Information Fusion and Hierarchical Knowledge Discovery by ARTMAP Neural Networks

Mapping novel terrain from sparse, complex data often requires the resolution of conflicting information from sensors working at different times, locations, and scales, and from experts with different goals and situations. Information fusion methods help resolve inconsistencies in order to distinguish correct from incorrect answers, as when evidence variously suggests that an object's class is car, truck, or airplane. The methods developed here consider a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an objects class is car, vehicle, or man-made. Underlying relationships among objects are assumed to be unknown to the automated system of the human user. The ARTMAP information fusion system uses distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierarchial knowledge structures. The system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. The procedure is illustrated with two image examples.

Self-Organizing Information Fusion and Hierarchical Knowledge Discovery: A New Framework Using Artmap Neural Networks

Classifying novel terrain or objects from sparse, complex data may require the resolution of conflicting information from sensors woring at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when eveidence variously suggests that and object's class is car, truck, or airplane. The methods described her address a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an object's class is car, vehicle, and man-made. Underlying relationships among classes are assumed to be unknown to the autonomated system or the human user. The ARTMAP information fusion system uses distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierachical knowlege structures. The fusion system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. The procedure is illustrated with two image examples, but is not limited to image domain.

Hospital clinicians information behaviour and attitudes towards the 'Clinical Informationist': an Irish survey

Background: Hospital clinicians are increasingly expected to practice evidence-based medicine (EBM) in order to minimize medical errors and ensure quality patient care, but experience obstacles to information-seeking. The introduction of a Clinical Informationist (CI) is explored as a possible solution. Aims:  This paper investigates the self-perceived information needs, behaviour and skill levels of clinicians in two Irish public hospitals. It also explores clinicians perceptions and attitudes to the introduction of a CI into their clinical teams. Methods: A questionnaire survey approach was utilised for this study, with 22 clinicians in two hospitals. Data analysis was conducted using descriptive statistics. Results: Analysis showed that clinicians experience diverse information needs for patient care, and that barriers such as time constraints and insufficient access to resources hinder their information-seeking. Findings also showed that clinicians struggle to fit information-seeking into their working day, regularly seeking to answer patient-related queries outside of working hours. Attitudes towards the concept of a CI were predominantly positive. Conclusion: This paper highlights the factors that characterise and limit hospital clinicians information-seeking, and suggests the CI as a potentially useful addition to the clinical team, to help them to resolve their information needs for patient care.

First principles simulation of amorphous silicon bulk, interfaces, and nanowires for photovoltaics

Amorphous silicon has become the material of choice for many technologies, with major applications in large area electronics: displays, image sensing and thin film photovoltaic cells. This technology development has occurred because amorphous silicon is a thin film semiconductor that can be deposited on large, low cost substrates using low temperature. In this thesis, classical molecular dynamics and first principles DFT calculations have been performed to generate structural models of amorphous and hydrogenated amorphous silicon and interfaces of amorphous and crystalline silicon, with the ultimate aim of understanding the photovoltaic properties of core-shell crystalline amorphous Si nanowire structures. We have shown, unexpectedly, from the simulations, that our understanding of hydrogenated bulk a-Si needs to be revisited, with our robust finding that when fully saturated with hydrogen, bulk a-Si exhibits a constant optical energy gap, irrespective of the hydrogen concentration in the sample. Unsaturated a-Si:H, with a lower than optimum hydrogen content, shows a smaller optical gap, that increases with hydrogen content until saturation is reached. The mobility gaps obtained from an analysis of the electronic states show similar behavior. We also obtained that the optical and mobility gaps show a volcano curve as the H content is varied from 7% (undersaturation) to 18% (mild oversaturation). In the case of mild over saturation, the mid-gap states arise exclusively from an increase in the density of strained Si-Si bonds. Analysis of our structures shows the extra H atoms in this case form a bridge between neighboring silicon atoms which increases the corresponding Si-Si distance and promotes bond length disorder in the sample. That has the potential to enhance the Staebler-Wronski effect. Planar interface models of amorphous-crystalline silicon have been generated in Si (100), (110) and (111) surfaces. The interface models are characterized by structure, RDF, electronic density of states and optical absorption spectrum. We find that the least stable (100) surface will result in the formation of the thickest amorphous silicon layer, while the most stable (110) surface forms the smallest amorphous region. We calculated for the first time band offsets of a-Si:H/c-Si heterojunctions from first principles and examined the influence of different surface orientations and amorphous layer thickness on the offsets and implications for device performance. The band offsets depend on the amorphous layer thickness and increase with thickness. By controlling the amorphous layer thickness we can potentially optimise the solar cell parameters. Finally, we have successfully generated different amorphous layer thickness of the a-Si/c-Si and a-Si:H/c-Si 5 nm nanowires from heat and quench. We perform structural analysis of the a-Si-/c-Si nanowires. The RDF, Si-Si bond length distributions, and the coordination number distributions of amorphous regions of the nanowires reproduce similar behaviour compared to bulk amorphous silicon. In the final part of this thesis we examine different surface terminating chemical groups, -H, - OH and –NH2 in (001) GeNW. Our work shows that the diameter of Ge nanowires and the nature of surface terminating groups both play a significant role in both the magnitude and the nature of the nanowire band gaps, allowing tuning of the band gap by up to 1.1 eV. We also show for the first time how the nanowire diameter and surface termination shifts the absorption edge in the Ge nanowires to longer wavelengths. Thus, the combination of nanowire diameter and surface chemistry can be effectively utilised to tune the band gaps and thus light absorption properties of small diameter Ge nanowires.

Equity trading volume and volatility: Latent information arrivals and common long-run dependencies

This article examines the behavior of equity trading volume and volatility for the individual firms composing the Standard & Poor's 100 composite index. Using multivariate spectral methods, we find that fractionally integrated processes best describe the long-run temporal dependencies in both series. Consistent with a stylized mixture-of-distributions hypothesis model in which the aggregate "news"-arrival process possesses long-memory characteristics, the long-run hyperbolic decay rates appear to be common across each volume-volatility pair.

Information relaxations and duality in stochastic dynamic programs

We describe a general technique for determining upper bounds on maximal values (or lower bounds on minimal costs) in stochastic dynamic programs. In this approach, we relax the nonanticipativity constraints that require decisions to depend only on the information available at the time a decision is made and impose a "penalty" that punishes violations of nonanticipativity. In applications, the hope is that this relaxed version of the problem will be simpler to solve than the original dynamic program. The upper bounds provided by this dual approach complement lower bounds on values that may be found by simulating with heuristic policies. We describe the theory underlying this dual approach and establish weak duality, strong duality, and complementary slackness results that are analogous to the duality results of linear programming. We also study properties of good penalties. Finally, we demonstrate the use of this dual approach in an adaptive inventory control problem with an unknown and changing demand distribution and in valuing options with stochastic volatilities and interest rates. These are complex problems of significant practical interest that are quite difficult to solve to optimality. In these examples, our dual approach requires relatively little additional computation and leads to tight bounds on the optimal values. © 2010 INFORMS.

Hazard and risk assessment from Hurricane Ivan (2004) in Grenada using Geographical Information Systems and remote sensing

Hurricanes are destructive storms with strong winds, intense storm surges, and heavy rainfall. The resulting impact from a hurricane can include structural damage to buildings and infrastructure, flooding, and ultimately loss of human life. This paper seeks to identify the impact of Hurricane Ivan on the aected population of Grenada, one of the Caribbean islands. Hurricane Ivan made landfall on 7th September 2004 and resulted in 80% of the population being adversely aected. The methods that were used to model these impacts involved performing hazard and risk assessments using GIS and remote sensing techniques. Spatial analyses were used to create a hazard and a risk map. Hazards were identied initially as those caused by storm surges, severe winds speeds, and flooding events related to Hurricane Ivan. These estimated hazards were then used to create a risk map. An innovative approach was adopted, including the use of hillshading to assess the damage caused by high wind speeds. This paper explains in detail the methodology used and the results produced.

The importance of independent marine evidence provision in a multi-use environment: MBA information resources and The Marine Life Information Network.

One of the most pressing challenges today is the need to manage our oceans on a sustainable basis, balancing opportunities for exploitation with the need for conservation and protection. A vital tool for informing sustainable management is access to accurate, up-to-date marine environmental data and information, which is also seen as ‘independent’ by industry, conservationists, policy-makers and other Stakeholders. The Marine Biological Association has specialised in providing independent evidence for over a century and hosts a number of programmes dedicated to independent evidence provision. For example, the Marine Life Information Network (MarLIN) is the most comprehensive information resource for the marine environment of the British Isles and also the largest review of the effects of human activities and natural events on marine species and habitats ever undertaken. MarLIN, along with the Data Archive for Seabed Species and Habitats (DASSH and other MBA information resources, is currently being used to support a wide range of UK and European legislation as well as providing vital underpinning information for industry (e.g. through informing EIAs). We provide an overview of MarLIN in particular whilst examining the importance of ‘independent’ scientific information in a multi-use environment.

Information sharing and reporting systems in the UK and Ireland: Professional barriers to reporting child maltreatment concerns

Across the UK recent policy developments have focused on improved information sharing and inter-agency cooperation. Professional non-reporting of child maltreatment concerns has been consistently highlighted as a problem in a range of countries and the research literature indicates that this can happen for a variety of reasons. Characteristics such as the type of abuse and the threshold of evidence available are key factors, as are concerns that reporting will damage the professional-client relationship. Professional discipline can also impact on willingness to report, as can personal beliefs about abuse, attitudes towards child protection services and experiences of court processes. Research examining the role of organisational factors in information sharing and reporting emphasises the importance of training and there are some positive indications that training can increase professional awareness of reporting processes and requirements and help to increase knowledge of child abuse and its symptoms. Nonetheless, this is a complex issue and the need for training to go beyond simple awareness raising is recognised. In order to tackle non-reporting in a meaningful way, childcare professionals need access to on-going multidisciplinary training which is specifically tailored to address the range of different factors which impact on reporting attitudes and behaviours.