Adaptive estimation of hidden semi-Markov chains with parameterised transition probabilities and exponential decaying states

This paper develops maximum likelihood (ML) estimation schemes for finite-state semi-Markov chains in white Gaussian noise. We assume that the semi-Markov chain is characterised by transition probabilities of known parametric from with unknown parameters. We reformulate this hidden semi-Markov model (HSM) problem in the scalar case as a two-vector homogeneous hidden Markov model (HMM) problem in which the state consist of the signal augmented by the time to last transition. With this reformulation we apply the expectation Maximumisation (EM ) algorithm to obtain ML estimates of the transition probabilities parameters, Markov state levels and noise variance. To demonstrate our proposed schemes, motivated by neuro-biological applications, we use a damped sinusoidal parameterised function for the transition probabilities.

Reduced complexity on-line estimation of hidden Markov model parameters

In this paper we propose and study low complexity algorithms for on-line estimation of hidden Markov model (HMM) parameters. The estimates approach the true model parameters as the measurement noise approaches zero, but otherwise give improved estimates, albeit with bias. On a nite data set in the high noise case, the bias may not be signi cantly more severe than for a higher complexity asymptotically optimal scheme. Our algorithms require O(N3) calculations per time instant, where N is the number of states. Previous algorithms based on earlier hidden Markov model signal processing methods, including the expectation-maximumisation (EM) algorithm require O(N4) calculations per time instant.

Autonomous vehicle path planning for persistence monitoring under uncertainty using Gaussian based Markov decision process

One of the main challenges facing online and offline path planners is the uncertainty in the magnitude and direction of the environmental energy because it is dynamic, changeable with time, and hard to forecast. This thesis develops an artificial intelligence for a mobile robot to learn from historical or forecasted data of environmental energy available in the area of interest which will help for a persistence monitoring under uncertainty using the developed algorithm.

5,7-di-N-acetyl-acinetaminic acid: A novel non-2-ulosonic acid found in the capsule of an Acinetobacter baumannii isolate

An Acinetobacter baumannii global clone 1 (GC1) isolate was found to carry a novel capsule biosynthesis gene cluster, designated KL12. KL12 contains genes predicted to be involved in the synthesis of simple sugars, as well as ones for N-acetyl-l-fucosamine (l-FucpNAc) and N-acetyl-d-fucosamine (d-FucpNAc). It also contains a module of 10 genes, 6 of which are required for 5,7-di-N-acetyl-legionaminic acid synthesis. Analysis of the composition of the capsule revealed the presence of N-acetyl-d-galactosamine, l-FucpNAc and d-FucpNAc, confirming the role of fnlABC and fnr/gdr genes in the synthesis of l-FucpNAc and d-FucpNAc, respectively. A non-2-ulosonic acid, shown to be 5,7-diacetamido-3,5,7,9-tetradeoxy-l-glycero-l-altro-non-2-ulosonic acid, was also detected. This sugar has not previously been recovered from biological source, and was designated 5,7-di-N-acetyl-acinetaminic acid (Aci5Ac7Ac). Proteins encoded by novel genes, named aciABCD, were predicted to be involved in the conversion of 5,7-di-N-acetyl-legionaminic acid to Aci5Ac7Ac. A pathway for 5,7-di-N-acetyl-8-epilegionaminic acid biosynthesis was also proposed. In available A. baumannii genomes, genes for the synthesis of 5,7-di-N-acetyl-acinetaminic acid were only detected in two closely related capsule gene clusters, KL12 and KL13, which differ only in the wzy gene. KL12 and KL13 are carried by isolates belonging to clinically important clonal groups, GC1, GC2 and ST25. Genes for the synthesis of N-acyl derivatives of legionaminic acid were also found in 10 further A. baumannii capsule gene clusters, and three carried additional genes for production of 5,7-di-N-acetyl-8-epilegionaminic acid.

The sweet aroma of success: Modification of products for new markets in the case of TORQ by Di Bella Coffee

In 2002, Phillip Di Bella’s childhood passion for coffee and keen entrepreneurial spirit led him to establish a small coffee roasting warehouse in in the inner suburbs of Brisbane (Di Bella, 2012). With a keen sense of direction and passion for his coffee products and providing unparalleled customer service, Di Bella Coffee quickly grew to become a key player in the coffee roasting scene. This passion for the ultimate coffee experience is evident in the firm’s logo ‘Di Bella Coffee Inspires Passion’. Phillip Di Bella stated that ‘the common denominator of this company is about inspiration and passion. We are not a coffee company, we are a people company. You know, are we inspiring you from the moment you walk in the door to the moment you leave. If you are not feeling inspired then we haven’t done our job properly as a company’. Fundamentally, providing the ultimate coffee experience, as detailed in the following case is one in which focuses on the coffee consumption experience, not the coffee itself. Over that last 10 years Di Bella Coffee has constantly strived for the ultimate coffee, while expanding business operations into the booming Asian coffee market, establishing headquarters in Shanghai in 2010. In 2011, Di Bella Coffee commenced their second international venture with the launch of operations in India (Di Bella Coffee, 2012); followed shortly by the creation of a new category of coffee, set to revolutionise to coffee industry. The fusion of two traditional forms of coffee; espresso coffee and instant coffee, to create a third category- espresso instant, led to the development of TORQ by Di Bella.

Cross database training of audio-visual hidden Markov models for phone recognition

Speech recognition can be improved by using visual information in the form of lip movements of the speaker in addition to audio information. To date, state-of-the-art techniques for audio-visual speech recognition continue to use audio and visual data of the same database for training their models. In this paper, we present a new approach to make use of one modality of an external dataset in addition to a given audio-visual dataset. By so doing, it is possible to create more powerful models from other extensive audio-only databases and adapt them on our comparatively smaller multi-stream databases. Results show that the presented approach outperforms the widely adopted synchronous hidden Markov models (HMM) trained jointly on audio and visual data of a given audio-visual database for phone recognition by 29% relative. It also outperforms the external audio models trained on extensive external audio datasets and also internal audio models by 5.5% and 46% relative respectively. We also show that the proposed approach is beneficial in noisy environments where the audio source is affected by the environmental noise.

Crystal structure of poly[[di-mu2-aqua-aquasodium] 4-amino-3,5,6-trichloropyridine-2-carboxylate trihydrate], the sodium salt of the herbicide picloram

In the structure of the title complex [[Na(H2O)3]+ (C6H2Cl3N2O2)-^ . 3(H2O)]n, the Na salt of the herbicide picloram, the cation is a polymeric chain structure, based on doubly water-bridged NaO5 trigonal bipyramidal complex units which have in addition, a singly-bonded monodentate water molecule. Each of the bridges within the chain which lies along the a cell direction is centrosymmetric with Na...Na separations of 3.4807(16) and 3.5109(16)Ang. In the crystal, there are three water molecules of solvation and these, as well as the coordinated water molecules and the amino group of the 4-amino-3,5,6-trichloropicolinate anion are involved in extensive inter-species hydrogen-bonding interactions with carboxyl and water O-atoms as well as the pyridine N-atom. Among these association is a centrosymmetric cyclic tetra-water R4/4(8) ring , resulting in an overall three-dimensional structure.

Online hidden Markov model parameter estimation and minimax robust quickest change detection in uncertain stochastic processes

Stochastic (or random) processes are inherent to numerous fields of human endeavour including engineering, science, and business and finance. This thesis presents multiple novel methods for quickly detecting and estimating uncertainties in several important classes of stochastic processes. The significance of these novel methods is demonstrated by employing them to detect aircraft manoeuvres in video signals in the important application of autonomous mid-air collision avoidance.

Linear programming for large-scale Markov decision problems

We consider the problem of controlling a Markov decision process (MDP) with a large state space, so as to minimize average cost. Since it is intractable to compete with the optimal policy for large scale problems, we pursue the more modest goal of competing with a low-dimensional family of policies. We use the dual linear programming formulation of the MDP average cost problem, in which the variable is a stationary distribution over state-action pairs, and we consider a neighborhood of a low-dimensional subset of the set of stationary distributions (defined in terms of state-action features) as the comparison class. We propose a technique based on stochastic convex optimization and give bounds that show that the performance of our algorithm approaches the best achievable by any policy in the comparison class. Most importantly, this result depends on the size of the comparison class, but not on the size of the state space. Preliminary experiments show the effectiveness of the proposed algorithm in a queuing application.

Structure of the K12 capsule containing 5,7-di-N-acetylacinetaminic acid from Acinetobacter baumannii isolate D36

The repeat unit of the K12 capsular polysaccharide isolated from the Acinetobacter baumannii global clone 1 clinical isolate, D36, was elucidated by means of chemical and spectroscopical methods. The structure was shown to contain N-acetyl-D-galactosamine (D-GalpNAc), N-acetyl-D-fucosamine and N-acetyl-L-fucosamine linked together in the main chain, with the novel sugar, 5,7-diacetamido-3,5,7,9-tetradeoxy-L-glycero-L-altro-non-2-ulosonic acid (5,7-di-N-acetylacinetaminic acid or Aci5Ac7Ac), attached to D-GalpNAc as a side branch. This matched the sugar composition of the K12 capsule and the genetic content of the KL12 capsule gene cluster reported previously. D-FucpNAc was predicted to be the substrate for the initiating transferase, ItrB3, with the Wzy polymerase making a α-D-FucpNAc-(1 → 3)-D-GalpNAc linkage between the repeat units. The three glycosyltransferases encoded by KL12 are all retaining glycosyltransferases and were predicted to form specific linkages between the sugars in the K12 repeat unit.

Predicting a new phase (T′′) of two-dimensional transition metal di-chalcogenides and strain-controlled topological phase transition

Single layered transition metal dichalcogenides have attracted tremendous research interest due to their structural phase diversities. By using a global optimization approach, we have discovered a new phase of transition metal dichalcogenides (labelled as T′′), which is confirmed to be energetically, dynamically and kinetically stable by our first-principles calculations. The new T′′ MoS2 phase exhibits an intrinsic quantum spin Hall (QSH) effect with a nontrivial gap as large as 0.42 eV, suggesting that a two-dimensional (2D) topological insulator can be achieved at room temperature. Most interestingly, there is a topological phase transition simply driven by a small tensile strain of up to 2%. Furthermore, all the known MX2 (M = Mo or W; X = S, Se or Te) monolayers in the new T′′ phase unambiguously display similar band topologies and strain controlled topological phase transitions. Our findings greatly enrich the 2D families of transition metal dichalcogenides and offer a feasible way to control the electronic states of 2D topological insulators for the fabrication of high-speed spintronics devices.

The cost-effectiveness of the MobileMums intervention to increase physical activity among mothers with young children: A Markov model informed by a randomised controlled trial

Objectives: To determine the cost-effectiveness of the MobileMums intervention. MobileMums is a 12-week programme which assists mothers with young children to be more physically active, primarily through the use of personalised SMS text-messages. Design: A cost-effectiveness analysis using a Markov model to estimate and compare the costs and consequences of MobileMums and usual care. Setting: This study considers the cost-effectiveness of MobileMums in Queensland, Australia. Participants: A hypothetical cohort of over 36 000 women with a child under 1 year old is considered. These women are expected to be eligible and willing to participate in the intervention in Queensland, Australia. Data sources: The model was informed by the effectiveness results from a 9-month two-arm community-based randomised controlled trial undertaken in 2011 and registered retrospectively with the Australian Clinical Trials Registry (ACTRN12611000481976). Baseline characteristics for the model cohort, treatment effects and resource utilisation were all informed by this trial. Main outcome measures: The incremental cost per quality-adjusted life year (QALY) of MobileMums compared with usual care. Results: The intervention is estimated to lead to an increase of 131 QALYs for an additional cost to the health system of 1.1 million Australian dollars (AUD). The expected incremental cost-effectiveness ratio for MobileMums is 8608 AUD per QALY gained. MobileMums has a 98% probability of being cost-effective at a cost-effectiveness threshold of 64 000 AUD. Varying modelling assumptions has little effect on this result. Conclusions: At a cost-effectiveness threshold of 64 000 AUD, MobileMums would likely be a cost-effective use of healthcare resources in Queensland, Australia. Trial registration number: Australian Clinical Trials Registry; ACTRN12611000481976.