Parallel geostatistics for sparse and dense datasets

Very large spatially-referenced datasets, for example, those derived from satellite-based sensors which sample across the globe or large monitoring networks of individual sensors, are becoming increasingly common and more widely available for use in environmental decision making. In large or dense sensor networks, huge quantities of data can be collected over small time periods. In many applications the generation of maps, or predictions at specific locations, from the data in (near) real-time is crucial. Geostatistical operations such as interpolation are vital in this map-generation process and in emergency situations, the resulting predictions need to be available almost instantly, so that decision makers can make informed decisions and define risk and evacuation zones. It is also helpful when analysing data in less time critical applications, for example when interacting directly with the data for exploratory analysis, that the algorithms are responsive within a reasonable time frame. Performing geostatistical analysis on such large spatial datasets can present a number of problems, particularly in the case where maximum likelihood. Although the storage requirements only scale linearly with the number of observations in the dataset, the computational complexity in terms of memory and speed, scale quadratically and cubically respectively. Most modern commodity hardware has at least 2 processor cores if not more. Other mechanisms for allowing parallel computation such as Grid based systems are also becoming increasingly commonly available. However, currently there seems to be little interest in exploiting this extra processing power within the context of geostatistics. In this paper we review the existing parallel approaches for geostatistics. By recognising that diffeerent natural parallelisms exist and can be exploited depending on whether the dataset is sparsely or densely sampled with respect to the range of variation, we introduce two contrasting novel implementations of parallel algorithms based on approximating the data likelihood extending the methods of Vecchia [1988] and Tresp [2000]. Using parallel maximum likelihood variogram estimation and parallel prediction algorithms we show that computational time can be significantly reduced. We demonstrate this with both sparsely sampled data and densely sampled data on a variety of architectures ranging from the common dual core processor, found in many modern desktop computers, to large multi-node super computers. To highlight the strengths and weaknesses of the diffeerent methods we employ synthetic data sets and go on to show how the methods allow maximum likelihood based inference on the exhaustive Walker Lake data set.

Spatially clustered associations in health GIS

Overlaying maps using a desktop GIS is often the first step of a multivariate spatial analysis. The potential of this operation has increased considerably as data sources and Web services to manipulate them are becoming widely available via the Internet. Standards from the OGC enable such geospatial mashups to be seamless and user driven, involving discovery of thematic data. The user is naturally inclined to look for spatial clusters and correlation of outcomes. Using classical cluster detection scan methods to identify multivariate associations can be problematic in this context, because of a lack of control on or knowledge about background populations. For public health and epidemiological mapping, this limiting factor can be critical but often the focus is on spatial identification of risk factors associated with health or clinical status. Spatial entropy index HSu for the ScankOO analysis of the hypothetical dataset using a vicinity which is fixed by the number of points without distinction between their labels. (The size of the labels is proportional to the inverse of the index) In this article we point out that this association itself can ensure some control on underlying populations, and develop an exploratory scan statistic framework for multivariate associations. Inference using statistical map methodologies can be used to test the clustered associations. The approach is illustrated with a hypothetical data example and an epidemiological study on community MRSA. Scenarios of potential use for online mashups are introduced but full implementation is left for further research.

Spatially clustered associations in health related geospatial data

Overlaying maps using a desktop GIS is often the first step of a multivariate spatial analysis. The potential of this operation has increased considerably as data sources an dWeb services to manipulate them are becoming widely available via the Internet. Standards from the OGC enable such geospatial ‘mashups’ to be seamless and user driven, involving discovery of thematic data. The user is naturally inclined to look for spatial clusters and ‘correlation’ of outcomes. Using classical cluster detection scan methods to identify multivariate associations can be problematic in this context, because of a lack of control on or knowledge about background populations. For public health and epidemiological mapping, this limiting factor can be critical but often the focus is on spatial identification of risk factors associated with health or clinical status. In this article we point out that this association itself can ensure some control on underlying populations, and develop an exploratory scan statistic framework for multivariate associations. Inference using statistical map methodologies can be used to test the clustered associations. The approach is illustrated with a hypothetical data example and an epidemiological study on community MRSA. Scenarios of potential use for online mashups are introduced but full implementation is left for further research.

Dipole moments and Kerr effect of poly(n-vinylcarbazole) and its complexes with trinitrofluorenone

N-vinylcarbazole was polymerised using the free radical catalyst (azo-bisisobutyronitrile) and cationic catalysts (boron-trifluoride etherate and aluminium chloride). The polymers produced were characterised by molecular weight measurements and powder x-ray diffraction. The tacticity of the polymer samples was determined using proton and carbon-13 nuclear magnetic resonance spectroscopy. Measurements of their static dielectric permittivity and electro-optical birefringence (Kerr effect) in solution in 1,4-dioxane were carried out over a range of temperatures. The magnitudes of the dipole moments and Kerr constants were found to vary with changes in the tacticity of poly(N-vinylcarbazole). The results of these measurements support the view that the stereostructure of poly(N-vinylcarbazole) is sensitive to the mechanism of polymerisation. These results, together with proton and carbon-13 N.M.R. data, are discussed in terms of the possible conformations of the polymer chains and the relative orientation of the bulky carbazole side groups. The dielectric and molecular Kerr effect studies have also been carried out on complexes formed between 2,4,7-trinitro-9-fluorenone (TNF) and different stereoregular forms of poly(N-vinylcarbazole) in solution in 1,4-dioxane. The differences in the molar Kerr constants between pure (uncomplexed) and complexed poly(N-vinylcarbazole) samples were attributed to changes in optical anisotropy and dipole moments. A molecular modelling computer program Desktop Molecular Modeller was used to examine the 3/1 helical isotactic and 2/1 helical syndiotactic forms of poly(N-vinylcarbazole). These models were used to calculate the pitch distances of helices and the results were interpreted in terms of van der Waal's radii on TNF. This study indicated that the pitch distance in 3/1 isotactic helices was large enough to accommodate the bulky TNF molecules to form sandwich type charge transfer complexes whereas the pitch distance in syndiotactic poly(N-vinylcarbazole) was smaller and would not allow a similar type of complex formation.

Clinical decision support system for point of care use: ontology driven design and software implementation

OBJECTIVES: The objective of this research was to design a clinical decision support system (CDSS) that supports heterogeneous clinical decision problems and runs on multiple computing platforms. Meeting this objective required a novel design to create an extendable and easy to maintain clinical CDSS for point of care support. The proposed solution was evaluated in a proof of concept implementation. METHODS: Based on our earlier research with the design of a mobile CDSS for emergency triage we used ontology-driven design to represent essential components of a CDSS. Models of clinical decision problems were derived from the ontology and they were processed into executable applications during runtime. This allowed scaling applications' functionality to the capabilities of computing platforms. A prototype of the system was implemented using the extended client-server architecture and Web services to distribute the functions of the system and to make it operational in limited connectivity conditions. RESULTS: The proposed design provided a common framework that facilitated development of diversified clinical applications running seamlessly on a variety of computing platforms. It was prototyped for two clinical decision problems and settings (triage of acute pain in the emergency department and postoperative management of radical prostatectomy on the hospital ward) and implemented on two computing platforms-desktop and handheld computers. CONCLUSIONS: The requirement of the CDSS heterogeneity was satisfied with ontology-driven design. Processing of application models described with the help of ontological models allowed having a complex system running on multiple computing platforms with different capabilities. Finally, separation of models and runtime components contributed to improved extensibility and maintainability of the system.

Design and evaluation for the future of m-interaction

Mobile technology has been one of the major growth areas in computing over recent years (Urbaczewski, Valacich, & Jessup, 2003). Mobile devices are becoming increasingly diverse and are continuing to shrink in size and weight. Although this increases the portability of such devices, their usability tends to suffer. Fuelled almost entirely by lack of usability, users report high levels of frustration regarding interaction with mobile technologies (Venkatesh, Ramesh, & Massey, 2003). This will only worsen if interaction design for mobile technologies does not continue to receive increasing research attention. For the commercial benefit of mobility and mobile commerce (m-commerce) to be fully realized, users’ interaction experiences with mobile technology cannot be negative. To ensure this, it is imperative that we design the right types of mobile interaction (m-interaction); an important prerequisite for this is ensuring that users’ experience meets both their sensory and functional needs (Venkatesh, Ramesh, & Massey, 2003). Given the resource disparity between mobile and desktop technologies, successful electronic commerce (e-commerce) interface design and evaluation does not necessarily equate to successful m-commerce design and evaluation. It is, therefore, imperative that the specific needs of m-commerce are addressed–both in terms of design and evaluation. This chapter begins by exploring the complexities of designing interaction for mobile technology, highlighting the effect of context on the use of such technology. It then goes on to discuss how interaction design for mobile devices might evolve, introducing alternative interaction modalities that are likely to affect that future evolution. It is impossible, within a single chapter, to consider each and every potential mechanism for interacting with mobile technologies; to provide a forward-looking flavor of what might be possible, this chapter focuses on some more novel methods of interaction and does not, therefore, look at the typical keyboard and visual display-based interaction which, in essence, stem from the desktop interaction design paradigm. Finally, this chapter touches on issues associated with effective evaluation of m-interaction and mobile application designs. By highlighting some of the issues and possibilities for novel m-interaction design and evaluation, we hope that future designers will be encouraged to “think out of the box” in terms of their designs and evaluation strategies.

Handbook of research on user interface design and evaluation for mobile technology

In recent years, mobile technology has been one of the major growth areas in computing. Designing the user interface for mobile applications, however, is a very complex undertaking which is made even more challenging by the rapid technological developments in mobile hardware. Mobile human-computer interaction, unlike desktop-based interaction, must be cognizant of a variety of complex contextual factors affecting both users and technology. The Handbook of Research on User Interface Design and Evaluation provides students, researchers, educators, and practitioners with a compendium of research on the key issues surrounding the design and evaluation of mobile user interfaces, such as the physical environment and social context in which a mobile device is being used and the impact of multitasking behavior typically exhibited by mobile-device users. Compiling the expertise of over 150 leading experts from 26 countries, this exemplary reference tool will make an indispensable addition to every library collection.

A paradigm shift:alternative interaction techniques for use with mobile & wearable devices

Desktop user interface design originates from the fact that users are stationary and can devote all of their visual resource to the application with which they are interacting. In contrast, users of mobile and wearable devices are typically in motion whilst using their device which means that they cannot devote all or any of their visual resource to interaction with the mobile application -- it must remain with the primary task, often for safety reasons. Additionally, such devices have limited screen real estate and traditional input and output capabilities are generally restricted. Consequently, if we are to develop effective applications for use on mobile or wearable technology, we must embrace a paradigm shift with respect to the interaction techniques we employ for communication with such devices.This paper discusses why it is necessary to embrace a paradigm shift in terms of interaction techniques for mobile technology and presents two novel multimodal interaction techniques which are effective alternatives to traditional, visual-centric interface designs on mobile devices as empirical examples of the potential to achieve this shift.

Innocent but proven guilty:eliciting internalized false confessions using doctored-video evidence

More powerful computers and affordable digital-video equipment means that desktop-video editing is now accessible and popular. In two experiments, we investigated whether seeing fake-video evidence, or simply being told that video evidence exists, could lead people to believe they committed an act they never did. Subjects completed a computerized gambling task, and when they returned later the same day, we falsely accused them of cheating on the task. All of the subjects were told that incriminating video evidence existed, and half were also exposed to a fake video. See-video subjects were more likely to confess without resistance, and to internalize the act than told-video subjects, and see-video subjects tended to confabulate details more often than told-video subjects. We offer a metacognitive-based account of our results. Copyright © 2008 John Wiley & Sons, Ltd.