Arbuscular mycorrhizal fungi and collembola non-additively increase soil aggregation

Soil aggregation is a principal ecosystem process mediated by soil biota. Collembola and arbuscular mycorrhizal (AM) fungi are important groups in the soil, and can interact in various ways. Few studies have examined collembola effects on soil aggregation, while many have quantified AM effects. Here, we asked if collembola have any effect on soil aggregation, and if they alter AM fungi-mediated effects on soil aggregation.

We carried out a factorial greenhouse study, manipulating the presence of both collembola and AM fungi, using two different plant species, Sorghum vulgare and Daucus carota. We measured root length and biomass, AMF (and non-AMF) soil hyphal length, root colonization, and collembolan populations, and quantified water stable soil aggregates (WSA) in four size classes.

Soil exposed to growth of AMF hyphae and collembola individually had higher WSA than control treatments. Moreover, the interaction effects between AMF and collembola were significant, with nonadditive increases in the combined application compared to the single treatments.

Our findings show that collembola can play a crucial role in maintaining ecological sustainability through promoting soil aggregation, and point to the importance of considering organism interactions in understanding formation of soil structure. (C) 2011 Elsevier Ltd. All rights reserved.

Micro-arthropod communities under human disturbance: is taxonomic aggregation a valuable tool for detecting multivariate change? Evidence from Mediterranean soil oribatid coenoses

Animal communities are sensitive to environmental disturbance, and several multivariate methods have recently been developed to detect changes in community structure. The complex taxonomy of soil invertebrates constrains the use of the community level in monitoring environmental changes, since species identification requires expertise and time. However, recent literature data on marine communities indicate that little multivariate information is lost in the taxonomic aggregation of species data to high rank taxa. In the present paper, this hypothesis was tested on two oribatid mite (oribatida, Acari) assemblages under two different kinds of disturbance: metal pollution and fires. Results indicate that data sets built at the genus and family systematic rank can detect the effects of disturbance with little loss of information. This is an encouraging result in view of the use of the community level as a preliminary tool for describing patterns of human-disturbed soil ecosystems. (c) 2006 Elsevier SAS. All rights reserved.

Analysis of Bayesian classification-based approaches for Android malware detection

Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform. Additionally, Android malware is evolving rapidly to evade detection by traditional signature-based scanning. Despite current detection measures in place, timely discovery of new malware is still a critical issue. This calls for novel approaches to mitigate the growing threat of zero-day Android malware. Hence, the authors develop and analyse proactive machine-learning approaches based on Bayesian classification aimed at uncovering unknown Android malware via static analysis. The study, which is based on a large malware sample set of majority of the existing families, demonstrates detection capabilities with high accuracy. Empirical results and comparative analysis are presented offering useful insight towards development of effective static-analytic Bayesian classification-based solutions for detecting unknown Android malware.

Using geostatistical Bayesian Updating to integrate airborne radiometrics and soil geochemistry to improve mapping for mineral exploration

Mineral exploration programmes around the world use data from remote sensing, geophysics and direct sampling. On a regional scale, the combination of airborne geophysics and ground-based geochemical sampling can aid geological mapping and economic minerals exploration. The fact that airborne geophysical and traditional soil-sampling data are generated at different spatial resolutions means that they are not immediately comparable due to their different sampling density. Several geostatistical techniques, including indicator cokriging and collocated cokriging, can be used to integrate different types of data into a geostatistical model. With increasing numbers of variables the inference of the cross-covariance model required for cokriging can be demanding in terms of effort and computational time. In this paper a Gaussian-based Bayesian updating approach is applied to integrate airborne radiometric data and ground-sampled geochemical soil data to maximise information generated from the soil survey, to enable more accurate geological interpretation for the exploration and development of natural resources. The Bayesian updating technique decomposes the collocated estimate into a production of two models: prior and likelihood models. The prior model is built from primary information and the likelihood model is built from secondary information. The prior model is then updated with the likelihood model to build the final model. The approach allows multiple secondary variables to be simultaneously integrated into the mapping of the primary variable. The Bayesian updating approach is demonstrated using a case study from Northern Ireland where the history of mineral prospecting for precious and base metals dates from the 18th century. Vein-hosted, strata-bound and volcanogenic occurrences of mineralisation are found. The geostatistical technique was used to improve the resolution of soil geochemistry, collected one sample per 2 km2, by integrating more closely measured airborne geophysical data from the GSNI Tellus Survey, measured over a footprint of 65 x 200 m. The directly measured geochemistry data were considered as primary data in the Bayesian approach and the airborne radiometric data were used as secondary data. The approach produced more detailed updated maps and in particular maximized information on mapped estimates of zinc, copper and lead. Greater delineation of an elongated northwest/southeast trending zone in the updated maps strengthened the potential to investigate stratabound base metal deposits.

Opportunistic spectrum aggregation for cognitive communications under collision constraints

We consider a collision-sensitive secondary system that intends to opportunistically aggregate and utilize spectrum of a primary system to achieve higher data rates. In such opportunistic spectrum access, secondary transmission can collide with primary transmission. When the secondary system aggregates more channels for data transmission, more frequent collisions may occur, limiting the performance obtained by the opportunistic spectrum aggregation. In this context, dynamic spectrum aggregation problem is formulated to maximize the ergodic channel capacity under the constraint of collision tolerable level. To solve the problem, we develop the optimal spectrum aggregation approach, deriving closed-form expressions for the collision probability in terms of primary user traffic load, secondary user transmission interval, and the random number of sub-channels aggregated. Our results show that aggregating only a subset of sub-channels will be a better choice, depending on the ratio of collision sensitivity requirement to the primary user traffic.

A Bayesian method for detecting stellar flares

We present a Bayesian-odds-ratio-based algorithm for detecting stellar flares in light-curve data. We assume flares are described by a model in which there is a rapid rise with a half-Gaussian profile, followed by an exponential decay. Our signal model also contains a polynomial background model required to fit underlying light-curve variations in the data, which could otherwise partially mimic a flare. We characterize the false alarm probability and efficiency of this method under the assumption that any unmodelled noise in the data is Gaussian, and compare it with a simpler thresholding method based on that used in Walkowicz et al. We find our method has a significant increase in detection efficiency for low signal-to-noise ratio (S/N) flares. For a conservative false alarm probability our method can detect 95 per cent of flares with S/N less than 20, as compared to S/N of 25 for the simpler method. We also test how well the assumption of Gaussian noise holds by applying the method to a selection of 'quiet' Kepler stars. As an example we have applied our method to a selection of stars in Kepler Quarter 1 data. The method finds 687 flaring stars with a total of 1873 flares after vetos have been applied. For these flares we have made preliminary characterizations of their durations and and S/N.

Ranking Spatial and Temporal Hotspots for Road Safety Interventions Using a Nonlinear Aggregation Approach

In recent years, the concept of a composite performance index, brought from economic and business statistics, has gained popularity in the field of road safety. The construction of the Composite Safety Performance Index (CSPI) involves the following key steps: the selection of the most appropriate indicators to be aggregated and the method used to aggregate them.

Over the last decade, various aggregation methods for estimating the CSPI have been suggested in the literature. However, recent studies indicates that most of these methods suffer from many deficiencies at both the theoretical and operational level; these include the correlation and compensability between indicators, as well as their high “degree of freedom” which enables one to readily manipulate them to produce desired outcomes.

The purpose of this study is to introduce an alternative aggregation method for the estimation of the CSPI, which is free from the aforementioned deficiencies. In contrast with the current aggregation methods, which generally use linear combinations of road safety indicators to estimate a CSPI, the approach advocated in this study is based on non-linear combinations of indicators and can be summarized into the following two main steps: the pairwise comparison of road safety indicators and the development of marginal and composite road safety performance functions. The introduced method has been successfully applied to identify and rank temporal and spatial hotspots for Northern Ireland, using road traffic collision data recorded in the UK STATs19 database. The obtained results highlight the promising features of the proposed approach including its stability and consistency, which enables significantly reduced deficiencies associated with the current aggregation methods. Progressively, the introduced method could evolve into an intelligent support system for road safety assessment.

Functional role of microarthropods in soil aggregation

Soil aggregation has received a lot of attention in the last years; however, the focus was mostly on soil microorganismsor larger soil fauna, especially earthworms. The impact of the large group of microarthropods, e.g. Collembola and Acari, is nearly unknown and hence underrepresented in the literature. Here we propose and discuss potential direct and indirect mechanisms of how microarthropods could influence this process with the focus on collembolans, which are in general a relatively well studied taxon.Indirect mechanisms are likely to have larger impacts on soil aggregation than direct effects. The variety of indirect mechanisms based on the provision of organic material like faecal pellets, molts and necromass as food source for microorganisms is high and given available evidence we propose that these mechanismsare the most influential. We highlight the need for overcoming the challenges of culturing and handling of these animals in order to be able to design small scale experiments and field studies which would enable us to understand the role of the different
functional groups, their interaction with other soil faunaand the impact of land use practices on soil aggregation.

Min-BDeu and Max-BDeu Scores for Learning Bayesian Networks

This work presents two new score functions based on the Bayesian Dirichlet equivalent uniform (BDeu) score for learning Bayesian network structures. They consider the sensitivity of BDeu to varying parameters of the Dirichlet prior. The scores take on the most adversary and the most beneficial priors among those within a contamination set around the symmetric one. We build these scores in such way that they are decomposable and can be computed efficiently. Because of that, they can be integrated into any state-of-the-art structure learning method that explores the space of directed acyclic graphs and allows decomposable scores. Empirical results suggest that our scores outperform the standard BDeu score in terms of the likelihood of unseen data and in terms of edge discovery with respect to the true network, at least when the training sample size is small. We discuss the relation between these new scores and the accuracy of inferred models. Moreover, our new criteria can be used to identify the amount of data after which learning is saturated, that is, additional data are of little help to improve the resulting model.

Advances in Learning Bayesian Networks of Bounded Treewidth

This work presents novel algorithms for learning Bayesian networks of bounded treewidth. Both exact and approximate methods are developed. The exact method combines mixed integer linear programming formulations for structure learning and treewidth computation. The approximate method consists in sampling k-trees (maximal graphs of treewidth k), and subsequently selecting, exactly or approximately, the best structure whose moral graph is a subgraph of that k-tree. The approaches are empirically compared to each other and to state-of-the-art methods on a collection of public data sets with up to 100 variables.

Efficient Structure Learning of Bayesian Networks using Constraints

This paper addresses the problem of learning Bayesian network structures from data based on score functions that are decomposable. It describes properties that strongly reduce the time and memory costs of many known methods without losing global optimality guarantees. These properties are derived for different score criteria such as Minimum Description Length (or Bayesian Information Criterion), Akaike Information Criterion and Bayesian Dirichlet Criterion. Then a branch-and-bound algorithm is presented that integrates structural constraints with data in a way to guarantee global optimality. As an example, structural constraints are used to map the problem of structure learning in Dynamic Bayesian networks into a corresponding augmented Bayesian network. Finally, we show empirically the benefits of using the properties with state-of-the-art methods and with the new algorithm, which is able to handle larger data sets than before.