Accuracy of cornea and lens biometry using anterior segment optical coherence tomography

We assess the accuracy of the Visante anterior segment optical coherence tomographer (AS-OCT) and present improved formulas for measurement of surface curvature and axial separation. Measurements are made in physical model eyes. Accuracy is compared for measurements of corneal thickness (d1) and anterior chamber depth (d2) using-built-in AS-OCT software versus the improved scheme. The improved scheme enables measurements of lens thickness (d 3) and surface curvature, in the form of conic sections specified by vertex radii and conic constants. These parameters are converted to surface coordinates for error analysis. The built-in AS-OCT software typically overestimates (mean±standard deviation(SD)]d1 by +62±4 μm and d2 by +4±88μm. The improved scheme reduces d1 (-0.4±4 μm) and d2 (0±49 μm) errors while also reducing d3 errors from +218±90 (uncorrected) to +14±123 μm (corrected). Surface x coordinate errors gradually increase toward the periphery. Considering the central 6-mm zone of each surface, the x coordinate errors for anterior and posterior corneal surfaces reached +3±10 and 0±23 μm, respectively, with the improved scheme. Those of the anterior and posterior lens surfaces reached +2±22 and +11±71 μm, respectively. Our improved scheme reduced AS-OCT errors and could, therefore, enhance pre- and postoperative assessments of keratorefractive or cataract surgery, including measurement of accommodating intraocular lenses. © 2007 Society of Photo-Optical Instrumentation Engineers.

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.

Managing uncertainty in complex stochastic models: design and emulation of a Rabies model

In this paper we present a novel method for emulating a stochastic, or random output, computer model and show its application to a complex rabies model. The method is evaluated both in terms of accuracy and computational efficiency on synthetic data and the rabies model. We address the issue of experimental design and provide empirical evidence on the effectiveness of utilizing replicate model evaluations compared to a space-filling design. We employ the Mahalanobis error measure to validate the heteroscedastic Gaussian process based emulator predictions for both the mean and (co)variance. The emulator allows efficient screening to identify important model inputs and better understanding of the complex behaviour of the rabies model.

Rules for mapping a conceptual model onto various data base management systems. Available in 2 volumes.

The design and implementation of data bases involve, firstly, the formulation of a conceptual data model by systematic analysis of the structure and information requirements of the organisation for which the system is being designed; secondly, the logical mapping of this conceptual model onto the data structure of the target data base management system (DBMS); and thirdly, the physical mapping of this structured model into storage structures of the target DBMS. The accuracy of both the logical and physical mapping determine the performance of the resulting systems. This thesis describes research which develops software tools to facilitate the implementation of data bases. A conceptual model describing the information structure of a hospital is derived using the Entity-Relationship (E-R) approach and this model forms the basis for mapping onto the logical model. Rules are derived for automatically mapping the conceptual model onto relational and CODASYL types of data structures. Further algorithms are developed for partly automating the implementation of these models onto INGRES, MIMER and VAX-11 DBMS.

A rule based model for the objective determination of the e-business requirements of a manufacturing organisation

The starting point of this research was the belief that manufacturing and similar industries need help with the concept of e-business, especially in assessing the relevance of possible e-business initiatives. The research hypotheses was that it should be possible to produce a systematic model that defines, at a useful level of detail, the probable e-business requirements of an organisation based on objective criteria with an accuracy of 85%-90%. This thesis describes the development and validation of such a model. A preliminary model was developed from a variety of sources, including a survey of current and planned e-business activity and representative examples of e-business material produced by e-business solution providers. The model was subject to a process of testing and refinement based on recursive case studies, with controls over the improving accuracy and stability of the model. Useful conclusions were also possible as to the relevance of e-business functions to the case study participants themselves. Techniques were evolved to synthesise the e-business requirements of an organisation and present them at a management summary level of detail. The results of applying these techniques to all the case studies used in this research were discussed. The conclusion of the research was that the case study methodology employed was successful. A model was achieved suitable for practical application in a manufacturing organisation requiring help with a requirements definition process.

An accurate Markov model for slotted CSMA/CA algorithm in IEEE 802.15.4 networks

In this letter we propose an Markov model for slotted CSMA/CA algorithm working in a non-acknowledgement mode, specified in IEEE 802.15.4 standard. Both saturation throughput and energy consumption are modeled as functions of backoff window size, number of contending devices and frame length. Simulations show that the proposed model can achieve a very high accuracy (less than 1% mismatch) if compared to all existing models (bigger than 10% mismatch).

Feature LDA:a supervised topic model for automatic detection of web API documentations from the web

Web APIs have gained increasing popularity in recent Web service technology development owing to its simplicity of technology stack and the proliferation of mashups. However, efficiently discovering Web APIs and the relevant documentations on the Web is still a challenging task even with the best resources available on the Web. In this paper we cast the problem of detecting the Web API documentations as a text classification problem of classifying a given Web page as Web API associated or not. We propose a supervised generative topic model called feature latent Dirichlet allocation (feaLDA) which offers a generic probabilistic framework for automatic detection of Web APIs. feaLDA not only captures the correspondence between data and the associated class labels, but also provides a mechanism for incorporating side information such as labelled features automatically learned from data that can effectively help improving classification performance. Extensive experiments on our Web APIs documentation dataset shows that the feaLDA model outperforms three strong supervised baselines including naive Bayes, support vector machines, and the maximum entropy model, by over 3% in classification accuracy. In addition, feaLDA also gives superior performance when compared against other existing supervised topic models.

Rapid testing for group B streptococcus during labour: a test accuracy study with evaluation of acceptability and cost-effectiveness

OBJECTIVE: To determine the accuracy, acceptability and cost-effectiveness of polymerase chain reaction (PCR) and optical immunoassay (OIA) rapid tests for maternal group B streptococcal (GBS) colonisation at labour. DESIGN: A test accuracy study was used to determine the accuracy of rapid tests for GBS colonisation of women in labour. Acceptability of testing to participants was evaluated through a questionnaire administered after delivery, and acceptability to staff through focus groups. A decision-analytic model was constructed to assess the cost-effectiveness of various screening strategies. SETTING: Two large obstetric units in the UK. PARTICIPANTS: Women booked for delivery at the participating units other than those electing for a Caesarean delivery. INTERVENTIONS: Vaginal and rectal swabs were obtained at the onset of labour and the results of vaginal and rectal PCR and OIA (index) tests were compared with the reference standard of enriched culture of combined vaginal and rectal swabs. MAIN OUTCOME MEASURES: The accuracy of the index tests, the relative accuracies of tests on vaginal and rectal swabs and whether test accuracy varied according to the presence or absence of maternal risk factors. RESULTS: PCR was significantly more accurate than OIA for the detection of maternal GBS colonisation. Combined vaginal or rectal swab index tests were more sensitive than either test considered individually [combined swab sensitivity for PCR 84% (95% CI 79-88%); vaginal swab 58% (52-64%); rectal swab 71% (66-76%)]. The highest sensitivity for PCR came at the cost of lower specificity [combined specificity 87% (95% CI 85-89%); vaginal swab 92% (90-94%); rectal swab 92% (90-93%)]. The sensitivity and specificity of rapid tests varied according to the presence or absence of maternal risk factors, but not consistently. PCR results were determinants of neonatal GBS colonisation, but maternal risk factors were not. Overall levels of acceptability for rapid testing amongst participants were high. Vaginal swabs were more acceptable than rectal swabs. South Asian women were least likely to have participated in the study and were less happy with the sampling procedure and with the prospect of rapid testing as part of routine care. Midwives were generally positive towards rapid testing but had concerns that it might lead to overtreatment and unnecessary interference in births. Modelling analysis revealed that the most cost-effective strategy was to provide routine intravenous antibiotic prophylaxis (IAP) to all women without screening. Removing this strategy, which is unlikely to be acceptable to most women and midwives, resulted in screening, based on a culture test at 35-37 weeks' gestation, with the provision of antibiotics to all women who screened positive being most cost-effective, assuming that all women in premature labour would receive IAP. The results were sensitive to very small increases in costs and changes in other assumptions. Screening using a rapid test was not cost-effective based on its current sensitivity, specificity and cost. CONCLUSIONS: Neither rapid test was sufficiently accurate to recommend it for routine use in clinical practice. IAP directed by screening with enriched culture at 35-37 weeks' gestation is likely to be the most acceptable cost-effective strategy, although it is premature to suggest the implementation of this strategy at present.

Pulse oximetry as a screening test for congenital heart defects in newborn infants: a test accuracy study with evaluation of acceptability and cost-effectiveness

Background: Screening for congenital heart defects (CHDs) relies on antenatal ultrasound and postnatal clinical examination; however, life-threatening defects often go undetected. Objective: To determine the accuracy, acceptability and cost-effectiveness of pulse oximetry as a screening test for CHDs in newborn infants. Design: A test accuracy study determined the accuracy of pulse oximetry. Acceptability of testing to parents was evaluated through a questionnaire, and to staff through focus groups. A decision-analytic model was constructed to assess cost-effectiveness. Setting: Six UK maternity units. Participants: These were 20,055 asymptomatic newborns at = 35 weeks’ gestation, their mothers and health-care staff. Interventions: Pulse oximetry was performed prior to discharge from hospital and the results of this index test were compared with a composite reference standard (echocardiography, clinical follow-up and follow-up through interrogation of clinical databases). Main outcome measures: Detection of major CHDs – defined as causing death or requiring invasive intervention up to 12 months of age (subdivided into critical CHDs causing death or intervention before 28 days, and serious CHDs causing death or intervention between 1 and 12 months of age); acceptability of testing to parents and staff; and the cost-effectiveness in terms of cost per timely diagnosis. Results: Fifty-three of the 20,055 babies screened had a major CHD (24 critical and 29 serious), a prevalence of 2.6 per 1000 live births. Pulse oximetry had a sensitivity of 75.0% [95% confidence interval (CI) 53.3% to 90.2%] for critical cases and 49.1% (95% CI 35.1% to 63.2%) for all major CHDs. When 23 cases were excluded, in which a CHD was already suspected following antenatal ultrasound, pulse oximetry had a sensitivity of 58.3% (95% CI 27.7% to 84.8%) for critical cases (12 babies) and 28.6% (95% CI 14.6% to 46.3%) for all major CHDs (35 babies). False-positive (FP) results occurred in 1 in 119 babies (0.84%) without major CHDs (specificity 99.2%, 95% CI 99.0% to 99.3%). However, of the 169 FPs, there were six cases of significant but not major CHDs and 40 cases of respiratory or infective illness requiring medical intervention. The prevalence of major CHDs in babies with normal pulse oximetry was 1.4 (95% CI 0.9 to 2.0) per 1000 live births, as 27 babies with major CHDs (6 critical and 21 serious) were missed. Parent and staff participants were predominantly satisfied with screening, perceiving it as an important test to detect ill babies. There was no evidence that mothers given FP results were more anxious after participating than those given true-negative results, although they were less satisfied with the test. White British/Irish mothers were more likely to participate in the study, and were less anxious and more satisfied than those of other ethnicities. The incremental cost-effectiveness ratio of pulse oximetry plus clinical examination compared with examination alone is approximately £24,900 per timely diagnosis in a population in which antenatal screening for CHDs already exists. Conclusions: Pulse oximetry is a simple, safe, feasible test that is acceptable to parents and staff and adds value to existing screening. It is likely to identify cases of critical CHDs that would otherwise go undetected. It is also likely to be cost-effective given current acceptable thresholds. The detection of other pathologies, such as significant CHDs and respiratory and infective illnesses, is an additional advantage. Other pulse oximetry techniques, such as perfusion index, may enhance detection of aortic obstructive lesions.

Estimating the SCAN*PRO model of store sales: HB, FM or just OLS?

In this paper we investigate whether consideration of store-level heterogeneity in marketing mix effects improves the accuracy of the marketing mix elasticities, fit, and forecasting accuracy of the widely-applied SCAN*PRO model of store sales. Models with continuous and discrete representations of heterogeneity, estimated using hierarchical Bayes (HB) and finite mixture (FM) techniques, respectively, are empirically compared to the original model, which does not account for store-level heterogeneity in marketing mix effects, and is estimated using ordinary least squares (OLS). The empirical comparisons are conducted in two contexts: Dutch store-level scanner data for the shampoo product category, and an extensive simulation experiment. The simulation investigates how between- and within-segment variance in marketing mix effects, error variance, the number of weeks of data, and the number of stores impact the accuracy of marketing mix elasticities, model fit, and forecasting accuracy. Contrary to expectations, accommodating store-level heterogeneity does not improve the accuracy of marketing mix elasticities relative to the homogeneous SCAN*PRO model, suggesting that little may be lost by employing the original homogeneous SCAN*PRO model estimated using ordinary least squares. Improvements in fit and forecasting accuracy are also fairly modest. We pursue an explanation for this result since research in other contexts has shown clear advantages from assuming some type of heterogeneity in market response models. In an Afterthought section, we comment on the controversial nature of our result, distinguishing factors inherent to household-level data and associated models vs. general store-level data and associated models vs. the unique SCAN*PRO model specification.

Joint sentiment/topic model for sentiment analysis

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework based on Latent Dirichlet Allocation (LDA), called joint sentiment/topic model (JST), which detects sentiment and topic simultaneously from text. Unlike other machine learning approaches to sentiment classification which often require labeled corpora for classifier training, the proposed JST model is fully unsupervised. The model has been evaluated on the movie review dataset to classify the review sentiment polarity and minimum prior information have also been explored to further improve the sentiment classification accuracy. Preliminary experiments have shown promising results achieved by JST.

A four-factor user interaction model for content-based image retrieval

In order to bridge the “Semantic gap”, a number of relevance feedback (RF) mechanisms have been applied to content-based image retrieval (CBIR). However current RF techniques in most existing CBIR systems still lack satisfactory user interaction although some work has been done to improve the interaction as well as the search accuracy. In this paper, we propose a four-factor user interaction model and investigate its effects on CBIR by an empirical evaluation. Whilst the model was developed for our research purposes, we believe the model could be adapted to any content-based search system.

A fuzzy decision tree-based duration model for Standard Yorùbá text-to-speech synthesis

In this paper, we present syllable-based duration modelling in the context of a prosody model for Standard Yorùbá (SY) text-to-speech (TTS) synthesis applications. Our prosody model is conceptualised around a modular holistic framework. This framework is implemented using the Relational Tree (R-Tree) techniques. An important feature of our R-Tree framework is its flexibility in that it facilitates the independent implementation of the different dimensions of prosody, i.e. duration, intonation, and intensity, using different techniques and their subsequent integration. We applied the Fuzzy Decision Tree (FDT) technique to model the duration dimension. In order to evaluate the effectiveness of FDT in duration modelling, we have also developed a Classification And Regression Tree (CART) based duration model using the same speech data. Each of these models was integrated into our R-Tree based prosody model. We performed both quantitative (i.e. Root Mean Square Error (RMSE) and Correlation (Corr)) and qualitative (i.e. intelligibility and naturalness) evaluations on the two duration models. The results show that CART models the training data more accurately than FDT. The FDT model, however, shows a better ability to extrapolate from the training data since it achieved a better accuracy for the test data set. Our qualitative evaluation results show that our FDT model produces synthesised speech that is perceived to be more natural than our CART model. In addition, we also observed that the expressiveness of FDT is much better than that of CART. That is because the representation in FDT is not restricted to a set of piece-wise or discrete constant approximation. We, therefore, conclude that the FDT approach is a practical approach for duration modelling in SY TTS applications. © 2006 Elsevier Ltd. All rights reserved.

Laser-induced breakdown spectroscopy quantitative analysis method via adaptive analytical line selection and relevance vector machine regression model

Abstract A new LIBS quantitative analysis method based on analytical line adaptive selection and Relevance Vector Machine (RVM) regression model is proposed. First, a scheme of adaptively selecting analytical line is put forward in order to overcome the drawback of high dependency on a priori knowledge. The candidate analytical lines are automatically selected based on the built-in characteristics of spectral lines, such as spectral intensity, wavelength and width at half height. The analytical lines which will be used as input variables of regression model are determined adaptively according to the samples for both training and testing. Second, an LIBS quantitative analysis method based on RVM is presented. The intensities of analytical lines and the elemental concentrations of certified standard samples are used to train the RVM regression model. The predicted elemental concentration analysis results will be given with a form of confidence interval of probabilistic distribution, which is helpful for evaluating the uncertainness contained in the measured spectra. Chromium concentration analysis experiments of 23 certified standard high-alloy steel samples have been carried out. The multiple correlation coefficient of the prediction was up to 98.85%, and the average relative error of the prediction was 4.01%. The experiment results showed that the proposed LIBS quantitative analysis method achieved better prediction accuracy and better modeling robustness compared with the methods based on partial least squares regression, artificial neural network and standard support vector machine.

A Two Layered Model for Evolving Web Resources

In this paper the key features of a two-layered model for describing the semantic of dynamical web resources are introduced. In the current Semantic Web proposal [Berners-Lee et al., 2001] web resources are classified into static ontologies which describes the semantic network of their inter-relationships [Kalianpur, 2001][Handschuh & Staab, 2002] and complex constraints described by logical quantified formula [Boley et al., 2001][McGuinnes & van Harmelen, 2004][McGuinnes et al., 2004], the basic idea is that software agents can use techniques of automatic reasoning in order to relate resources and to support sophisticated web application. On the other hand, web resources are also characterized by their dynamical aspects, which are not adequately addressed by current web models. Resources on the web are dynamical since, in the minimal case, they can appear or disappear from the web and their content is upgraded. In addition, resources can traverse different states, which characterized the resource life-cycle, each resource state corresponding to different possible uses of the resource. Finally most resources are timed, i.e. they information they provide make sense only if contextualised with respect to time, and their validity and accuracy is greatly bounded by time. Temporal projection and deduction based on dynamical and time constraints of the resources can be made and exploited by software agents [Hendler, 2001] in order to make previsions about the availability and the state of a resource, for deciding when consulting the resource itself or in order to deliberately induce a resource state change for reaching some agent goal, such as in the automated planning framework [Fikes & Nilsson, 1971][Bacchus & Kabanza,1998].