Thermodynamic properties of spin-1/2 transverse XY chain with Dzyaloshinskii-Moriya interaction: Exact solution for correlated Lorentzian disorder

We extend the consideration of the spin-1/2 transverse XY chain with correlated Lorentzian disorder (Phys. Rev. B {\bf 55,} 14298 (1997)) for the case of additional Dzyaloshinskii-Moriya interspin interaction. It is shown how the averaged density of states can be calculated exactly. Results are presented for the density of states and the transverse magnetization.Comment: 2 figure

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”

Trusting numbers: uncertainty and the pathology laboratory

The document attached has been archived with permission from the editor of the Medical Journal of Australia. An external link to the publisher’s copy is included.T Paul Hutchinso

JWST observations of stellar occultations by solar system bodies and rings

In this paper we investigate the opportunities provided by the James Webb Space Telescope (JWST) for significant scientific advances in the study of solar system bodies and rings using stellar occultations. The strengths and weaknesses of the stellar occultation technique are evaluated in light of JWST's unique capabilities. We identify several possible JWST occultation events by minor bodies and rings, and evaluate their potential scientific value. These predictions depend critically on accurate a priori knowledge of the orbit of JWST near the Sun-Earth Lagrange-point 2 (L2). We also explore the possibility of serendipitous stellar occultations by very small minor bodies as a by-product of other JWST observing programs. Finally, to optimize the potential scientific return of stellar occultation observations, we identify several characteristics of JWST's orbit and instrumentation that should be taken into account during JWST's development.Comment: This paper is one of a series for a special issue on Solar System observations with JWST in PASP. Accepted 2-Oct-2015. Preprint 30 pages, 5 tables, 8 figure