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    Rapid-Cycle Learning for Effective Remedial Action and Dissemination::Ukraine Explosive Ordnance Disposal Case Study

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    Rapid adaptation has always been an imperative for military organizations facing dynamic threats. Learning lessons from experience is equally familiar as a way of tackling problems to military personnel. However, militaries have often struggled to institutionalise that learning so that it becomes available at scale and in potentially strategic ways. ‘Lessons Learned’ is a highly-developed NATO process supported by a NATO Handbook, related other NATO handbooks and courses, and consultancy services from the NATO Joint Analysis and Lessons Learned Centre (JALLC). And yet our current study and recent others show that most lessons-learned systems fail to achieve their full potential impact. Just how that happens, and why that shortfall persists even in the face of direct threat, is puzzling; it indicates that learning involves far more than just resources like databases and training to collect new information

    Hughes, Briony

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    Masters, Paul

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    Elliott, Caroline

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    Virtual Exploration of the Human Vocal Tract

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    Hypothesis: By simulating the acoustic field throughout the entire vocal tract the evolution of speech sounds within the tract can be directly and quantitatively related to physical variations in the tract geometry. This insight into speech production could then be applied to a variety of fields where the ability to alter or investigate speech characteristics in a targeted way could be useful for example in the teaching of speech science, in speech coaching, or as part of the planning of medical procedures. In this research, a bespoke acoustic simulation package has been produced using a continuous 3-dimensional Digital Waveguide Mesh (DWM) which can produce acoustic output throughout the entire simulation domain containing the tract at every time step. This package has been shown to reproduce formant frequencies for a variety of vocal tract shapes with an average mean absolute error of 10.12% at the lips, which is comparable to other research. These results have been investigated by comparing simulation output to recorded output from physical models. This simulation package has also been used to perform studies into the shifting of formant frequencies during speech sound production along the length of the tract, and into the effect on formant frequencies of the removal of geometric features of the tract such as the piriform fossae. These studies have been compared to physical internal measurements of vocal tract models from living subjects, showing preliminary agreement with further development required. A large emphasis has been placed on the accessibility of this research, with the production of several tools for visualisation of the data contained within, and with decisions made during the production of the simulation package itself

    Video Deepfake Classification Using Particle Swarm Optimization-based Evolving Ensemble Models

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    The recent breakthrough of deep learning based generative models has led to the escalated generation of photo-realistic synthetic videos with significant visual quality. Automated reliable detection of such forged videos requires the extraction of fine-grained discriminative spatial-temporal cues. To tackle such challenges, we propose weighted and evolving ensemble models comprising 3D Convolutional Neural Networks (CNNs) and CNN-Recurrent Neural Networks (RNNs) with Particle Swarm Optimization (PSO) based network topology and hyper-parameter optimization for video authenticity classification. A new PSO algorithm is proposed, which embeds Muller’s method and fixed-point iteration based leader enhancement, reinforcement learning-based optimal search action selection, a petal spiral simulated search mechanism, and cross-breed elite signal generation based on adaptive geometric surfaces. The PSO variant optimizes the RNN topologies in CNN-RNN, as well as key learning configurations of 3D CNNs, with the attempt to extract effective discriminative spatial-temporal cues. Both weighted and evolving ensemble strategies are used for ensemble formulation with aforementioned optimized networks as base classifiers. In particular, the proposed PSO algorithm is used to identify optimal subsets of optimized base networks for dynamic ensemble generation to balance between ensemble complexity and performance. Evaluated using several well-known synthetic video datasets, our approach outperforms existing studies and various ensemble models devised by other search methods with statistical significance for video authenticity classification. The proposed PSO model also illustrates statistical superiority over a number of search methods for solving optimization problems pertaining to a variety of artificial landscapes with diverse geometrical layouts

    Essays on Fiscal Rule Design and its implications

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    The three chapters of this thesis examine different aspects of the design of fiscal rules and their implications on the effects of policy interventions and how policy reacts to the economy.Chapter 1 focuses on the design of fiscal rules in DSGE models, which has been shown to matter crucially in identifying the effects of policy interventions and analyses two mechanically distinct components of fiscal policy rules: fiscal rule interactions and multimodality.In a first exercise, a set of alternative fiscal rules is considered for the benchmark Leeper, Plante and Traum (2010) model, with the main design feature being across budget block (expenditure vs taxation) interactions. The models are compared using the Bayesian data density. The results show that the Leeper, Plante and Traum (2010) model is competitive in the set but may be improved by including across-budget component interaction with taxes ordered first. Mechanically, the budget component interactions trickle down to how policy interventions are financed, showing increased coordination across blocks. In the benchmark, a government consumption shock raises the federal government's expenditures, and along the path, taxation increases to bring the debt level back to the steady state. In the recursive block models, budget impacts can be temporarily purely expansionary in that expenditure increases and taxation is reduced. Combining both aspects, it seems to reflect a temporary but coordinated approach to raise output across the expenditure and taxation categories.Secondly, I explore the role of multimodality in fiscal rules. Herbst and Schorfheide (2016) showed that fiscal parameters in the aforementioned model can become multimodal, leading to multimodal impulse responses. In essence, what that means is that fiscal policy may have varied impacts depending on the exact posterior parameter draw. For the Leeper, Plante and Traum (2010) model, I argue using graphs and demonstrate that the source of multimodality in the model is likely the structural design of the rules. Furthermore, building on the analysis in Herbst and Schorfheide (2016), I apply bi-modal regions to the highest posterior density regions as intervals tend to overestimate uncertainty of bi-modal distributions. The results show that the effects of consumption taxation shocks not only predict different scenarios depending on the mode but also disjointed impact scenarios. In particular, for consumption taxation shocks, the average effect of a structural shock is not a particularly likely event by itself.In Chapter 2, I explore how fiscal policy decisions relate to the business cycle and, building on that, how the effects of policy interventions may vary depending on when policy is conducted in the business cycle. To assess this, I estimate a small to medium-sized DSGE model with expressive non-linear fiscal and monetary rules using a higher-order approximation.The estimation procedure employed in this chapter combines several existing approaches developed by Herbst and Schorfheide (2016), Jasra et al. (2010), Buchholz, Chopin and Jacob (2021) and Amisano and Tristani (2010) to trade off computation time and inference quality. The model is estimated using Sequential Monte Carlo techniques to estimate the posterior parameter distribution and particle filter techniques to estimate the likelihood. Together, the estimation procedure reduces the estimation from weeks to days by up to 94%, depending on the comparison basis.To assess the behaviour of the effects of fiscal policy interventions, I sample impulse responses conducted along the historical data. The results present time-varying policy rules in which the effects of fiscal shocks go through deep cycles depending on the initial conditions of the economy. Among the set of fiscal instruments, government consumption goes through the most persistent cycles in its effectiveness in stimulating output. In particular, the effects of government consumption stimulus are estimated to be more effective during the financial crisis and, later, the Covid crisis, while being less effective in periods of above steady state output like the early 2000s.Relating the effects of specific stimulating shocks to the initial conditions using regression techniques, I show that fiscal policy is more effective at stimulating output if the interest rate and debt are low. Furthermore, the effects of government consumption are estimated to be increasing in output while tax cuts are decreasing.As a last contribution of Chapter 2, I explore how the behaviour of the central bank and government varies depending on the business cycle by analysing sampled policy rule gradients constructed on historical data. For the central bank, the results show that in phases of high output growth, the central bank puts more emphasis on controlling inflation and less on output. As the economy shifts into crisis, the central bank reduces its focus on inflation and shifts towards bringing output growth back to target. For the fiscal side, the behaviour is heavily governed by the current debt level, and, for example, during the high debt periods of the 1990s, labour taxation became increasingly responsive to debt to stabilize the budget.Chapter 3 applies the model developed in Chapter 2 to a forecasting exercise using the DSGE-VAR framework. The analysis confirms previous results of the literature that the DSGE-VAR framework and, by extension, DSGE models are frequently useful in aiding forecasting performance for output compared to standard models. Furthermore, I show that DSGE-VAR models can help aid forecasting performance of governmental variables like government consumption and debt quite significantly. However, there seems to be no single best methodology across all data series and forecasting settings considered, similar to the results in Gürkaynak, Kısacıkoğlu and Rossi (2014). Rather, the best-performing methodology may depend on factors like sample selection, modelling framework and potentially others.In a novel exercise, I explore the utility of a variation of the Chapter 2 model with a Zero Lower Bound constraint for forecasting. Overall, the model performs well but is not necessarily competitive with the much simpler DSGE-VAR. However, the ZLB model does show some strength in forecasting fiscal variables

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