Computational inference of neural information flow networks.

Determining how information flows along anatomical brain pathways is a fundamental requirement for understanding how animals perceive their environments, learn, and behave. Attempts to reveal such neural information flow have been made using linear computational methods, but neural interactions are known to be nonlinear. Here, we demonstrate that a dynamic Bayesian network (DBN) inference algorithm we originally developed to infer nonlinear transcriptional regulatory networks from gene expression data collected with microarrays is also successful at inferring nonlinear neural information flow networks from electrophysiology data collected with microelectrode arrays. The inferred networks we recover from the songbird auditory pathway are correctly restricted to a subset of known anatomical paths, are consistent with timing of the system, and reveal both the importance of reciprocal feedback in auditory processing and greater information flow to higher-order auditory areas when birds hear natural as opposed to synthetic sounds. A linear method applied to the same data incorrectly produces networks with information flow to non-neural tissue and over paths known not to exist. To our knowledge, this study represents the first biologically validated demonstration of an algorithm to successfully infer neural information flow networks.

Recognition of Planar Segments in Point Cloud Based on Wavelet Transform

© 2005-2012 IEEE.Within industrial automation systems, three-dimensional (3-D) vision provides very useful feedback information in autonomous operation of various manufacturing equipment (e.g., industrial robots, material handling devices, assembly systems, and machine tools). The hardware performance in contemporary 3-D scanning devices is suitable for online utilization. However, the bottleneck is the lack of real-time algorithms for recognition of geometric primitives (e.g., planes and natural quadrics) from a scanned point cloud. One of the most important and the most frequent geometric primitive in various engineering tasks is plane. In this paper, we propose a new fast one-pass algorithm for recognition (segmentation and fitting) of planar segments from a point cloud. To effectively segment planar regions, we exploit the orthonormality of certain wavelets to polynomial function, as well as their sensitivity to abrupt changes. After segmentation of planar regions, we estimate the parameters of corresponding planes using standard fitting procedures. For point cloud structuring, a z-buffer algorithm with mesh triangles representation in barycentric coordinates is employed. The proposed recognition method is tested and experimentally validated in several real-world case studies.

Micro-mechanical parameterisations for continuum modelling of granular material using the discrete element method

The present work uses the discrete element method (DEM) to describe assemblies of particulate bulk materials. Working numerical descriptions of entire processes using this scheme are infeasible because of the very large number of elements (1012 or more in a moderately sized industrial silo). However it is possible to capture much of the essential bulk mechanics through selective DEM on important regions of an assembly, thereafter using the information in continuum numerical descriptions of particulate processes. The continuum numerical model uses population balances of the various components in bulk solid mixtures. It depends on constitutive relationships for the internal transfer, creation and/or destruction of components within the mixture. In this paper we show the means of generating such relationships for two important flow phenomena – segregation whereby particles differing in some important property (often size) separate into discrete phases, and degradation, whereby particles break into sub-elements, through impact on each other or shearing. We perform DEM simulations under a range of representative conditions, extracting the important parameters for the relevant transfer, creation and/or destruction of particles in certain classes within the assembly over time. Continuum predictions of segregation and degradation using this scheme are currently being successfully validated against bulk experimental data and are beginning to be used in schemes to improve the design and operation of bulk solids process plant.

Investigation into a modular rule-based testing method for testing business rules in scheduling applications

Rule testing in transport scheduling is a complex and potentially costly business problem. This paper proposes an automated method for the rule-based testing of business rules using the extensible Markup Language for rule representation and transportation. A compiled approach to rule execution is also proposed for performance-critical scheduling systems.

A method to assess the sensitivity of sedimentary communities to fishing activities.

1.Methods of sensitivity assessment to identify species and habitats in need of management or protection have been available since the 1970s. 2.The approach to sensitivity assessment adopted by the Marine Life Information Network (MarLIN) assumes that the sensitivity of a community or biotope is dependent on the species within it. However, the application of this approach to sedimentary communities, especially offshore, is complex because of a lack of knowledge of the structural or functional role of many sedimentary species. 3.This paper describes a method to assess the overall sensitivity of sedimentary communities, based on the intolerance and recoverability of component species to physical disturbance. A range of methods were applied to identify the best combinations of abundant, dominant or high biomass species for the assessment of sensitivity in the sedimentary communities examined. 4.Results showed that reporting the most frequent species' sensitivity assessment, irrespective of the four methods used to select species, consistently underestimated the total sensitivity of the community. In contrast, reporting the most sensitive assessment from those species selected resulted in a range of biotope sensitivities from very low to very high, that was better able to discriminate between the sensitivities of the communities examined. 5.The assumptions behind the methodology, its limitations and potential application are discussed.

Microabrasion : A novel method to assess surface degradation of UHMWPE following sterilisation and ageing

The ageing behaviour of ultra-high molecular weight polyethylene (UHMWPE) has been studied following gamma irradiation (25 or 40 kGy) in air. Accelerated ageing procedures used elevated temperature (70°C) and/or pressurised oxygen (5 bar). Shelf-aged UHMWPE was also studied. The variation in surface density and mechanical properties were determined following the various sterilisation and ageing treatments. Microabrasive wear testing was also performed. Wear rates were found to correlate well with stress at break for sterilised and aged UHMWPE but not with elongation to failure. It is proposed that the wear mechanism is fracture dominated and occurs following some disentanglement of the polymer chains. Wear also depends upon embrittlement of the surface layer due to its processing and ageing. Elongation to failure in a tensile test is not a good measure of this embrittlement whereas the microabrasion test provides more surface sensitive information concerning this property.

A method for carbon stable isotope analysis of methyl halides and chlorofluorocarbons at pptv concentrations

A pre-concentration system has been validated for use with a gas chromatography/mass spectrometry/isotope ratio mass spectrometer (GC/MS/IRMS) to determine ambient air 13C/12C ratios for methyl halides (MeCl and MeBr) and chlorofluorocarbons (CFCs). The isotopic composition of specific compounds can provide useful information on their atmospheric budgets and biogeochemistry that cannot be ascertained from abundance measurements alone. Although pre-concentration systems have been previously used with a GC/MS/IRMS for atmospheric trace gas analysis, this is the first study also to report system validation tests. Validation results indicate that the pre-concentration system and subsequent separation technologies do not significantly alter the stable isotopic ratios of the target methyl halides, CFC-12 (CCl2F2) and CFC-113 (C2Cl3F3). Significant, but consistent, isotopic shifts of -27.5 to -25.6 do occur within the system for CFC-11 (CCl3F), although the shift is correctible. The method presented has the capacity to separate these target halocarbons from more than 50 other compounds in ambient air samples. Separation allows for the determination of stable carbon isotope ratios of five of these six target trace atmospheric constituents within ambient air for large volume samples (10 L). Representative urban air analyses from Belfast City are also presented which give carbon isotope results similar to published values for 13C/12C analysis of MeCl (-39.1) and CFC-113 (-28.1). However, this is the first paper reporting stable carbon isotope signatures for CFC-11 (-29.4) and CFC-12 (-37.0).

Quantum-information processing with noisy cluster states

We provide an analysis of basic quantum-information processing protocols under the effect of intrinsic nonidealities in cluster states. These nonidealities are based on the introduction of randomness in the entangling steps that create the cluster state and are motivated by the unavoidable imperfections faced in creating entanglement using condensed-matter systems. Aided by the use of an alternative and very efficient method to construct cluster-state configurations, which relies on the concatenation of fundamental cluster structures, we address quantum-state transfer and various fundamental gate simulations through noisy cluster states. We find that a winning strategy to limit the effects of noise is the management of small clusters processed via just a few measurements. Our study also reinforces recent ideas related to the optical implementation of a one-way quantum computer.

A decentralized model of information pricing in networks

We propose a recursive method of pricing an information good in a network of holders and demanders of this good. The prices are determined via a unique equilibrium outcome in a sequence of bilateral bargaining games that are played by connected agents. If the information is an homogenous, non-depreciating good without network effects we derive explicit formulae which elucidate the role of the link pattern among the players. Particularly, we find out that the equilibrium price is intimately related to the existence of cycles in the network: It is zero if a cycle covers the trading pair and it is proportional to the direct and indirect utility that the good generates otherwise.

A method to bridge the gap between breadth and depth in IS narrative analysis

A method extending narrative analysis with grounded theory analysis is proposed to bridge the gap between breadth and depth in IS narrative research. The purpose of the method is not to develop a theory but to make narrative analysis more accessible, transparent and accountable; and the resultant narrative more contextually grounded. The method is aimed particularly at inexperienced narrative researchers who currently lack guidance through the complexity of narrative analysis, but may also benefit experienced narrative researchers who may not be familiar with the applicability of grounded theory tools and techniques in this area.

A method for determining generators’ shares in loads, line flows and losses

This paper presents a new method for calculating the individual generators’ shares in line flows, line losses and loads. The method is described and illustrated on active power flows, but it can be applied in the same way to reactive power flows. Starting from a power flow solution, the line flow matrix is formed. This matrix is used for identifying node types, tracing the power flow from generators downstream to loads, and to determine generators’ participation factors to lines and loads. Neither exhaustive search nor matrix inversion is required. Hence, the method is claimed to be the least computationally demanding amongst all of the similar methods.

MISEP method for postnonlinear blind source separation

In this letter, a standard postnonlinear blind source separation algorithm is proposed, based on the MISEP method, which is widely used in linear and nonlinear independent component analysis. To best suit a wide class of postnonlinear mixtures, we adapt the MISEP method to incorporate a priori information of the mixtures. In particular, a group of three-layered perceptrons and a linear network are used as the unmixing system to separate sources in the postnonlinear mixtures, and another group of three-layered perceptron is used as the auxiliary network. The learning algorithm for the unmixing system is then obtained by maximizing the output entropy of the auxiliary network. The proposed method is applied to postnonlinear blind source separation of both simulation signals and real speech signals, and the experimental results demonstrate its effectiveness and efficiency in comparison with existing methods.

Performing meta-analysis with incomplete statistical information in clinical trials

Background: Results from clinical trials are usually summarized in the form of sampling distributions. When full information (mean, SEM) about these distributions is given, performing meta-analysis is straightforward. However, when some of the sampling distributions only have mean values, a challenging issue is to decide how to use such distributions in meta-analysis. Currently, the most common approaches are either ignoring such trials or for each trial with a missing SEM, finding a similar trial and taking its SEM value as the missing SEM. Both approaches have drawbacks. As an alternative, this paper develops and tests two new methods, the first being the prognostic method and the second being the interval method, to estimate any missing SEMs from a set of sampling distributions with full information. A merging method is also proposed to handle clinical trials with partial information to simulate meta-analysis.

The outflow ranking method for weighted directed graphs

A ranking method assigns to every weighted directed graph a (weak) ordering of the nodes. In this paper we axiomatize the ranking method that ranks the nodes according to their outflow using four independent axioms. Besides the well-known axioms of anonymity and positive responsiveness we introduce outflow monotonicity – meaning that in pairwise comparison between two nodes, a node is not doing worse in case its own outflow does not decrease and the other node’s outflow does not increase – and order preservation – meaning that adding two weighted digraphs such that the pairwise ranking between two nodes is the same in both weighted digraphs, then this is also their pairwise ranking in the ‘sum’ weighted digraph. The outflow ranking method generalizes the ranking by outdegree for directed graphs, and therefore also generalizes the ranking by Copeland score for tournaments.

Evaluation of the Q-method as a method of public participation in the selection of sustainable development indicators

The complexity of sustainable development means that it is often difficult to evaluate and communicate the concept effectively. One standard method to reduce complexity and improve Communication, while maintaining scientific objectivity, is to use selected indicators. The aim of this paper is to describe and evaluate a process Of public participation in the selection of sustainable development indicators that utilised the Q-method for discourse analysis. The Q-method was Utilised to combine public opinion with technical expertise to create a list of technically robust indicators that would be relevant to the public, The method comprises statement collection, statement analysis, Q-sorts and Q-sort analysis. The results of the Q-method generated a list of statements for which a preliminary list of indicators was then developed by a team of experts from the fields of environmental science, sustainable development and Psychology. Subsequently members of the public evaluated the preliminary list of indicators, to select a final list of indicators that were both technically sound and incorporated the views of the public. The Utilisation of the Q-method in this process was evaluated using previously published criteria. The application of the Q-method in this context needs to be considered not only by the quality of the indicators developed, but also from the perspective of the benefit of the process to the participants. it was concluded that the Q-method provided an effective framework for public participation in the selection of indicators as it allowed the public to discuss Sustainable development in familiar language and in the context of their daily lives. By combining this information with expert input, a list of technically robust indicators that resonate with the public was developed. The results demonstrated that many citizens are not aware Of Sustainable development, and if it is to be successfully communicated to them, then indicators and policy need to be couched in terms familiar and relevant to citizen and communities. (C) 2009 Elsevier Ltd. All rights reserved.