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Presentation Title: Developing Novel ICT Infrastructure for Future Power and Energy Systems Summary: The presentation will provide an overview of the research activities of the Institute of Energy Futures at Brunel University London. The focus of the presentation will be on the major research projects that are currently in progress within the Smart Power Networks theme of the Institute of Energy futures. Specific reference will be made to ongoing major research projects relating to the development of novel ICT infrastructure for future power and energy systems.

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Title: An Application- and Platform-agnostic Control and Monitoring Framework for Multicore Systems Abstract: Heterogeneous multiprocessor systems have increased in complexity to provide both high performance and energy efficiency for a diverse range of applications. This motivates the need for a standard framework that enables the management, at runtime, of software applications executing on these processors. This paper proposes the first fully application and platform-agnostic framework for runtime management approaches that control and optimise software applications and hardware resources. This is achieved by separating the system into three distinct layers connected by an API and cross-layer constructs called knobs and monitors. The proposed framework also supports the management of applications that are executing concurrently on heterogeneous platforms. The operation of the proposed framework is experimentally validated using a basic runtime controller and two heterogeneous platforms, to show how it is application- and platform-agnostic and easy to use. Furthermore, the management of concurrently executing applications through the framework is demonstrated. Finally, two recently reported runtime management approaches are implemented to demonstrate how the framework enables their operation and comparison. The energy and latency overheads introduced by the framework have been quantified and an open-source implementation has been released.

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Security features of microprocessors and how they are leveraged at programming languages.

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Cryptocurrency masterclass with Police

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Uncertainty and variability is intrinsic to a plethora of biological processes that we want to understand, model and predict. In cardiac modelling, sources of uncertainty stem from the experimental error in the measurements from our protocols, lack of knowledge about the underlying mechanisms leading to structural error in our models, variability due to differences in cell and ion channel states due to cells being in different settings and gene expression patterns, and variability due to the inherent stochasticity of some of these processes exhibited at multiple time and spatial scales. To accommodate mathematical/phenomenological models in safety-critical clinical practice and drug development, it is therefore of utmost importance to quantify and propagate these uncertainties to model predictions. Bayesian statistics plays a major role in carrying out uncertainty quantification effectively. However, cardiac models pose a unique set of challenges for Bayesian statistical methods. In this talk I would present Bayesian statistical and modern machine learning approaches towards “forward” (from inputs to model predictions) and “inverse” (from experimental data to model structure) uncertainty quantification in cellular cardiac electropysiological models. Specifically, I would present approaches to overcome the computational and statistical challenges associated with uncertainty quantification in mechanistic models, described by differential equations, and highlight some of the open challenges. Furthermore, I would discuss the potential of modern machine learning techniques such as black-box variational inference and probabilistic programming towards solving the uncertainty quantification problem efficiently. Following are the references accompanying this talk: 1) Sanmitra Ghosh, David Gavaghan, Gary Mirams, “Gaussian process emulation for discontinuous response surfaces with applications for cardiac electrophysiology models”, https://arxiv.org/abs/1805.10020v1 2) Sanmitra Ghosh “Probabilistic Programming for Mechanistic Models (P2M2) tutorial repository”, https://github.com/sanmitraghosh/P2M2

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One of the key challenges of visual perception is to extract abstract models of 3D objects and object categories from visual measurements, which are affected by complex nuisance factors such as viewpoint, occlusion, motion, and deformations. In this talk, I will introduce two approaches that, given a large number of images of an object and no other supervision, can factorize image deformations and appearance. I will demonstrate the applicability of this method to articulated objects and deformable objects such as human faces and body by learning embeddings from random synthetic transformations or optical flow correspondences, all without any manual supervision. The talk will cover three recent recent papers: [1] Thewlis, J., Bilen, H., & Vedaldi, A. (2017). Unsupervised learning of object landmarks by factorized spatial embeddings. In International Conference on Computer Vision (ICCV). [2] Thewlis, J., Bilen, H., & Vedaldi, A. (2017). Unsupervised learning of object landmarks by factorized spatial embeddings. In Neural Information Processing Systems (NIPS). [3] Jakab, T., Gupta, A., Bilen, H., & Vedaldi, A. (2018). Conditional Image Generation for Learning the Structure of Visual Objects. (NIPS).

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