Shear dispersion along circular pipes is affected by bends, but the torsion of the pipe is negligible

The flow of a viscous fluid along a curving pipe of fixed radius is driven by a pressure gradient. For a generally curving pipe it is the fluid flux which is constant along the pipe and so I correct fluid flow solutions of Dean (1928) and Topakoglu (1967) which assume constant pressure gradient. When the pipe is straight, the fluid adopts the parabolic velocity profile of Poiseuille flow; the spread of any contaminant along the pipe is then described by the shear dispersion model of Taylor (1954) and its refinements by Mercer, Watt et al (1994,1996). However, two conflicting effects occur in a generally curving pipe: viscosity skews the velocity profile which enhances the shear dispersion; whereas in faster flow centrifugal effects establish secondary flows that reduce the shear dispersion. The two opposing effects cancel at a Reynolds number of about 15. Interestingly, the torsion of the pipe seems to have very little effect upon the flow or the dispersion, the curvature is by far the dominant influence. Lastly, curvature and torsion in the fluid flow significantly enhance the upstream tails of concentration profiles in qualitative agreement with observations of dispersion in river flow

Correcting Experience Replay for Multi-Agent Communication

We consider the problem of learning to communicate using multi-agent reinforcement learning (MARL). A common approach is to learn off-policy, using data sampled from a replay buffer. However, messages received in the past may not accurately reflect the current communication policy of each agent, and this complicates learning. We therefore introduce a 'communication correction' which accounts for the non-stationarity of observed communication induced by multi-agent learning. It works by relabelling the received message to make it likely under the communicator's current policy, and thus be a better reflection of the receiver's current environment. To account for cases in which agents are both senders and receivers, we introduce an ordered relabelling scheme. Our correction is computationally efficient and can be integrated with a range of off-policy algorithms. It substantially improves the ability of communicating MARL systems to learn across a variety of cooperative and competitive tasks

Polynomial matrix reduction to linearised form

In many aspects of system analysis it is required to consider a set of equations in order to infer the behaviour or, more simply, properties of the system. In many cases these equations will be complex and consequently difficult to analyse. It would be useful therefore from the analysis point of view if a similar but equivalent set of equations describing the system's behaviour could be found. [Continues.

Automatic phased mission system reliability model generation

There are many methods for modelling the reliability of systems based on component failure data. This task becomes more complex as systems increase in size, or undertake missions that comprise multiple discrete modes of operation, or phases. Existing techniques require certain levels of expertise in the model generation and calculation processes, meaning that risk and reliability assessments of systems can often be expensive and time-consuming. This is exacerbated as system complexity increases. This thesis presents a novel method which generates reliability models for phasedmission systems, based on Petri nets, from simple input files. The process has been automated with a piece of software designed for engineers with little or no experience in the field of risk and reliability. The software can generate models for both repairable and non-repairable systems, allowing redundant components and maintenance cycles to be included in the model. Further, the software includes a simulator for the generated models. This allows a user with simple input files to perform automatic model generation and simulation with a single piece of software, yielding detailed failure data on components, phases, missions and the overall system. A system can also be simulated across multiple consecutive missions. To assess performance, the software is compared with an analytical approach and found to match within 5% in both the repairable and non-repairable cases. The software documented in this thesis could serve as an aid to engineers designing new systems to validate the reliability of the system. This would not require specialist consultants or additional software, ensuring that the analysis provides results in a timely and cost-effective manner

Aspects classificatoires des systèmes à objets

Colloque avec actes et comité de lecture.Cet article cherche à mettre en évidence les divers aspects de la classification dans la variété des systèmes à objets. Pour cela, la classification est examinée en tant que processus de formation et d'organisation de classes, mais aussi en tant que processus de raisonnement, pour des besoins de reconnaissance par exemple. Il est montré que l'approche objet apporte un point de vue particulier et des interrogations nouvelles et complexes dans le domaine de la classification

Robust learning of acoustic representations from diverse speech data

Automatic speech recognition is increasingly applied to new domains. A key challenge is to robustly learn, update and maintain representations to cope with transient acoustic conditions. A typical example is broadcast media, for which speakers and environments may change rapidly, and available supervision may be poor. The concern of this thesis is to build and investigate methods for acoustic modelling that are robust to the characteristics and transient conditions as embodied by such media. The first contribution of the thesis is a technique to make use of inaccurate transcriptions as supervision for acoustic model training. There is an abundance of audio with approximate labels, but training methods can be sensitive to label errors, and their use is therefore not trivial. State-of-the-art semi-supervised training makes effective use of a lattice of supervision, inherently encoding uncertainty in the labels to avoid overfitting to poor supervision, but does not make use of the transcriptions. Existing approaches that do aim to make use of the transcriptions typically employ an algorithm to filter or combine the transcriptions with the recognition output from a seed model, but the final result does not encode uncertainty. We propose a method to combine the lattice output from a biased recognition pass with the transcripts, crucially preserving uncertainty in the lattice where appropriate. This substantially reduces the word error rate on a broadcast task. The second contribution is a method to factorise representations for speakers and environments so that they may be combined in novel combinations. In realistic scenarios, the speaker or environment transform at test time might be unknown, or there may be insufficient data to learn a joint transform. We show that in such cases, factorised, or independent, representations are required to avoid deteriorating performance. Using i-vectors, we factorise speaker or environment information using multi-condition training with neural networks. Specifically, we extract bottleneck features from networks trained to classify either speakers or environments. The resulting factorised representations prove beneficial when one factor is missing at test time, or when all factors are seen, but not in the desired combination. The third contribution is an investigation of model adaptation in a longitudinal setting. In this scenario, we repeatedly adapt a model to new data, with the constraint that previous data becomes unavailable. We first demonstrate the effect of such a constraint, and show that using a cyclical learning rate may help. We then observe that these successive models lend themselves well to ensembling. Finally, we show that the impact of this constraint in an active learning setting may be detrimental to performance, and suggest to combine active learning with semi-supervised training to avoid biasing the model. The fourth contribution is a method to adapt low-level features in a parameter-efficient and interpretable manner. We propose to adapt the filters in a neural feature extractor, known as SincNet. In contrast to traditional techniques that warp the filterbank frequencies in standard feature extraction, adapting SincNet parameters is more flexible and more readily optimised, whilst maintaining interpretability. On a task adapting from adult to child speech, we show that this layer is well suited for adaptation and is very effective with respect to the small number of adapted parameters

Langages et modèles à objets - état des recherches et perspectives

Langages et modèles à objets donne un aperçu de la diversité des travaux développés autour de la notion d'objet, à un moment où leur incidence est de plus en plus grande dans tous les domaines de l'informatique. l'ouvrage est divisé en quatre parties, centrées autour de thèmes choisis : génie logiciel, concepts avancés, représentation des connaissances et applications. Tous les chapitres (sauf un) ont été spécialement conçus pour l'occasion et leurs auteurs ont été choisis parmi les meilleurs spécialistes français. l'ouvrage peut être lu sans la connaissance préalable d'un langage particulier et ne donne pas une connaissance approfondie d'un langage ou de son histoire. l'accent est plutôt mis sur les grands principes des thèmes traités, qui sont présentés en détail et caractérisés les uns par rapport aux autres

Cadre conceptuel pour la composition des objets et la spécification du comportement

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal