Numerical study of the steady/unsteady multibody interaction in ship propulsion systems

The goal of this research is to reduce the computational cost of a fully unsteady RANS simulation for the multibody interaction problems in the ship propulsion system. To achieve this, the boundary element method (BEM) can be coupled with a RANS solver. The rapid-changing propeller-induced flow is first decoupled from the slow-changing or steady total flow. While RANS can be used to calculate the total flow, BEM is applied to the propeller-induced flow. By representing the propeller blades by a body force field and a mass source field, it becomes possible for RANS to use a larger time step size (or even run as a steady problem) and a smaller number of cells. The use of BEM to handle the propeller-induced flow improves the numerical efficiency and also provides a framework for sheet cavitation predictions. Depending on the level of simplifications, the coupled BEM/RANS scheme can be implemented by three different approaches: the unsteady approach, the time-averaged non-axisymmetric approach, and the time-averaged axisymmetric approach. All of the three approaches are described in this dissertation, as well as some numerical studies on different body force distribution models, mass source models, effective wake calculation models, etc. Then, the scheme is validated by several simple cases in which the propeller’s interaction with upstream bodies is not considered. Finally, the scheme is applied to a hull-propeller-rudder interaction problem and a contra-rotating propeller problem.Civil, Architectural, and Environmental Engineerin

Serological Prevalence of Schistosoma japonicum in Mobile Populations in Previously Endemic but Now Non-Endemic Regions of China: A Systematic Review and Meta-Analysis.

Background: Schistosomiasis japonica has been resurging in certain areas of China where its transmission was previously well controlled or interrupted. Several factors may be contributing to this, including mobile populations, which if infected, may spread the disease. A wide range of estimates have been published for S. japonicum infections in mobile populations, and a synthesis of these data will elucidate the relative risk presented from these groups. Methods: A literature search for publications up to Oct 31, 2014 on S. japonicum infection in mobile populations in previously endemic but now non-endemic regions was conducted using four bibliographic databases: China National Knowledge Infrastructure, WanFang, VIP Chinese Journal Databases, and PubMed. A meta-analysis was conducted by pooling one arm binary data with MetaAnalyst Beta 3.13. The protocol is available on PROSPERO (No. CRD42013005967). Results: A total of 41 studies in Chinese met the inclusion criteria, covering seven provinces of China. The time of post-interruption surveillance ranged from the first year to the 31st year. After employing a random-effects model, from 1992 to 2013 the pooled seroprevalence ranged from 0.9% (95% CI: 0.5-1.6%) in 2003 to 2.3% (95% CI: 1.5-3.4) in 1995; from the first year after the disease had been interrupted to the 31st year, the pooled seroprevalence ranged from 0.6% (95% CI: 0.2-2.1%) in the 27th year to 4.0% (95%CI: 1.3-11.3%) in the second year. The pooled seroprevalence in mobile populations each year was significantly lower than among the residents of endemic regions, whilst four papers reported a lower level of infection in the mobile populations than in the local residents out of only 13 papers which included this data. Conclusions: The re-emergence of S. japonicum in areas which had previously interrupted transmission might be due to other factors, although risk from re-introduction from mobile populations could not be excluded

Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking

The natural language generation (NLG) component of a spoken dialogue system (SDS) usually needs a substantial amount of handcrafting or a well-labeled dataset to be trained on. These limitations add significantly to development costs and make cross-domain, multi-lingual dialogue systems intractable. Moreover, human languages are context-aware. The most natural response should be directly learned from data rather than depending on predefined syntaxes or rules. This paper presents a statistical language generator based on a joint recurrent and convolutional neural network structure which can be trained on dialogue act-utterance pairs without any semantic alignments or predefined grammar trees. Objective metrics suggest that this new model outperforms previous methods under the same experimental conditions. Results of an evaluation by human judges indicate that it produces not only high quality but linguistically varied utterances which are preferred compared to n-gram and rule-based systems.Comment: To be appear in SigDial 201

Band Structure and Quantum Conductance of Nanostructures from Maximally-Localized Wannier Functions: The Case of Functionalized Carbon Nanotubes

We have combined large-scale, $\Gamma$-point electronic-structure calculations with the maximally-localized Wannier functions approach to calculate efficiently the band structure and the quantum conductance of complex systems containing thousands of atoms while maintaining full first-principles accuracy. We have applied this approach to study covalent functionalizations in metallic single-walled carbon nanotubes. We find that the band structure around the Fermi energy is much less dependent on the chemical nature of the ligands than on the $sp^3$ functionalization pattern disrupting the conjugation network. Common aryl functionalizations are more stable when paired with saturating hydrogens; even when paired, they still act as strong scattering centers that degrade the ballistic conductance of the nanotubes already at low degrees of coverage.Comment: To be published in Phys. Rev. Let

Sample Efficient Deep Reinforcement Learning for Dialogue Systems with Large Action Spaces

In spoken dialogue systems, we aim to deploy artificial intelligence to build automated dialogue agents that can converse with humans. A part of this effort is the policy optimisation task, which attempts to find a policy describing how to respond to humans, in the form of a function taking the current state of the dialogue and returning the response of the system. In this paper, we investigate deep reinforcement learning approaches to solve this problem. Particular attention is given to actor-critic methods, off-policy reinforcement learning with experience replay, and various methods aimed at reducing the bias and variance of estimators. When combined, these methods result in the previously proposed ACER algorithm that gave competitive results in gaming environments. These environments however are fully observable and have a relatively small action set so in this paper we examine the application of ACER to dialogue policy optimisation. We show that this method beats the current state-of-the-art in deep learning approaches for spoken dialogue systems. This not only leads to a more sample efficient algorithm that can train faster, but also allows us to apply the algorithm in more difficult environments than before. We thus experiment with learning in a very large action space, which has two orders of magnitude more actions than previously considered. We find that ACER trains significantly faster than the current state-of-the-art.Toshiba Research Europe Ltd, Cambridge Research Laboratory - RG85875 EPSRC Research Council - RG8079

Policy committee for adaptation in multi-domain spoken dialogue systems

Moving from limited-domain dialogue systems to open domain dialogue systems raises a number of challenges. One of them is the ability of the system to utilise small amounts of data from disparate domains to build a dialogue manager policy. Previous work has focused on using data from different domains to adapt a generic policy to a specific domain. Inspired by Bayesian committee machines, this paper proposes the use of a committee of dialogue policies. The results show that such a model is particularly beneficial for adaptation in multi-domain dialogue systems. The use of this model significantly improves performance compared to a single policy baseline, as confirmed by the performed real-user trial. This is the first time a dialogue policy has been trained on multiple domains on-line in interaction with real users.The research leading to this work was funded by the EPSRC grant EP/M018946/1 ”Open Domain Statistical Spoken Dialogue Systems”.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/ASRU.2015.740487

Policy committee for adaptation in multi-domain spoken dialogue systems

Moving from limited-domain dialogue systems to open domain dialogue systems raises a number of challenges. One of them is the ability of the system to utilise small amounts of data from disparate domains to build a dialogue manager policy. Previous work has focused on using data from different domains to adapt a generic policy to a specific domain. Inspired by Bayesian committee machines, this paper proposes the use of a committee of dialogue policies. The results show that such a model is particularly beneficial for adaptation in multi-domain dialogue systems. The use of this model significantly improves performance compared to a single policy baseline, as confirmed by the performed real-user trial. This is the first time a dialogue policy has been trained on multiple domains on-line in interaction with real users.The research leading to this work was funded by the EPSRC grant EP/M018946/1 ”Open Domain Statistical Spoken Dialogue Systems”.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/ASRU.2015.740487

Multi-domain neural network language generation for spoken dialogue systems

Moving from limited-domain natural language generation (NLG) to open domain is difficult because the number of semantic input combinations grows exponentially with the number of domains. Therefore, it is important to leverage existing resources and exploit similarities between domains to facilitate domain adaptation. In this paper, we propose a procedure to train multi-domain, Recurrent Neural Network-based (RNN) language generators via multiple adaptation steps. In this procedure, a model is first trained on counterfeited data synthesised from an out-of-domain dataset, and then fine tuned on a small set of in-domain utterances with a discriminative objective function. Corpus-based evaluation results show that the proposed procedure can achieve competitive performance in terms of BLEU score and slot error rate while significantly reducing the data needed to train generators in new, unseen domains. In subjective testing, human judges confirm that the procedure greatly improves generator performance when only a small amount of data is available in the domain.Toshiba Research Europe Ltd.This is the accepted manuscript. It is currently embargoed pending publication

Dialogue manager domain adaptation using Gaussian process reinforcement learning

Spoken dialogue systems allow humans to interact with machines using natural speech. As such, they have many benefits. By using speech as the primary communication medium, a computer interface can facilitate swift, human-like acquisition of information. In recent years, speech interfaces have become ever more popular, as is evident from the rise of personal assistants such as Siri, Google Now, Cortana and Amazon Alexa. Recently, data-driven machine learning methods have been applied to dialogue modelling and the results achieved for limited-domain applications are comparable to or outperform traditional approaches. Methods based on Gaussian processes are particularly effective as they enable good models to be estimated from limited training data. Furthermore, they provide an explicit estimate of the uncertainty which is particularly useful for reinforcement learning. This article explores the additional steps that are necessary to extend these methods to model multiple dialogue domains. We show that Gaussian process reinforcement learning is an elegant framework that naturally supports a range of methods, including prior knowledge, Bayesian committee machines and multi-agent learning, for facilitating extensible and adaptable dialogue systems.Engineering and Physical Sciences Research Council (Grant ID: EP/M018946/1 ”Open Domain Statistical Spoken Dialogue Systems”

Electron-phonon effects and transport in carbon nanotubes

We calculate the electron-phonon scattering and binding in semiconducting carbon nanotubes, within a tight binding model. The mobility is derived using a multi-band Boltzmann treatment. At high fields, the dominant scattering is inter-band scattering by LO phonons corresponding to the corners K of the graphene Brillouin zone. The drift velocity saturates at approximately half the graphene Fermi velocity. The calculated mobility as a function of temperature, electric field, and nanotube chirality are well reproduced by a simple interpolation formula. Polaronic binding give a band-gap renormalization of ~70 meV, an order of magnitude larger than expected. Coherence lengths can be quite long but are strongly energy dependent.Comment: 5 pages and 4 figure