Bayesian inference of local government audit outcomes.

The scandals in publicly listed companies have highlighted the large losses that can result from financial statement fraud and weak corporate governance. Machine learning techniques have been applied to automatically detect financial statement fraud with great success. This work presents the first application of a Bayesian inference approach to the problem of predicting the audit outcomes of financial statements of local government entities using financial ratios. Bayesian logistic regression (BLR) with automatic relevance determination (BLR-ARD) is applied to predict audit outcomes. The benefit of using BLR-ARD, instead of BLR without ARD, is that it allows one to automatically determine which input features are the most relevant for the task at hand, which is a critical aspect to consider when designing decision support systems. This work presents the first implementation of BLR-ARD trained with Separable Shadow Hamiltonian Hybrid Monte Carlo, No-U-Turn sampler, Metropolis Adjusted Langevin Algorithm and Metropolis-Hasting algorithms. Unlike the Gibbs sampling procedure that is typically employed in sampling from ARD models, in this work we jointly sample the parameters and the hyperparameters by putting a log normal prior on the hyperparameters. The analysis also shows that the repairs and maintenance as a percentage of total assets ratio, current ratio, debt to total operating revenue, net operating surplus margin and capital cost to total operating expenditure ratio are the important features when predicting local government audit outcomes using financial ratios. These results could be of use for auditors as focusing on these ratios could potentially speed up the detection of fraudulent behaviour in municipal entities, and improve the speed and quality of the overall audit.

Quantum-Inspired Magnetic Hamiltonian Monte Carlo.

Hamiltonian Monte Carlo (HMC) is a Markov Chain Monte Carlo algorithm that is able to generate distant proposals via the use of Hamiltonian dynamics, which are able to incorporate first-order gradient information about the target posterior. This has driven its rise in popularity in the machine learning community in recent times. It has been shown that making use of the energy-time uncertainty relation from quantum mechanics, one can devise an extension to HMC by allowing the mass matrix to be random with a probability distribution instead of a fixed mass. Furthermore, Magnetic Hamiltonian Monte Carlo (MHMC) has been recently proposed as an extension to HMC and adds a magnetic field to HMC which results in non-canonical dynamics associated with the movement of a particle under a magnetic field. In this work, we utilise the non-canonical dynamics of MHMC while allowing the mass matrix to be random to create the Quantum-Inspired Magnetic Hamiltonian Monte Carlo (QIMHMC) algorithm, which is shown to converge to the correct steady state distribution. Empirical results on a broad class of target posterior distributions show that the proposed method produces better sampling performance than HMC, MHMC and HMC with a random mass matrix.

Bayesian inference of COVID-19 spreading rates in South Africa.

The Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has highlighted the need for performing accurate inference with limited data. Fundamental to the design of rapid state responses is the ability to perform epidemiological model parameter inference for localised trajectory predictions. In this work, we perform Bayesian parameter inference using Markov Chain Monte Carlo (MCMC) methods on the Susceptible-Infected-Recovered (SIR) and Susceptible-Exposed-Infected-Recovered (SEIR) epidemiological models with time-varying spreading rates for South Africa. The results find two change points in the spreading rate of COVID-19 in South Africa as inferred from the confirmed cases. The first change point coincides with state enactment of a travel ban and the resultant containment of imported infections. The second change point coincides with the start of a state-led mass screening and testing programme which has highlighted community-level disease spread that was not well represented in the initial largely traveller based and private laboratory dominated testing data. The results further suggest that due to the likely effect of the national lockdown, community level transmissions are slower than the original imported case driven spread of the disease.

System dynamics of active and passive postural changes: Insights from principal dynamic modes analysis of baroreflex loop.

The baroreflex being a key modulator of cardiovascular control ensures adequate blood pressure regulation under orthostatic stress which otherwise may cause severe hypotension. Contrary to conventional baroreflex sensitivity indices derived across a-priori traditional frequency bands, the present study is aimed at proposing new indices for the assessment of baroreflex drive which follows active (supine to stand-up) and passive (supine to head-up tilt) postural changes. To achieve this, a novel system identification approach of principal dynamic modes (PDM) was utilized to extract data-adaptive frequency components of closed-loop interactions between beat-to-beat interval and systolic blood pressure recorded from 10 healthy humans. We observed that the gain of low-pass global PDM of cardiac arm (:feedback reflex loop, mediated by pressure sensors to adjust heart rate in response to arterial blood pressure), and 0.2 Hz global PDM of mechanical arm (:feed-forward pathways, originating changes in arterial blood pressure in response to heart rate variations) may function as potential markers to distinguish active and passive orthostatic tests in healthy subjects.

A multivariate additive noise model for complete causal discovery.

Explaining causal reasoning in the form of directed acyclic graphs (DAGs) yields nodal structures with multivariate relationships. In real-world phenomena, these effects can be seen as multiple feature dependency with unmeasured external influences or noises. The bivariate models for causal discovery simply miss to find the multiple feature dependency criteria in the causal models. Here, we propose a multivariate additive noise model (MANM) to solve these issues while analyzing and presenting a multi-nodal causal structure. We introduce new criteria of causal independence for qualitative analysis of causal models and causal influence factor (CIF) for the successful discovery of causal directions in the multivariate system. The scores of CIF provide the information for the goodness of casual inference. The identifiability of the proposed model to discover linear, non-linear causal relations is verified in simulated, real-world datasets and the ability to construct the complete causal model. In comparison test, MANM has out performed Independent Component Analysis based Linear Non-Gaussian Acyclic Model (ICA-LiNGAM), Greedy DAG Search (GDS) and Regression with Sub-sequent Independent Test (RESIT), and performed better for Gaussian and non-Gaussian mixture models with both correlated and uncorrelated feature relations. In performance test, different model fitting errors which occur during causal model construction are discussed and the performance of MANM in comparison to ICA-LiNGAM, GDS and RESIT is provided. Results show that MANM has better causal model construction ability, producing few extra sets of direction with no missing or wrong directions and can estimate every possible causal direction in complex feature sets.

Caller behaviour classification using computational intelligence methods.

A classification system that accurately categorizes caller interaction within Interactive Voice Response systems is essential in determining caller behaviour. Field and call performance classifier for pay beneficiary application are developed. Genetic Algorithms, Multi-Layer Perceptron neural network, Radial Basis Function neural network, Fuzzy Inference Systems and Support Vector Machine computational intelligent techniques were considered in this research. Exceptional results were achieved. Classifiers with accuracy values greater than 90% were developed. The preferred models for field 'Say amount', 'Say confirmation' and call performance classification are the ensemble of classifiers. However, the Multi-Layer Perceptron classifiers performed the best in field 'Say account' and 'Select beneficiary' classification.

High transparency coded apertures in planar nuclear medicine imaging.

Coded apertures provide an alternative to the collimators of nuclear medicine imaging, and advances in the field have lessened the artifacts that are associated with the near-field geometry. Thickness of the aperture material, however, results in a decoded image with thickness artifacts, and constrains both image resolution and the available manufacturing techniques. Thus in theory, thin apertures are clearly desirable, but high transparency leads to a loss of contrast in the recorded data. Coupled with the quantization effects of detectors, this leads to significant noise in the decoded image. This noise must be dependent on the bit-depth of the gamma camera. If there are a sufficient number of measurable values, high transparency need not adversely affect the signal-to-noise ratio. This novel hypothesis is tested by means of a ray-tracing computer simulator. The simulation results presented in the paper show that replacing a highly opaque coded aperture with a highly transparent aperture, simulated with an 8-bit gamma camera, worsens the root-mean-square error measurement. However, when simulated with a 16-bit gamma camera, a highly transparent coded aperture significantly reduces both thickness artifacts and the root-mean-square error measurement.