The environment is made up of composition of small particles. Hence, particle simulation is an important tool in many scientific and engineering research fields to simulate the real life processes of the environment. Because of the enormous amount of data in such simulations, data management, storage and processing are very challenging tasks. Spatial Distance Histogram (SDH) is one of the most popular queries being used in this field. In this thesis, we are interested in investigating the performance of improvement of an existing algorithm for computing SDH. The algorithm already being used is using a conceptual data structure called density map which is implemented via a quad tree index. An algorithm having density maps implemented via binary tree is proposed in this thesis. After carrying out many experiments and analysis of the data, we figure out that although the binary tree approach seems efficient in earlier stage, it is same as the quad tree approach in terms of time complexity. However, it provides an improvement in computing time by a constant factor for some data inputs. The second part of this thesis is dedicated to an approach that can potentially reduce the computational time to a great extent by taking advantage of regions where data points are uniformly distributed.

Gestational diabetes Mellitus (GDM), a condition involving abnormal levels of glucose in the blood plasma has seen a rapid surge amongst the gestating mothers belonging to different regions and ethnicities around the world. Cur- rent method of screening and diagnosing GDM is restricted to Oral Glucose Tolerance Test (OGTT). With the advent of machine learning algorithms, the healthcare has seen a surge of machine learning methods for disease diag- nosis which are increasingly being employed in a clinical setup. Yet in the area of GDM, there has not been wide spread utilization of these algorithms to generate multi-parametric diagnostic models to aid the clinicians for the aforementioned condition diagnosis.In literature, there is an evident scarcity of application of machine learn- ing algorithms for the GDM diagnosis. It has been limited to the proposed use of some very simple algorithms like logistic regression. Hence, we have attempted to address this research gap by employing a wide-array of machine learning algorithms, known to be effective for binary classification, for GDM classification early on amongst gestating mother. This can aid the clinicians for early diagnosis of GDM and will offer chances to mitigate the adverse out- comes related to GDM among the gestating mother and their progeny.We set up an empirical study to look into the performance of different ma- chine learning algorithms used specifically for the task of GDM classification. These algorithms were trained on a set of chosen predictor variables by the ex- perts. Then compared the results with the existing machine learning methods in the literature for GDM classification based on a set of performance metrics. Our model couldn’t outperform the already proposed machine learning mod- els for GDM classification. We could attribute it to our chosen set of predictor variable and the under reporting of various performance metrics like precision in the existing literature leading to a lack of informed comparison. / Graviditetsdiabetes Mellitus (GDM), ett tillstånd som involverar onormala ni- våer av glukos i blodplasma har haft en snabb kraftig ökning bland de drab- bade mammorna som tillhör olika regioner och etniciteter runt om i världen. Den nuvarande metoden för screening och diagnos av GDM är begränsad till Oralt glukosetoleranstest (OGTT). Med tillkomsten av maskininlärningsalgo- ritmer har hälso- och sjukvården sett en ökning av maskininlärningsmetoder för sjukdomsdiagnos som alltmer används i en klinisk installation. Ändå inom GDM-området har det inte använts stor spridning av dessa algoritmer för att generera multiparametriska diagnostiska modeller för att hjälpa klinikerna för ovannämnda tillståndsdiagnos.I litteraturen finns det en uppenbar brist på tillämpning av maskininlär- ningsalgoritmer för GDM-diagnosen. Det har begränsats till den föreslagna användningen av några mycket enkla algoritmer som logistisk regression. Där- för har vi försökt att ta itu med detta forskningsgap genom att använda ett brett spektrum av maskininlärningsalgoritmer, kända för att vara effektiva för binär klassificering, för GDM-klassificering tidigt bland gesterande mamma. Det- ta kan hjälpa klinikerna för tidig diagnos av GDM och kommer att erbjuda chanser att mildra de negativa utfallen relaterade till GDM bland de dödande mamma och deras avkommor.Vi inrättade en empirisk studie för att undersöka prestandan för olika ma- skininlärningsalgoritmer som används specifikt för uppgiften att klassificera GDM. Dessa algoritmer tränades på en uppsättning valda prediktorvariabler av experterna. Jämfört sedan resultaten med de befintliga maskininlärnings- metoderna i litteraturen för GDM-klassificering baserat på en uppsättning pre- standametriker. Vår modell kunde inte överträffa de redan föreslagna maskininlärningsmodellerna för GDM-klassificering. Vi kunde tillskriva den valda uppsättningen prediktorvariabler och underrapportering av olika prestanda- metriker som precision i befintlig litteratur vilket leder till brist på informerad jämförelse.

In recent years, the popularity of wind energy has grown. It is starting to play a large role in generating renewable, clean energy around the world. In New Zealand, there is increasing recognition and awareness of global warming and the pollution caused by burning fossil fuels, as well as the increased difficulty of obtaining oil from foreign sources, and the fluctuating price of non-renewable energy products. This makes wind energy a very attractive alternative to keep New Zealand clean and green. There are many issues involved in wind farm development. These issues can be grouped into two categories - economic issues and environmental issues. Wind farm developers often use site selection process to minimise the impact of these issues. This thesis aims to develop GIS based models that provide effective decision support tool for evaluating, at a regional scale, potential wind farm locations. This thesis firstly identifies common issues involved in wind farm development. Then, by reviewing previous research on wind farm site selection, methods and models used by academic and corporate sector to solve issues are listed. Criteria for an effective decision support tool are also discussed. In this case, an effective decision support tool needs to be flexible, easy to implement and easy to use. More specifically, an effective decision support tool needs to provide users the ability to identify areas that are suitable for wind farm development based on different criteria. Having established the structure and criteria for a wind farm analysis model, a GIS based tool was implemented using AML code using a Boolean logic model approach. This method uses binary maps for the final analysis. There are a total of 3645 output maps produced based on different combination of criteria. These maps can be used to conduct sensitivity analysis. This research concludes that an effective GIS analysis tool can be developed for provide effective decision support for evaluating wind farm sites.

The photocatalytic oxidation of trichloroethylene (TCE), ethylene. ethane and toluene on TiO2, Pt/TiO2 and Ag/TiO2 were investigated in a dedicated reactor set-up operated at room temperature and ambient pressure condition. The gas phase experiments were carried out for both single and binary mixtures of these chemicals to identify the role of Pt and Ag metallisation in the photocatalytic oxidation of different contaminants. In a single contaminant system, the presence of Pt enhanced the oxidation of ethylene, ethane and toluene but detrimental to the oxidation of TCE. In the oxidation of ethylene, Pt enhanced the oxidation by acting as catalyst and as electron sink. However, in ethane oxidation, the enhancement was solely associated to the ability of Pt to act as electron sink. The detrimental effect observed in TCE oxidation was attributed to Pt and Cl interaction, which formed a persistent inorganic chlorine species decreasing the overall Pt/TiO2 photocatalyst performance. Interestingly, Ag did not show any significant effect to the oxidation of any single system degradation. In binary system degradation, where TCE and another organic compound either ethylene, ethane or toluene were degraded simultaneously, Pt always caused a detrimental effect due to its strong interaction with Cl. However, the presence of Ag and Cl gives a more synergetic effect. Ag was found to provide sites to temporarily trap chlorine radicals as AgCl. Under illumination, electrons transferred from Cl to Ag forming chlorine radicals that could react with the surface contaminant enhancing its breakdown and mineralization.

Previous research indicates that many important changes in executive functions, or higher cognitive capacities, occur between the ages of three and five years. Additionally, a distinction can be made between the cognitive functions associated with two different cortical regions. The functions of the dorsolateral prefrontal cortex (DL-PFC) are assessed using 'cool' tasks that are abstract and decontextualised. In contrast, the functions of the orbitofrontal cortex (OFC) are assessed using 'hot' tasks that require flexible appraisal of the affective significance of stimuli (Zelazo & Müller, 2002). Different clinical populations have been hypothesized to differ in terms of their impairment on tasks associated with each area of functioning. Current research conclusions regarding the primacy of hot versus cool executive function impairments are limited, however, as they have not taken complexity into account. That is, tasks currently used in investigations of hot and cool executive functions might differ in terms of the complexity of the cognitive processes that the tasks require. Therefore, comparisons across tasks may be misleading because these tasks vary in terms of the demands they place on participants as well as their hot versus cool status. While complexity theories have been applied to a number of cool tasks, only one hot task, those measuring theory-of-mind abilities, have been analysed in terms of complexity. One aim of the current research was to modify several tasks presumed to measure OFC performance to include a complexity manipulation. Tasks from three hot domains (conditional discrimination, the Children's Gambling Task, and future-oriented decision-making) were analysed in terms of their relational complexity, that is, the number of related entities or arguments inherent in a task or concept (Halford, 1993). Based on these complexity analyses, binary-relational and ternary-relational items of each of these tasks were developed or existing tasks were selected and/or modified. The binary-relational items were closely matched to the ternary-relational items in terms of stimuli and procedure, however, they were lower in complexity. After pilot testing, the three new measures of hot executive functioning were included in a larger test battery that was administered to a sample of 120 normally developing 3-, 4-, 5- and 6-year-old children. Existing binary- and ternary-relational items assessing theory-of-mind (a hot task) and three cool measures (transitivity, class inclusion and the Dimensional Change Card Sort test) were also included. The inclusion of measures of both hot and cool executive functions, each with complexity manipulated, allowed for the examination of a possible differential age of emergence of executive abilities associated with the DL-PFC versus the OFC. In support of the relational complexity approach, significant complexity effects were found across all seven tasks. Items at a higher level of complexity were experienced as relatively more difficult by children of all ages. Significant effects of age were also observed, with performance across all tasks increasing with age. The age effects were strongest on the ternary-relational items. The pass-fail data indicated that the majority of children in all age groups succeeded on the binary-relational items. However, it was not until a median of five years of age that children were able to process ternary relations. Consequently, the ternary-relational items produce the greatest differences in performance between the four age groups. The overall pattern of the results also suggested that a distinction can be made between the ages of emergence of abilities associated with the OFC versus the DL-PFC. The results of the pass-fail percentages, patterns of age-related change and age effects on domain factor scores all suggested that while hot executive functions may begin to develop around four years of age, similar levels of improvement are not seen in cool executive functions until five years of age. Thus, the ability to succeed on ternary-relational items of hot executive function tasks appeared to emerge slightly earlier than the cool executive function tasks. Complexity appears to be a critical factor underlying children's performance on executive function tasks, and future assessment regarding the development of executive abilities will benefit from keeping this in mind. While some refinement of new task items may be beneficial, the current test battery may have utility in further examinations of the executive profiles underlying clinical groups, such as children with autism and ADHD.

A Temporal database is one that supports some aspect of time distinct from user defined time. Over the last two decades interest in the field of temporal databases has increased significantly, with contributions from many researchers. However, the lack of efficient access methods is perhaps one of the reasons why commercial RDBMS vendors have been reluctant to adopt the advances in temporal database research. Therefore, an obvious research question is: can we develop more robust and more efficient access methods for temporal databases than the existing ones? This thesis attempts to address this question, and the main contributions of this study are summarised as follows: We investigated different representations of 'now' and how the modelling of current time influences the efficiency of accessing 'now relative' temporal data. A new method, called the 'Point' approach, is proposed. Our approach not only elegantly models the current time but also significantly outperforms the existing methods. We proposed a new index structure, called a Virtual Binary tree (VB-tree), based on spatial representation of interval data and a regular triangular decomposition of this space. Further, we described a sound and complete query algorithm. The performance of the algorithm is then evaluated both asymptotically and experimentally with respect to the state-of-the-art in the field. We claim that the VB-tree requires less space and uses fewer disk accesses than the currently best known structure - the RI-tree.

On s'intéresse aux invariants pour l'action naturelle du groupe SL_2<br />sur l'algèbre B des coordonnées homogènes de la Grassmannienne des<br />pinceaux de formes quintiques binaires. La variété quotient<br />Proj(B^SL_2) est un candidat naturel pour la variété de modules des<br />quintiques gauches rationnelles.<br /><br />Un procédé connu établit une correspondance birationnelle et<br />équivariante entre la Grassmannienne des pinceaux de formes binaires<br />de degré d et l'espace projectif des formes binaires de degré 2d-2.<br />Lorsque le degré d est 5, cela suggère de comparer l'algèbre B^SL_2 et<br />l'algèbre des invariants d'une forme octique binaire. Cette algèbre a<br />été décrite en détail par T. Shioda en 1967.<br /><br />Nous établissons pour B^SL_2 un résultat analogue à celui de T.<br />Shioda : l'algèbre B^SL_2 est le quotient de l'algèbre de polynômes à<br />neuf indéterminées R=C[x_1,x_2,x_3,x'_3,x_4,x_5,x'_5,x_6,x_7] (les<br />indices donnent les degrés des indéterminées) par l'idéal des<br />4-Pfaffiens d'une matrice alternée 5x5 ; on identifie (numériquement)<br />la résolution libre minimale du R-module B^SL_2 ; enfin, on obtient<br />une famille génératrice minimale de l'algèbre B^SL_2.<br /><br />Pour y parvenir on commence par étendre la formule de T. Springer<br />(donnant la série de Poincaré de l'algèbre des invariants d'une forme<br />binaire) à l'algèbre des coordonnées homogènes d'une Grassmannienne.<br /><br /><br />Le point clé suivant consiste en l'identification d'un système de<br />paramètres homogènes. C'est possible grâce à une caractérisation, au<br />moyen du morphisme Wronskien, de la stabilité sur la Grassmannienne.<br />Il faut ensuite étudier les covariants d'ordre 4 et degré 2, ce qui<br />donne lieu à quelques énoncés de nature géométrique.<br /><br />Ces techniques permettent également de décrire les algèbres<br />d'invariants des pinceaux de cubiques et quartiques. Par ailleurs<br />l'étude du Wronskien conduit à de nouvelles formules de pléthysme.

<p>This Masters thesis studies the concept of local binary patterns, which describe the neighbourhood of a pixel in a digital image by binary derivatives. The operator is often used in texture analysis and has been successfully used in facial recognition.</p><p>This thesis suggests two methods based on some basic ideas of Björn Kruse and studies of literature on the subject. The first suggested method presented is an algorithm which reproduces images from their local binary patterns by a kind of integration of the binary derivatives. This method is a way to prove the preservation of information. The second suggested method is a technique of interpolating missing pixels in a single CCD camera based on local binary patterns and machine learning. The algorithm has shown some very promising results even though in its current form it does not keep up with the best algorithms of today.</p>

M.A. Epstein. / "March 14, 1958"--Cover. "Reprinted from the 1958 IRE National Convention Record, Part 4"--P. 69. / Bibliography: p. 66. / Army Signal Corps Contract DA36-039-sc-64637. Dept. of the Army Task 3-99-06-108 and Project 3-99-00-100.

One of the main criticisms of previously studied label noise models in the PAC-learning framework is the inability of such models to represent the noise in real world data. In this thesis, we study this problem by introducing a framework for modeling label noise and suggesting four new label noise models. We prove positive learnability results for these noise models in learning simple concept classes and discuss the difficulty of the problem of learning other interesting concept classes under these new models. In addition, we study the previous general learning algorithm, called the minimum pn-disagreement strategy, that is used to prove learnability results in the PAC-learning framework both in the absence and presence of noise. Because of limitations of the minimum pn-disagreement strategy, we propose a new general learning algorithm called the minimum nn-disagreement strategy. Finally, for both minimum pn-disagreement strategy and minimum nn-disagreement strategy, we investigate some properties of label noise models that provide sufficient conditions for the learnability of specific concept classes.