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Road infrastructure is necessary for any country to function as daily life depends upon it. Transport authorities monitor road infrastructure to identify distressed areas and repair them, enhancing users’ comfort and safety. Major steps have been taken in automating road monitoring using sensing- and vision-based technologies, reducing the level of subjectivity caused by traditional manual inspection processes. However, automated detection of distressed areas is often challenged by an insufficient amount of training data or uneven class balance within datasets. Data augmentation techniques offer the opportunity to enlarge the size and quality of existing datasets. This paper investigates how various augmentation techniques impact the performance of a set of representative detection algorithms applied to detecting highway potholes. The results suggest that: firstly, no specific augmentation seems to be more beneficial than the others in this domain; and second, using a moderate amount of augmentation techniques can provide performance gains. This work forms the basis for performance improvement of multiple detection algorithms, used for transport infrastructure management, and it consequently contributes to enhanced transport systems simulation and monitoring.

Due to exposure to the driving rain, water ingress can cause faults in electrical joints, junctions and distribution points in broadband lines. Over time, faulting behaviour may grow in magnitude eroding the electrical capability of these lines causing degradation of broadband service. Developing effective data-driven models for broadband line prognostics remains a challenge due to the limited failure data availability in the telecommunications industry. In order to address this problem, we present a technique for generating failure data that realistically reflect the behaviour of degrading broadband lines. To this end, we use the conditional generative adversarial network and more importantly, we control and direct the failure data generation process using expert knowledge on the water ingress failure cause. The proposed technique is evaluated using a real-world case study involving the time-to-failure prediction of two types of broadband lines in a south-west city in England. The prognostics performance is measured using the Kappa statistic and F-score. Benchmark performance is obtained using Random Oversampling, Synthetic Minority Oversampling and Adaptive Synthesis which can be used to oversample failure data by duplicating existing failure data or randomly generating data. Among these techniques, Random Oversampling achieved the best prognostics performance. It is shown that the proposed technique outperforms Random Oversampling technique by a large margin. More specifically, it increased the prognostics performance by 33% (Kappa statistic) and 25% (F-score) for Asymmetric Digital Subscriber Lines, and 17% (Kappa statistic) and 13% (F-score) for Very High Bitrate Digital Subscriber Lines compared to the Random Oversampling technique.

Markov chain Monte Carlo methods have revolutionized mathematical computation and enabled statistical inference within many previously intractable models. In this context, Hamiltonian dynamics have been proposed as an efficient way of building chains that can explore probability densities efficiently. The method emerges from physics and geometry, and these links have been extensively studied over the past thirty years. The aim of this review is to provide a comprehensive introduction to the geometric tools used in Hamiltonian Monte Carlo at a level accessible to statisticians, machine learners, and other users of the methodology with only a basic understanding of Monte Carlo methods. This will be complemented with some discussion of the most recent advances in the field, which we believe will become increasingly relevant to scientists.

The stochastic behavior of single ion channels is most often described as an aggregated continuous-time Markov process with discrete states. For ligand-gated channels each state can represent a different conformation of the channel protein or a different number of bound ligands. Single-channel recordings show only whether the channel is open or shut: states of equal conductance are aggregated, so transitions between them have to be inferred indirectly. The requirement to filter noise from the raw signal further complicates the modeling process, as it limits the time resolution of the data. The consequence of the reduced bandwidth is that openings or shuttings that are shorter than the resolution cannot be observed; these are known as missed events. Postulated models fitted using filtered data must therefore explicitly account for missed events to avoid bias in the estimation of rate parameters and therefore assess parameter identifiability accurately. In this article, we present the first, to our knowledge, Bayesian modeling of ion-channels with exact missed events correction. Bayesian analysis represents uncertain knowledge of the true value of model parameters by considering these parameters as random variables. This allows us to gain a full appreciation of parameter identifiability and uncertainty when estimating values for model parameters. However, Bayesian inference is particularly challenging in this context as the correction for missed events increases the computational complexity of the model likelihood. Nonetheless, we successfully implemented a two-step Markov chain Monte Carlo method that we called “BICME”, which performs Bayesian inference in models of realistic complexity. The method is demonstrated on synthetic and real single-channel data from muscle nicotinic acetylcholine channels. We show that parameter uncertainty can be characterized more accurately than with maximum-likelihood methods. Our code for performing inference in these ion channel models is publicly available.

Mechanistic models based on systems of nonlinear differential equations can help provide a quantitative understanding of complex physical or biological phenomena. The use of such models to describe nonlinear interactions in molecular biology has a long history; however, it is only recently that advances in computing have allowed these models to be set within a statistical framework, further increasing their usefulness and binding modelling and experimental approaches more tightly together. A probabilistic approach to modelling allows us to quantify uncertainty in both the model parameters and the model predictions, as well as in the model hypotheses themselves. In this paper, the Bayesian approach to statistical inference is adopted and we examine the significant challenges that arise when performing inference over nonlinear ordinary differential equation models describing cell signalling pathways and enzymatic circadian control; in particular, we address the difficulties arising owing to strong nonlinear correlation structures, high dimensionality and non-identifiability of parameters. We demonstrate how recently introduced differential geometric Markov chain Monte Carlo methodology alleviates many of these issues by making proposals based on local sensitivity information, which ultimately allows us to perform effective statistical analysis. Along the way, we highlight the deep link between the sensitivity analysis of such dynamic system models and the underlying Riemannian geometry of the induced posterior probability distributions.

The non-parametric deterministic Support Vector Machines (SVMs) produce high levels of performances in text classification. This article offers a much needed evaluation of the Gaussian Process (GP) classifier, as a non-parametric probabilistic analogue to SVMs, which has been rarely applied to text classification. We provide an extensive experimental comparison of the performance and properties of these competing classifiers on the challenging problem of protein interaction detection in biomedical publications. Our results show that GPs can match the performance of SVMs without the need for costly margin parameter tuning, whilst offering the advantage of an extendable probabilistic framework for text classification.

Because of its availability, ease of collection, and correlation with physiology and pathology, urine is an attractive source for clinical proteomics/peptidomics. However, the lack of comparable data sets from large cohorts has greatly hindered the development of clinical proteomics. Here, we report the establishment of a reproducible, high resolution method for peptidome analysis of naturally occurring human urinary peptides and proteins, ranging from 800 to 17,000 Da, using samples from 3,600 individuals analyzed by capillary electrophoresis coupled to MS. All processed data were deposited in an Structured Query Language (SQL) database. This database currently contains 5,010 relevant unique urinary peptides that serve as a pool of potential classifiers for diagnosis and monitoring of various diseases. As an example, by using this source of information, we were able to define urinary peptide biomarkers for chronic kidney diseases, allowing diagnosis of these diseases with high accuracy. Application of the chronic kidney disease-specific biomarker set to an independent test cohort in the subsequent replication phase resulted in 85.5% sensitivity and 100% specificity. These results indicate the potential usefulness of capillary electrophoresis coupled to MS for clinical applications in the analysis of naturally occurring urinary peptides.

Because of its availability, ease of collection, and correlation with physiology and pathology, urine is an attractive source for clinical proteomics/peptidomics. However, the lack of comparable data sets from large cohorts has greatly hindered the development of clinical proteomics. Here, we report the establishment of a reproducible, high resolution method for peptidome analysis of naturally occurring human urinary peptides and proteins, ranging from 800 to 17,000 Da, using samples from 3,600 individuals analyzed by capillary electrophoresis coupled to MS. All processed data were deposited in an Structured Query Language (SQL) database. This database currently contains 5,010 relevant unique urinary peptides that serve as a pool of potential classifiers for diagnosis and monitoring of various diseases. As an example, by using this source of information, we were able to define urinary peptide biomarkers for chronic kidney diseases, allowing diagnosis of these diseases with high accuracy. Application of the chronic kidney disease-specific biomarker set to an independent test cohort in the subsequent replication phase resulted in 85.5% sensitivity and 100% specificity. These results indicate the potential usefulness of capillary electrophoresis coupled to MS for clinical applications in the analysis of naturally occurring urinary peptides.

The problem of analysing dynamically evolving textual data has arisen within the last few years. An example of such data is the discussion appearing in Internet chat lines. In this Letter a recently introduced source separation method, termed as complexity pursuit, is applied to the problem of finding topics in dynamical text and is compared against several blind separation algorithms for the problem considered. Complexity pursuit is a generalisation of projection pursuit to time series and it is able to use both higher-order statistical measures and temporal dependency information in separating the topics. Experimental results on chat line and newsgroup data demonstrate that the minimum complexity time series indeed do correspond to meaningful topics inherent in the dynamical text data, and also suggest the applicability of the method to query-based retrieval from a temporally changing text stream.

This paper presents a probabilistic mixture modeling framework for the hierarchic organisation of document collections. It is demonstrated that the probabilistic corpus model which emerges from the automatic or unsupervised hierarchical organisation of a document collection can be further exploited to create a kernel which boosts the performance of state-of-the-art support vector machine document classifiers. It is shown that the performance of such a classifier is further enhanced when employing the kernel derived from an appropriate hierarchic mixture model used for partitioning a document corpus rather than the kernel associated with a flat non-hierarchic mixture model. This has important implications for document classification when a hierarchic ordering of topics exists. This can be considered as the effective combination of documents with no topic or class labels (unlabeled data), labeled documents, and prior domain knowledge (in the form of the known hierarchic structure), in providing enhanced document classification performance.

We propose a novel probabilistic method, based on latent variable models, for unsupervised topographic visualisation of dynamically evolving, coherenttextual information. This can be seen as a complementary tool for topic detection and tracking applications. This is achieved by the exploitation of the a priori domain knowledge available, that there are relatively homogeneous temporal segments in the data stream. In a different manner from topographical techniques previously utilized for static text collections, the topography is an outcome of the coherence in time of the data stream in the proposed model. Simulation results on both toy-data settings and an actual application on Internet chat line discussion analysis is presented by way of demonstration.

This paper presents a generalisation of the nonlinear 'Infomax' algorithm based on the linear latent variable model of factor analysis. The algorithm is based on an information theoretic index for projection pursuit which defines linear projections of observed data onto subspaces of lower dimension. This is applied to the visualisation and interpretation of complex high dimensional data and is empirically compared with the recently developed Generative Topographic Mapping.

A temporal variant of Foldiak's first model with lateral inhibitory synaptic weights is proposed. The usual symmetric scalar values of the lateral weights are replaced with data driven asymmetric memory based lateral weights, which take the form of Finite Impulse Response (FIR) coefficients. Linear anti-Hebbian learning, as defined by Foldiak (IEEE/INNS International Joint Conference on Neural Networks, 1989) and Matsuoka et al. (Neural Networks, Vol. 8, pp. 411–419, 1995), is employed in the self-organisation of the network weights. The temporal anti-Hebbian learning, when applied to the separation of convolved mixtures of signals, causes the network weights to converge to the truncated FIR filter coefficients of the unmixing transfer function and so recover the original signals. Simulation results are presented for separating two natural speech sources convolved and mixed by a priori unknown direct and cross-coupled transfer functions. We compare temporal anti-Hebbian learning with information maximisation learning when applied to the blind separation of convolved sources.

Adoption of digital information tools in the construction sector provides fertile ground for the birth and growth of companies that specialize in applications of technologies to design and construction. While some of the technologies are new, many implement ideas proposed in construction research decades ago that were impractical without a sound digital building information foundation. Building Information Modelling (BIM) itself can be traced to a landmark paper from 1975; ideas for artificially intelligent design and code checking tools date from the mid-1980s; and construction robots have laboured in research labs for decades. Yet only within the past five years has venture capital actively sought startup companies in the ‘Construction Tech’ sector. Following a set of digital construction innovations through their known past and their uncertain present, we review their increasingly optimistic future, all through the lens of their dependence on digital information. The review identifies new challenges, yielding a set of research topics with the potential to unlock a range of future applications that apply artificial intelligence.

Infectious diseases exert a large and in many contexts growing burden on human health, but violate most of the assumptions of classical epidemiological statistics and hence require a mathematically sophisticated approach. Viral shedding data are collected during human studies—either where volunteers are infected with a disease or where existing cases are recruited—in which the levels of live virus produced over time are measured. These have traditionally been difficult to analyse due to strong, complex correlations between parameters. Here, we show how a Bayesian approach to the inverse problem together with modern Markov chain Monte Carlo algorithms based on information geometry can overcome these difficulties and yield insights into the disease dynamics of two of the most prevalent human pathogens—influenza and norovirus—as well as Ebola virus disease.

Recommendations about structuring proteomic biomarker studies should increase the probability that such markers will be clinically useful.

Bayesian inference–based modeling identifies the most likely paths through a signaling network.

Motivation: There is currently much interest in reverse-engineering regulatory relationships between genes from microarray expression data. We propose a new algorithmic method for inferring such interactions between genes using data from gene knockout experiments. The algorithm we use is the Sparse Bayesian regression algorithm of Tipping and Faul. This method is highly suited to this problem as it does not require the data to be discretized, overcomes the need for an explicit topology search and, most importantly, requires no heuristic thresholding of the discovered connections. Results: Using simulated expression data, we are able to show that this algorithm outperforms a recently published correlation-based approach. Crucially, it does this without the need to set any ad hoc threshold on possible connections. Availability: Matlab code which allows all experimental results to be reproduced is available at http://www.dcs.gla.ac.uk/~srogers/reg_nets.html Contact: srogers@dcs.gla.ac.uk Supplementary information: Appendices and supplementary figures mentioned in the text can be found at http://www.dcs.gla.ac.uk/~srogers/reg_nets.html

Motivation: The identification of physiological processes underlying and generating the expression pattern observed in microarray experiments is a major challenge. Principal component analysis (PCA) is a linear multivariate statistical method that is regularly employed for that purpose as it provides a reduced-dimensional representation for subsequent study of possible biological processes responding to the particular experimental conditions. Making explicit the data assumptions underlying PCA highlights their lack of biological validity thus making biological interpretation of the principal components problematic. A microarray data representation which enables clear biological interpretation is a desirable analysis tool. Results: We address this issue by employing the probabilistic interpretation of PCA and proposing alternative linear factor models which are based on refined biological assumptions. A practical study on two well-understood microarray datasets highlights the weakness of PCA and the greater biological interpretability of the linear models we have developed. Availability: The model estimation routines are currently implemented as Matlab routines and these, as well as data and results reported, are available from the following URL: http://www.dcs.gla.ac.uk/~girolami/lfm/index.html