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Funder: Cambridge Trust, Funder: Engineering and Physical Sciences Research Council; Id: http://dx.doi.org/10.13039/501100000266, Epidemiological forecasts are beset by uncertainties about the underlying epidemiological processes, and the surveillance process through which data are acquired. We present a Bayesian inference methodology that quantifies these uncertainties, for epidemics that are modelled by (possibly) non-stationary, continuous-time, Markov population processes. The efficiency of the method derives from a functional central limit theorem approximation of the likelihood, valid for large populations. We demonstrate the methodology by analysing the early stages of the COVID-19 pandemic in the UK, based on age-structured data for the number of deaths. This includes maximum a posteriori estimates, Markov chain Monte Carlo sampling of the posterior, computation of the model evidence, and the determination of parameter sensitivities via the Fisher information matrix. Our methodology is implemented in PyRoss, an open-source platform for analysis of epidemiological compartment models.

In systems of diffusing particles, we investigate large deviations of a time-averaged measure of clustering around one particle. We focus on biased ensembles of trajectories, which realise large-deviation events. The bias acts on a single particle, but elicits a response that spans the whole system. We analyse this effect through the lens of Macroscopic Fluctuation Theory, focussing on the coupling of the bias to hydrodynamic modes. This explains that the dynamical free energy has non-trivial scaling relationships with the system size, in 1 and 2 spatial dimensions. We show that the long-ranged response to a bias on one particle also has consequences when biasing two particles., Comment: 14 pages, 11 figures

We analyse large deviations of the dynamical activity in one-dimensional systems of diffusing hard particles. Using an optimal-control representation of the large-deviation problem, we analyse effective interaction forces which can be added to the system, to aid sampling of biased ensembles of trajectories. We find several distinct regimes, as a function of the activity and the system size: we present approximate analytical calculations that characterise the effective interactions in several of these regimes. For high activity the system is hyperuniform and the interactions are long-ranged and repulsive. For low activity, there is a near-equilibrium regime described by macroscopic fluctuation theory, characterised by long-ranged attractive forces. There is also a far-from-equilibrium regime in which one of the interparticle gaps becomes macroscopic and the interactions depend strongly on the size of this gap. We discuss the extent to which transition path sampling of these ensembles is improved by adding suitable control forces., Comment: 37 pages

Small Cell Cloud (SCC) consists of Cloud-enabled Small Cells (CeSCs), which serve as radio end-points for mobile user equipments (UEs) and host computation offloaded from mobile UEs. SCC hereby brings advantages of a centralized cloud computation to the users' vicinity. The SCC architecture provides a mechanism for distribution of computation demand across the CeSCs. An effectiveness of the offloading is determined based on quality of radio channel between the UEs and the CeSC and predicted computation complexity. In this paper, we introduce an implementation of an offloading framework to facilitate adaptation of mobile apps for the SCC and to handle low-level communication between the app and the SCC. An evaluation of the offloading framework is conducted using Augmented Reality (AR) app, which requires intensive computations and low latency. The offloading framework and the AR app are a basement for the SCC testbed used to proof the concept of the computation offloading. Various computation and radio parameters are investigated to reveal benefits of the SCC. According to the performed measurements, the computation offloading can decrease latency up to 88 % and energy consumption of the UEs up to 93 %.

Multimodal human-computer interaction combining multiple input modalities is the key for permitting our highly skilled and coordinated communicative behavior to control computer systems in flexible and natural manner. In this paper a multimodal interaction platform, called Manitou, is presented. The platform empowers researchers and developers to employ speech, gestures and other modalities in applications, enabling multimodal interaction. Key features of the platform are exposed to the web environment to facilitate a fast prototyping and delivering applications, exploiting multimodal interfaces. Benefits of the platform have been proven by a sample application and subsequent user experience survey.

Advances in voice-recognition platforms have led to new possibilities in deploying automated voice-interactive engines for Web content. We present Voice2Web, an architecture allowing to manage access to the resources of the World Wide Web using voice interaction. It rests on the VoiceXML standard and enables rapid composition of dynamic services querying the Web resources. We demonstrate its use on practical examples, discuss architecture implications and invite further platform experimentation.