similarities, which are not sufficiently addressed while comparing XML documents. In this paper, we provide an integrated and fine-grained comparison framework to deal with both structural and semantic similarities in XML documents (detecting the occurrences and repetitions of structurally and semantically similar sub-trees), and to allow the end-user to adjust the comparison process according to her requirements. Our framework consists of four main modules for (i) discovering the structural commonalities between sub-trees, (ii) identifying sub-tree semantic resemblances, (iii) computing tree-based edit operations costs, and (iv) computing tree edit distance. Experimental results demonstrate higher comparison accuracy with respect to alternative methods, while timing experiments reflect the impact of semantic similarity on overall system performance.

Soil profile, relief features and their relation to structure and distribution of Brazilian Atlantic rain forest trees

In tropical forests, the environmental heterogeneity can provide niche partitioning at local scales and determine the diversity and plant species distribution. Thus, this study aimed to investigate the variations of tree species structure and distribution in response to relief and soil profile features in a portion of the largest remnant of Brazilian Atlantic rain forest. All trees >= 5 cm diameter at breast height were recorded in two 0.99 ha plots. Topographic survey and a soil characterization were accomplished in both plots. Topsoil samples (0-20 cm) were taken from 88 quadrats and analyzed for chemical and particle size properties. Differences for both diversity and tree density were identified among three kinds of soils. A canonical correspondence analysis (CCA) indicated that the specific abundance varied among the three kinds of soils mapped: a shallow Udept - Orthent / Aquent gradient, probably due to differences in soil drainage. Nutrient content was less likely to affect tree species composition and distribution than relief, pH, Al3+, and soil texture. Some species were randomly distributed and did not show restriction to relief and soil properties. However, preferences in niche occupation detected in this study, derived from the catenary environments found, rise up as an important explanation for the high tree species diversity in tropical forests.

Efficient Forest Data Structure for Evolutionary Algorithms Applied to Network Design

The design of a network is a solution to several engineering and science problems. Several network design problems are known to be NP-hard, and population-based metaheuristics like evolutionary algorithms (EAs) have been largely investigated for such problems. Such optimization methods simultaneously generate a large number of potential solutions to investigate the search space in breadth and, consequently, to avoid local optima. Obtaining a potential solution usually involves the construction and maintenance of several spanning trees, or more generally, spanning forests. To efficiently explore the search space, special data structures have been developed to provide operations that manipulate a set of spanning trees (population). For a tree with n nodes, the most efficient data structures available in the literature require time O(n) to generate a new spanning tree that modifies an existing one and to store the new solution. We propose a new data structure, called node-depth-degree representation (NDDR), and we demonstrate that using this encoding, generating a new spanning forest requires average time O(root n). Experiments with an EA based on NDDR applied to large-scale instances of the degree-constrained minimum spanning tree problem have shown that the implementation adds small constants and lower order terms to the theoretical bound.

Structure and mechanism of diet specialisation: testing models of individual variation in resource use with sea otters

Studies of consumer-resource interactions suggest that individual diet specialisation is empirically widespread and theoretically important to the organisation and dynamics of populations and communities. We used weighted networks to analyze the resource use by sea otters, testing three alternative models for how individual diet specialisation may arise. As expected, individual specialisation was absent when otter density was low, but increased at high-otter density. A high-density emergence of nested resource-use networks was consistent with the model assuming individuals share preference ranks. However, a density-dependent emergence of a non-nested modular network for core resources was more consistent with the competitive refuge model. Individuals from different diet modules showed predictable variation in rank-order prey preferences and handling times of core resources, further supporting the competitive refuge model. Our findings support a hierarchical organisation of diet specialisation and suggest individual use of core and marginal resources may be driven by different selective pressures.

Bamboo overabundance alters forest structure and dynamics in the Atlantic Forest hotspot

With fast growth rates and clonal reproduction, bamboos can rapidly invade forest areas, drastically changing their original structure. In the Brazilian Atlantic Forest, where recent mapping efforts have shown that woody bamboos dominate large areas, the present study assessed the differences in soil and vegetation between plots dominated (>90% of bamboo coverage) and not dominated (<10% of coverage) by the native Guadua tagoara. Surface soil was physically and chemically analyzed, and trees at three size classes (seedling, sapling, and adult) were counted, identified and measured. New inventories were conducted to assess recruitment, mortality, and damage rates. Bamboo plots had more fertile soils (higher bases saturation and lower potential acidity) due to the preferential occurrence of G. tagoara on more clayey soils. Bamboo-dominated plots had lower density of adult trees (diameter >5 cm) and lower species density. In addition, overall tree diameter distribution was very different between environments, with bamboo plots having greater concentration of small-sized trees. Such differences are probably related to the general tendency of higher mortality, recruitment, and damage rates in bamboo plots. Greater physical (wind and bamboo-induced damages) and physiological stress (heat and light) in bamboo plots are probable causes of bamboo-dominated plots being more dynamic. Finally, we discuss the differences between Atlantic and Amazonian Guadua-dominated forests, causes, and possible consequences of bamboo overabundance to the Atlantic Forest conservation. (C) 2012 Elsevier Ltd. All rights reserved.

The genetic structure and mating system of Acrocomia aculeata (Arecaceae)

Acrocomia aculeata is a perennial, fruit-producing palm tree, native to tropical forests. Its fruits have spurred interest because of their significant potential for use in the cosmetic industry and as feedstock for biofuel. In the present study, the genetic structure and mating system in Acrocomia aculeata were analyzed, using eight nuclear microsatellite loci and samples from Sao Paulo and Minas Gerais states, Brazil. By means of Bayesian analysis, these populations were clustered into two or three groups. A high multilocus outcrossing rate suggests that outcrosses were predominant, although a certain degree of biparental inbreeding also occurred. Thus, although monoecious and self-compatible, there is every indication that A. aculeata bears a mixed reproductive system, with a predominance of outcrossing. Given the genetic structure revealed hereby, future conservation strategies and germplasm collecting should be focussed on sampling and preserving individuals from different clusters.

Parameter recovery for a skew-normal IRT model under a Bayesian approach: hierarchical framework, prior and kernel sensitivity and sample size

Item response theory (IRT) comprises a set of statistical models which are useful in many fields, especially when there is an interest in studying latent variables (or latent traits). Usually such latent traits are assumed to be random variables and a convenient distribution is assigned to them. A very common choice for such a distribution has been the standard normal. Recently, Azevedo et al. [Bayesian inference for a skew-normal IRT model under the centred parameterization, Comput. Stat. Data Anal. 55 (2011), pp. 353-365] proposed a skew-normal distribution under the centred parameterization (SNCP) as had been studied in [R. B. Arellano-Valle and A. Azzalini, The centred parametrization for the multivariate skew-normal distribution, J. Multivariate Anal. 99(7) (2008), pp. 1362-1382], to model the latent trait distribution. This approach allows one to represent any asymmetric behaviour concerning the latent trait distribution. Also, they developed a Metropolis-Hastings within the Gibbs sampling (MHWGS) algorithm based on the density of the SNCP. They showed that the algorithm recovers all parameters properly. Their results indicated that, in the presence of asymmetry, the proposed model and the estimation algorithm perform better than the usual model and estimation methods. Our main goal in this paper is to propose another type of MHWGS algorithm based on a stochastic representation (hierarchical structure) of the SNCP studied in [N. Henze, A probabilistic representation of the skew-normal distribution, Scand. J. Statist. 13 (1986), pp. 271-275]. Our algorithm has only one Metropolis-Hastings step, in opposition to the algorithm developed by Azevedo et al., which has two such steps. This not only makes the implementation easier but also reduces the number of proposal densities to be used, which can be a problem in the implementation of MHWGS algorithms, as can be seen in [R.J. Patz and B.W. Junker, A straightforward approach to Markov Chain Monte Carlo methods for item response models, J. Educ. Behav. Stat. 24(2) (1999), pp. 146-178; R. J. Patz and B. W. Junker, The applications and extensions of MCMC in IRT: Multiple item types, missing data, and rated responses, J. Educ. Behav. Stat. 24(4) (1999), pp. 342-366; A. Gelman, G.O. Roberts, and W.R. Gilks, Efficient Metropolis jumping rules, Bayesian Stat. 5 (1996), pp. 599-607]. Moreover, we consider a modified beta prior (which generalizes the one considered in [3]) and a Jeffreys prior for the asymmetry parameter. Furthermore, we study the sensitivity of such priors as well as the use of different kernel densities for this parameter. Finally, we assess the impact of the number of examinees, number of items and the asymmetry level on the parameter recovery. Results of the simulation study indicated that our approach performed equally as well as that in [3], in terms of parameter recovery, mainly using the Jeffreys prior. Also, they indicated that the asymmetry level has the highest impact on parameter recovery, even though it is relatively small. A real data analysis is considered jointly with the development of model fitting assessment tools. The results are compared with the ones obtained by Azevedo et al. The results indicate that using the hierarchical approach allows us to implement MCMC algorithms more easily, it facilitates diagnosis of the convergence and also it can be very useful to fit more complex skew IRT models.

A decaying factor accounts for contained activity in neuronal networks with no need of hierarchical or modular organization

The mechanisms responsible for containing activity in systems represented by networks are crucial in various phenomena, for example, in diseases such as epilepsy that affect the neuronal networks and for information dissemination in social networks. The first models to account for contained activity included triggering and inhibition processes, but they cannot be applied to social networks where inhibition is clearly absent. A recent model showed that contained activity can be achieved with no need of inhibition processes provided that the network is subdivided into modules (communities). In this paper, we introduce a new concept inspired in the Hebbian theory, through which containment of activity is achieved by incorporating a dynamics based on a decaying activity in a random walk mechanism preferential to the node activity. Upon selecting the decay coefficient within a proper range, we observed sustained activity in all the networks tested, namely, random, Barabasi-Albert and geographical networks. The generality of this finding was confirmed by showing that modularity is no longer needed if the dynamics based on the integrate-and-fire dynamics incorporated the decay factor. Taken together, these results provide a proof of principle that persistent, restrained network activation might occur in the absence of any particular topological structure. This may be the reason why neuronal activity does not spread out to the entire neuronal network, even when no special topological organization exists.

Oleoresin glands in copaiba (Copaifera trapezifolia Hayne: Leguminosae), a Brazilian rainforest tree

Although studies have addressed the chemical analysis and the biological activity of oleoresin in species of Copaifera, the cellular mechanisms of oleoresin production, storage, and release have rarely been investigated. This study detailed the distribution, ontogeny, and ultrastructure of secretory cavities and canals distributed in leaf and stem, respectively, of Copaifera trapezifolia, a Brazilian species included in a plant group of great economic interest. Axillary vegetative buds, leaflets, and portions of stem in primary and secondary growth were collected and processed in order to study the anatomy, histolocalization of substances, and ultrastructure. Secretory cavities are observed in the foliar blade and secretory canals in the petiolule and stem. They are made up of a uniseriate epithelium delimiting an isodiametric or elongated lumen. Biseriate epithelium is rarely observed and is a novelty for Leguminosae. Cavities and canals originate from ground meristem cells and the lumen is formed by schizogenesis. The content of the cavities and canals of both stem and leaf is oily and resinous, which suggests that the oleoresin could be extracted from the leaf instead of the stem. Phenolic compounds are also detected in the epithelial cell cytoplasm. Cavities and canals in the beginning of developmental stages have polarized epithelial cells. The cytoplasm is rich in smooth and rough endoplasmic reticula connected to vesicles or plastids. Smooth and rough endoplasmic reticulum and plastids were found to be predominant in the epithelial cells of the secretory cavities and canals of C. trapezifolia. Such features testify the quantities of oleoresin found in the lumen and phenolic compounds in the epithelial cell cytoplasm of these glands. Other studies employing techniques such as correlative light electron microscopy could show the vesicle traffic and the compartmentalization of the produced substances in such glands.

The importance of landscape structure for seed dispersal in rain forest fragments

Questions What are the main features of the seed rain in a fragmented Atlantic forest landscape? Can seed rain species attributes (life form, dispersal mode, successional status) relate to the spatial arrangement (size and number of fragments, edge density and presence of corridor) of forest fragments in the landscape? How does the rain forest landscape structure affect the seed rain? Location Atlantic rainforest, Sao Paulo State, Southeastern Brazil. Methods Seed rain samples were collected monthly throughout 1yr, counted, identified and classified according to species dispersal mode, successional status and life form. Seed rain composition was compared with woody species near the seed traps. Relationships between seed rain composition and landscape spatial arrangement (fragment area, presence of corridor, number of fragments in the surroundings, proximity of fragments, and edge density) were tested using canonical correspondence analysis (CCA). Results We collected 20142 seeds belonging to 115 taxa, most of them early successional and anemochorous trees. In general, the seed rain had a species composition distinct from that of the nearby forest tree community. Small isolated fragments contained more seeds, mainly of anemochorous, epiphytic and early-successional species; large fragments showed higher association with zoochorous and late-successional species compared to small fragments. The CCA significantly distinguished the species dispersal mode according to fragment size and isolation, anemochorous species being associated to small and isolated fragments, and zoochorous species to larger areas and fragment aggregation. Nevertheless, a gradient driven by proximity (PROX) and edge density (ED) segregated lianas (in the positive extremity), early successional and epiphyte species (in the negative end); large fragments were positively associated to PROX and ED. Conclusions The results highlight the importance of the size and spatial arrangement of forest patches to promote habitat connectivity and improve the flux of animal-dispersed seeds. Landscape structure controls seed fluxes and affects plant dispersal capacity, potentially influencing the composition and structure of forest fragments. The seed rain composition may be used to assess the effects of landscape spatial structure on plant assemblages, and provide relevant information for biodiversity conservation.

Diversity and genetic structure in natural populations of Hancornia speciosa var. speciosa Gomes in northeastern Brazil

Hancornia speciosa Gomes is a fruit tree native from Brazil that belongs to Apocinaceae family, and is popularly known as Mangabeira. Its fruits are widely consumed raw or processed as fruit jam, juices and ice creams, which have made it a target of intense exploitation. The extractive activities and intense human activity on the environment of natural occurrence of H. speciosa has caused genetic erosion in the species and little is known about the ecology or genetic structure of natural populations. The objective of this research was the evaluation of the genetic diversity and genetic structure of H. speciosa var. speciosa. The genetic variability was assessed using 11 allozyme loci with a sample of 164 individuals distributed in six natural populations located in the States of Pernambuco and Alagoas, Northeastern Brazil. The results showed a high level of genetic diversity within the species (e= 0.36) seeing that the most of the genetic variability of H. speciosa var. speciosa is within its natural populations with low difference among populations ( or = 0.081). The inbreeding values within ( = -0.555) and among populations ( =-0.428) were low showing lacking of endogamy and a surplus of heterozygotes. The estimated gene flow ( m ) was high, ranging from 2.20 to 13.18, indicating to be enough to prevent the effects of genetic drift and genetic differentiation among populations. The multivariate analyses indicated that there is a relationship between genetic and geographical distances, which was confirmed by a spatial pattern analysis using Mantel test (r = 0.3598; p = 0.0920) with 1000 random permutations. The high genetic diversity index in these populations indicates potential for in situ genetic conservation.

Hierarchical multi-label classification using local neural networks

Hierarchical multi-label classification is a complex classification task where the classes involved in the problem are hierarchically structured and each example may simultaneously belong to more than one class in each hierarchical level. In this paper, we extend our previous works, where we investigated a new local-based classification method that incrementally trains a multi-layer perceptron for each level of the classification hierarchy. Predictions made by a neural network in a given level are used as inputs to the neural network responsible for the prediction in the next level. We compare the proposed method with one state-of-the-art decision-tree induction method and two decision-tree induction methods, using several hierarchical multi-label classification datasets. We perform a thorough experimental analysis, showing that our method obtains competitive results to a robust global method regarding both precision and recall evaluation measures.