Higher-order co-occurrences for exploratory point pattern analysis and decision tree clustering on spatial data

Analyzing geographical patterns by collocating events, objects or their attributes has a long history in surveillance and monitoring, and is particularly applied in environmental contexts, such as ecology or epidemiology. The identification of patterns or structures at some scales can be addressed using spatial statistics, particularly marked point processes methodologies. Classification and regression trees are also related to this goal of finding "patterns" by deducing the hierarchy of influence of variables on a dependent outcome. Such variable selection methods have been applied to spatial data, but, often without explicitly acknowledging the spatial dependence. Many methods routinely used in exploratory point pattern analysis are2nd-order statistics, used in a univariate context, though there is also a wide literature on modelling methods for multivariate point pattern processes. This paper proposes an exploratory approach for multivariate spatial data using higher-order statistics built from co-occurrences of events or marks given by the point processes. A spatial entropy measure, derived from these multinomial distributions of co-occurrences at a given order, constitutes the basis of the proposed exploratory methods. © 2010 Elsevier Ltd.

Web based expert elicitation of uncertainties in environmental model inputs

When constructing and using environmental models, it is typical that many of the inputs to the models will not be known perfectly. In some cases, it will be possible to make observations, or occasionally physics-based uncertainty propagation, to ascertain the uncertainty on these inputs. However, such observations are often either not available or even possible, and another approach to characterising the uncertainty on the inputs must be sought. Even when observations are available, if the analysis is being carried out within a Bayesian framework then prior distributions will have to be specified. One option for gathering or at least estimating this information is to employ expert elicitation. Expert elicitation is well studied within statistics and psychology and involves the assessment of the beliefs of a group of experts about an uncertain quantity, (for example an input / parameter within a model), typically in terms of obtaining a probability distribution. One of the challenges in expert elicitation is to minimise the biases that might enter into the judgements made by the individual experts, and then to come to a consensus decision within the group of experts. Effort is made in the elicitation exercise to prevent biases clouding the judgements through well-devised questioning schemes. It is also important that, when reaching a consensus, the experts are exposed to the knowledge of the others in the group. Within the FP7 UncertWeb project (http://www.uncertweb.org/), there is a requirement to build a Webbased tool for expert elicitation. In this paper, we discuss some of the issues of building a Web-based elicitation system - both the technological aspects and the statistical and scientific issues. In particular, we demonstrate two tools: a Web-based system for the elicitation of continuous random variables and a system designed to elicit uncertainty about categorical random variables in the setting of landcover classification uncertainty. The first of these examples is a generic tool developed to elicit uncertainty about univariate continuous random variables. It is designed to be used within an application context and extends the existing SHELF method, adding a web interface and access to metadata. The tool is developed so that it can be readily integrated with environmental models exposed as web services. The second example was developed for the TREES-3 initiative which monitors tropical landcover change through ground-truthing at confluence points. It allows experts to validate the accuracy of automated landcover classifications using site-specific imagery and local knowledge. Experts may provide uncertainty information at various levels: from a general rating of their confidence in a site validation to a numerical ranking of the possible landcover types within a segment. A key challenge in the web based setting is the design of the user interface and the method of interacting between the problem owner and the problem experts. We show the workflow of the elicitation tool, and show how we can represent the final elicited distributions and confusion matrices using UncertML, ready for integration into uncertainty enabled workflows.We also show how the metadata associated with the elicitation exercise is captured and can be referenced from the elicited result, providing crucial lineage information and thus traceability in the decision making process.

Higher-order co-occurrences for exploratory point pattern analysis and decision tree clustering on spatial data

Analyzing geographical patterns by collocating events, objects or their attributes has a long history in surveillance and monitoring, and is particularly applied in environmental contexts, such as ecology or epidemiology. The identification of patterns or structures at some scales can be addressed using spatial statistics, particularly marked point processes methodologies. Classification and regression trees are also related to this goal of finding "patterns" by deducing the hierarchy of influence of variables on a dependent outcome. Such variable selection methods have been applied to spatial data, but, often without explicitly acknowledging the spatial dependence. Many methods routinely used in exploratory point pattern analysis are2nd-order statistics, used in a univariate context, though there is also a wide literature on modelling methods for multivariate point pattern processes. This paper proposes an exploratory approach for multivariate spatial data using higher-order statistics built from co-occurrences of events or marks given by the point processes. A spatial entropy measure, derived from these multinomial distributions of co-occurrences at a given order, constitutes the basis of the proposed exploratory methods. © 2010 Elsevier Ltd.

Sequence distributions in free-radical polymers

This thesis is concerned with demonstrating how the visual representation of the sequence distribution of individual monomer units, of a polymer, that would be observed upon polymerisation, may be utilised in designing and synthesizing polymers with relatively low cell adhesion characteristics, The initial part of this thesis is concerned with demonstrating the use of a computer simulation technique, in illustrating the sequence distribution that would be observed upon the polymerisation of a set of monomers. The power of the computer simulation technique has been demonstrated through the simulation of the sequence distributions of some generic contact lens materials. These generic contact lens materials were chosen simply because in the field of biomaterials their compositions are amongst the most systematically regulated and they present a wide range of compositions. The validity of the computer simulation technique has been assessed through the synthesis and analysis of linear free-radical polymers at different conversions. Two main parameters were examined, that of composition and the number-average sequence lengths of individual monomer units, at various conversions. The polymers were synthesized through the solution polymerisation process. The monomer composition was determined by elemental analysis and 13C nuclear magnetic analysis (NMR). Number-average sequence lengths were determined exclusively through 13C NMR. Although the computer simulation technique provides a visual representation of the monomer sequence distribution up to 100% conversion, these assessments were made on linear polymers at a reasonably high conversion (above 50%) but below 100% conversion of ease for analysis. The analyses proved that the computer simulation technique was reasonably accurate in predicting the sequence distribution of monomer units, upon polymerisation, in the polymer.An approach has been presented which allows one to manipulate the use of monomers, with their reactivity ratios, thereby enabling us to design polymers with controlled sequence distributions.Hydrogel membranes, with relatively controlled sequence distributions and polymerised to 100% conversion, were synthesized to represent prospective biomaterials. Cell adhesion studies were used as a biological probe to investigate the susceptibility of the surface of these membranes to cell adhesion. This was necessary in order to assess the surface biocompatibility or biotolerance of these prospective biomaterials.

The size frequency distributions of beta-amyloid deposit subtypes in Alzheimer's disease

The size frequency distributions of diffuse, primitive and classic beta/A4 deposits was studied in single sections in the hippocampus, parahippocampal gyrus (PHG) and lateral occipitotemporal gyrus (LOT) in five cases of Alzheimer's disease. In most brain regions, the size distribution of the diffuse deposits was significantly different from that of the primitive and classic deposits. The data suggested that larger diffuse deposits appeared to be converted less often into primitive and classic deposits. Significant differences in the size distribution of primitive deposits were commonly observed between brain regions in which there was no difference in the size distribution of the diffuse deposits. Hence, local brain factors may influence the size of diffuse deposit which can be converted into mature amyloid deposit.

Size frequency distributions of β-amyloid (Aβ) deposits:a comparative study of four neurodegenerative disorders

Deposition of ß-amyloid (Aß ), a 'signature' pathological lesion of Alzheimer's disease (AD), is also characteristic of Down's syndrome (DS), and has been observed in dementia with Lewy bodies (DLB) and corticobasal degeneration (CBD). To determine whether the growth of Aß deposits was similar in these disorders, the size frequency distributions of the diffuse ('pre-amyloid'), primitive ('neuritic'), and classic ('dense-cored') A ß deposits were compared in AD, DS, DLB, and CBD. All size distributions had essentially the same shape, i.e., they were unimodal and positively skewed. Mean size of Aß deposits, however, varied between disorders. Mean diameters of the diffuse, primitive, and classic deposits were greatest in DS, DS and CBD, and DS, respectively, while the smallest deposits, on average, were recorded in DLB. Although the shape of the frequency distributions was approximately log-normal, the model underestimated the frequency of smaller deposits and overestimated the frequency of larger deposits in all disorders. A 'power-law' model fitted the size distributions of the primitive deposits in AD, DS, and DLB, and the diffuse deposits in AD. The data suggest: (1) similarities in size distributions of Aß deposits among disorders, (2) growth of deposits varies with subtype and disorder, (3) different factors are involved in the growth of the diffuse/primitive and classic deposits, and (4) log-normal and power-law models do not completely account for the size frequency distributions.

A Bayesian modeling approach to multi-dimensional sentiment distributions prediction

Sentiment analysis has long focused on binary classification of text as either positive or negative. There has been few work on mapping sentiments or emotions into multiple dimensions. This paper studies a Bayesian modeling approach to multi-class sentiment classification and multidimensional sentiment distributions prediction. It proposes effective mechanisms to incorporate supervised information such as labeled feature constraints and document-level sentiment distributions derived from the training data into model learning. We have evaluated our approach on the datasets collected from the confession section of the Experience Project website where people share their life experiences and personal stories. Our results show that using the latent representation of the training documents derived from our approach as features to build a maximum entropy classifier outperforms other approaches on multi-class sentiment classification. In the more difficult task of multi-dimensional sentiment distributions prediction, our approach gives superior performance compared to a few competitive baselines. © 2012 ACM.

Representation of some class of time frequency distributions towards their determination through remote sensing techniques

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Regression analysis of ranked segment parameters for optic nerve head classification:a pilot study

Purpose: To determine whether curve-fitting analysis of the ranked segment distributions of topographic optic nerve head (ONH) parameters, derived using the Heidelberg Retina Tomograph (HRT), provide a more effective statistical descriptor to differentiate the normal from the glaucomatous ONH. Methods: The sample comprised of 22 normal control subjects (mean age 66.9 years; S.D. 7.8) and 22 glaucoma patients (mean age 72.1 years; S.D. 6.9) confirmed by reproducible visual field defects on the Humphrey Field Analyser. Three 10°-images of the ONH were obtained using the HRT. The mean topography image was determined and the HRT software was used to calculate the rim volume, rim area to disc area ratio, normalised rim area to disc area ratio and retinal nerve fibre cross-sectional area for each patient at 10°-sectoral intervals. The values were ranked in descending order, and each ranked-segment curve of ordered values was fitted using the least squares method. Results: There was no difference in disc area between the groups. The group mean cup-disc area ratio was significantly lower in the normal group (0.204 ± 0.16) compared with the glaucoma group (0.533 ± 0.083) (p < 0.001). The visual field indices, mean deviation and corrected pattern S.D., were significantly greater (p < 0.001) in the glaucoma group (-9.09 dB ± 3.3 and 7.91 ± 3.4, respectively) compared with the normal group (-0.15 dB ± 0.9 and 0.95 dB ± 0.8, respectively). Univariate linear regression provided the best overall fit to the ranked segment data. The equation parameters of the regression line manually applied to the normalised rim area-disc area and the rim area-disc area ratio data, correctly classified 100% of normal subjects and glaucoma patients. In this study sample, the regression analysis of ranked segment parameters method was more effective than conventional ranked segment analysis, in which glaucoma patients were misclassified in approximately 50% of cases. Further investigation in larger samples will enable the calculation of confidence intervals for normality. These reference standards will then need to be investigated for an independent sample to fully validate the technique. Conclusions: Using a curve-fitting approach to fit ranked segment curves retains information relating to the topographic nature of neural loss. Such methodology appears to overcome some of the deficiencies of conventional ranked segment analysis, and subject to validation in larger scale studies, may potentially be of clinical utility for detecting and monitoring glaucomatous damage. © 2007 The College of Optometrists.

Analysis of the Distributions of Color Characteristics in Art Painting Images

In this paper we study some of the characteristics of the art painting image color semantics. We analyze the color features of differ- ent artists and art movements. The analysis includes exploration of hue, saturation and luminance. We also use quartile’s analysis to obtain the dis- tribution of the dispersion of defined groups of paintings and measure the degree of purity for these groups. A special software system “Art Paint- ing Image Color Semantics” (APICSS) for image analysis and retrieval was created. The obtained result can be used for automatic classification of art paintings in image retrieval systems, where the indexing is based on color characteristics.

Reliability for Beta Models

In the area of stress-strength models there has been a large amount of work as regards estimation of the reliability R = Pr(X2 < X1 ) when X1 and X2 are independent random variables belonging to the same univariate family of distributions. The algebraic form for R = Pr(X2 < X1 ) has been worked out for the majority of the well-known distributions including Normal, uniform, exponential, gamma, weibull and pareto. However, there are still many other distributions for which the form of R is not known. We have identified at least some 30 distributions with no known form for R. In this paper we consider some of these distributions and derive the corresponding forms for the reliability R. The calculations involve the use of various special functions.

On some Mixture Distributions

The aim of this paper is to establish some mixture distributions that arise in stochastic processes. Some basic functions associated with the probability mass function of the mixture distributions, such as k-th moments, characteristic function and factorial moments are computed. Further we obtain a three-term recurrence relation for each established mixture distribution.

Matrix-Variate Statistical Distributions and Fractional Calculus

MSC 2010: 15A15, 15A52, 33C60, 33E12, 44A20, 62E15 Dedicated to Professor R. Gorenflo on the occasion of his 80th birthday