Proceedings of The Tenth International Workshop on Ontology Matching (OM-2015)

shvaiko2016aInternational audienceno abstrac

Uncertainty in Neural Networks: Approximately Bayesian Ensembling

Understanding the uncertainty of a neural network's (NN) predictions is essential for many purposes. The Bayesian framework provides a principled approach to this, however applying it to NNs is challenging due to large numbers of parameters and data. Ensembling NNs provides an easily implementable, scalable method for uncertainty quantification, however, it has been criticised for not being Bayesian. This work proposes one modification to the usual process that we argue does result in approximate Bayesian inference; regularising parameters about values drawn from a distribution which can be set equal to the prior. A theoretical analysis of the procedure in a simplified setting suggests the recovered posterior is centred correctly but tends to have an underestimated marginal variance, and overestimated correlation. However, two conditions can lead to exact recovery. We argue that these conditions are partially present in NNs. Empirical evaluations demonstrate it has an advantage over standard ensembling, and is competitive with variational methods.The lead author was funded through EPSRC (EP/N509620/1) and partially accommodated by the Alan Turing Institute

Uncertainty in Neural Networks; Bayesian Ensembles, Priors & Prediction Intervals

The breakout success of deep neural networks (NNs) in the 2010's marked a new era in the quest to build artificial intelligence (AI). With NNs as the building block of these systems, excellent performance has been achieved on narrow, well-defined tasks where large amounts of data are available. However, these systems lack certain capabilities that are important for broad use in real-world applications. One such capability is the communication of uncertainty in a NN's predictions and decisions. In applications such as healthcare recommendation or heavy machinery prognostics, it is vital that AI systems be aware of and express their uncertainty – this creates safer, more cautious, and ultimately more useful systems. This thesis explores how to engineer NNs to communicate robust uncertainty estimates on their predictions, whilst minimising the impact on usability. One way to encourage uncertainty estimates to be robust is to adopt the Bayesian framework, which offers a principled approach to handling uncertainty. Two of the major contributions in this thesis relate to Bayesian NNs (BNNs). Specifying appropriate priors is an important step in any Bayesian model, yet it is not clear how to do this in BNNs. The first contribution shows that the connection between BNNs and Gaussian Processes (GPs) provides an effective lens to study BNN priors. NN architectures are derived which mirror the combining of GP kernels to create priors tailored to a task. The second major contribution is a novel way to perform approximate Bayesian inference in BNNs using a modified version of ensembling. Novel analysis improves an understanding of a technique known as randomised MAP sampling. It's shown this is particularly effective when strong correlations exist between parameters, making it well suited to NNs. The third major contribution of the thesis is a non-Bayesian technique that trains a NN to directly output prediction intervals for regression tasks through a tailored objective function. This advances over related works that were incompatible with gradient descent, and ignored one source of uncertainty.EPSRC, Alan Turing Institut

A Multivariate Framework for Variable Selection and Identification of Biomarkers in High-Dimensional Omics Data

In this thesis, we address the identification of biomarkers in high-dimensional omics data. The identification of valid biomarkers is especially relevant for personalized medicine that depends on accurate prediction rules. Moreover, biomarkers elucidate the provenance of disease, or molecular changes related to disease. From a statistical point of view the identification of biomarkers is best cast as variable selection. In particular, we refer to variables as the molecular attributes under investigation, e.g. genes, genetic variation, or metabolites; and we refer to observations as the specific samples whose attributes we investigate, e.g. patients and controls. Variable selection in high-dimensional omics data is a complicated challenge due to the characteristic structure of omics data. For one, omics data is high-dimensional, comprising cellular information in unprecedented details. Moreover, there is an intricate correlation structure among the variables due to e.g internal cellular regulation, or external, latent factors. Variable selection for uncorrelated data is well established. In contrast, there is no consensus on how to approach variable selection under correlation. Here, we introduce a multivariate framework for variable selection that explicitly accounts for the correlation among markers. In particular, we present two novel quantities for variable importance: the correlation-adjusted t (CAT) score for classification, and the correlation-adjusted (marginal) correlation (CAR) score for regression. The CAT score is defined as the Mahalanobis-decorrelated t-score vector, and the CAR score as the Mahalanobis-decorrelated correlation between the predictor variables and the outcome. We derive the CAT and CAR score from a predictive point of view in linear discriminant analysis and regression; both quantities assess the weight of a decorrelated and standardized variable on the prediction rule. Furthermore, we discuss properties of both scores and relations to established quantities. Above all, the CAT score decomposes Hotelling’s T 2 and the CAR score the proportion of variance explained. Notably, the decomposition of total variance into explained and unexplained variance in the linear model can be rewritten in terms of CAR scores. To render our approach applicable on high-dimensional omics data we devise an efficient algorithm for shrinkage estimates of the CAT and CAR score. Subsequently, we conduct extensive simulation studies to investigate the performance of our novel approaches in ranking and prediction under correlation. Here, CAT and CAR scores consistently improve over marginal approaches in terms of more true positives selected and a lower model error. Finally, we illustrate the application of CAT and CAR score on real omics data. In particular, we analyze genomics, transcriptomics, and metabolomics data. We ascertain that CAT and CAR score are competitive or outperform state of the art techniques in terms of true positives detected and prediction error

Sviluppo di metodologie per la valutazione della freschezza del pesce mediante applicazioni metabonomiche.

This study focuses on the use of metabonomics applications in measuring fish freshness in various biological species and in evaluating how they are stored. This metabonomic approach is innovative and is based upon molecular profiling through nuclear magnetic resonance (NMR). On one hand, the aim is to ascertain if a type of fish has maintained, within certain limits, its sensory and nutritional characteristics after being caught; and on the second, the research observes the alterations in the product’s composition. The spectroscopic data obtained through experimental nuclear magnetic resonance, 1H-NMR, of the molecular profiles of the fish extracts are compared with those obtained on the same samples through analytical and conventional methods now in practice. These second methods are used to obtain chemical indices of freshness through biochemical and microbial degradation of the proteic nitrogen compounds and not (trimethylamine, N-(CH3)3, nucleotides, amino acids, etc.). At a later time, a principal components analysis (PCA) and a linear discriminant analysis (PLS-DA) are performed through a metabonomic approach to condense the temporal evolution of freshness into a single parameter. In particular, the first principal component (PC1) under both storage conditions (4 °C and 0 °C) represents the component together with the molecular composition of the samples (through 1H-NMR spectrum) evolving during storage with a very high variance. The results of this study give scientific evidence supporting the objective elements evaluating the freshness of fish products showing those which can be labeled “fresh fish.”Il presente studio è centrato all’utilizzo di applicazioni metabonomiche, finalizzate alla misura della freschezza di prodotti ittici in funzione della specie biologica e della modalità di conservazione. Questo approccio metabonomico nello studio in esame è innovativo e si basa sulla profilazione molecolare mediante la risonanza magnetica nucleare (NMR), per valutare da una parte se una tipologia di pesce ha ancora mantenuto, entro certi limiti, le proprie caratteristiche sensoriali e nutrizionali presenti al tempo iniziale e dall’altra per osservare le eventuali alterazioni che intervengono nella composizione del prodotto ittico. I dati spettroscopici sperimentali ottenuti attraverso la risonanza magnetica nucleare, 1H-NMR, dei profili molecolari di estratti di pesce preparati in modo opportuno sono stati confrontati con quelli ottenuti sugli stessi campioni attraverso metodiche analitiche strumentali classiche e convenzionali, a cui le metodologie ufficiali fanno riferimento. Quest’ultime vengono utilizzate per l’ottenimento di indici chimici di freschezza derivanti dalla degradazione biochimica e microbica di composti azotati proteici e non (trimetilammina ,N-(CH3)3, nucleotidi, amminoacidi, ecc.). In un secondo momento mediante un approccio metabonomico è stata eseguita un’analisi delle componenti principali (PCA) e un’analisi discriminante lineare (PLS-DA) al fine di condensare il concetto di evoluzione temporale della freschezza in un parametro omnicomprensivo. In particolare, la prima componente principale (PC1) in entrambe le condizioni di conservazione (4 °C e 0 °C) rappresenta la componente lungo la quale la composizione molecolare dei campioni, descritta dallo spettro1H-NMR, evolve durante il tempo di conservazione con una varianza molto elevata. I risultati di questo studio vogliono mirare ad ottenere un supporto scientifico che sia in grado di fornire elementi oggettivi di valutazione, per far si che il prodotto ittico possa fregiarsi della denominazione di “pesce fresco”

Dynamic process modelling for business engineering and information systems evaluation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This research is concerned with the pre-implementation evaluation of investments in Information Systems (IS). IS evaluation is important as organisations need to assess the financial justifiability of business change proposals that include (but usually are not limited to) the introduction of IS applications. More specifically, this research addresses the problem of benefits assessment within IS evaluation. We contend that benefits assessment should not be performed at the level of the IS application, as most extant evaluation methods advocate. Instead, to study the dynamics and the interactions of the IS applications with their surrounding environment, we propose to adopt the business process as the analytic lens of evaluation and to assess the impacts of IS on organisational, rather than on technical, performance indicators. Drawing on these propositions, this research investigates the potential of dynamic process modelling (via discrete-event simulation) as a facilitator of IS evaluation. We argue that, in order to be effective evaluation tools, business process models should be able to explicitly incorporate the effects of IS introduction on business performance, an issue that is found to be under-researched in previous literature. The above findings serve as the central theme for the development of a design theory of IS evaluation by simulation. The theory provides prescriptive elements that refer both to the design products of the evaluation and the design process by which these products can come into reality. The theory draws on a set of kernel theories from the business engineering domain and proposes a set of meta-requirements that should be satisfied by business process models, a meta-design structure that meets these requirements, and a design method that provides guidance in applying the theoretical propositions in practice. The design theory is developed and empirically tested by means of two real-life case studies. The first study is used to complement the findings of a literature review and to drive the development of the design theory's components, while the second study is employed to validate and further enhance the theory's propositions. The research results support the arguments for simulation-assisted IS evaluation and demonstrate the contribution of the design theory to the field

Parametric classification in domains of characters, numerals, punctuation, typefaces and image qualities

This thesis contributes to the Optical Font Recognition problem (OFR), by developing a classifier system to differentiate ten typefaces using a single English character ‘e’. First, features which need to be used in the classifier system are carefully selected after a thorough typographical study of global font features and previous related experiments. These features have been modeled by multivariate normal laws in order to use parameter estimation in learning. Then, the classifier system is built up on six independent schemes, each performing typeface classification using a different method. The results have shown a remarkable performance in the field of font recognition. Finally, the classifiers have been implemented on Lowercase characters, Uppercase characters, Digits, Punctuation and also on Degraded Images

Adaptation of bacteriophage to variable environments

Because a virus is an obligate cellular parasite, the host is a key part of its environment. Viruses may expand their ecological niche by switching host. Successful host switching can be influenced by ecological and evolutionary factors, genetic constraints and fitness within new hosts. An outcome of host switching is reduced fitness exhibited by viruses, a phenomenon observed in the evolution of viral disease emergence and resistance. To understand the genetic basis of this cost, investigations are required at the genotypic and phenotypic level. A host switching paradigm was developed using the model bacteriophage φX174 which was propagated with its laboratory bacterial host Escherichia coli C and with the novel host Salmonella enterica serovar Typhimurium, LT2 strain IJ750 or Escherichia coli K-12 mutant strain JWO196-2 designated as E. coli K-12gmhB-mut. A chemostat was used to achieve steady-state conditions for propagation of φX174 and bacterial cells. Two experiments were performed using this approach. In the first, φX174 was cultured on E. coli K-12gmhB-mut for 3 days (~206 generations). In the second experiment, φX174 was cultured on E. coli C and S. Typhimurium for four consecutive periods of 10 days (~720 generations), alternating between the two hosts. For the second chemostat experiment, the fitness and attachment rates of each viral population were measured using qPCR in liquid culture in order to identify and characterise fitness costs associated with host-switching. Deep sequencing of chemostat samples was also carried out to identify allelic changes occurring before and after host switches. Viral samples were chosen to capture substitutions associated with each host across the experiment (which might explain observed changes in fitness) and time series were picked to identify the dynamics of adaptation on a new host. Bacterial host strains were not sampled in this study. The phenotype measures indicated the pleiotropic costs of host switching, that is a reduction in phage fitness was observed when this was tested on the host used prior to switching, and this may be explained by changes in the attachment rate. The genotype data revealed sets of changes that could be identified as signatures of adaptation to each host, although control data indicate that these may arise during DNA preparation, implicating synthesis of replicative form DNA in the host as a source of selective constraint. Some host-specific alleles and some shared alleles were identified and their fitness effects were examined in isolation after reconstruction of these alleles in the ancestor via targeted mutagenesis. The fitness effects observed for reconstructed mutants were in the direction expected although they do not fully account for the observed costs of host switching. By analysing different phenotypes and genotypes produced during evolution, a detailed view of φX174’s adaptation to different hosts was obtained. The results support the idea that costs associated with pathogen-host adaptation may be host-specific, associated with specific mutations, acquired early and persist. Examining these is relevant for understanding emerging infectious diseases