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    ABSTRACT: The energy consumption of the Internet accounts for approximately 1% of the world's total electricity usage, which may become one of the main constraints on its further growth. In response, we propose an evolutionary based dynamic energy management framework that reduces the overall energy consumption without degrading network performance. The main concept is to combine infrastructure sleeping with virtual router migration. During off-peak hours, the virtual routers are moved onto fewer physical platforms and the unused resources are placed in a sleep state to save energy. The sleeping physical platforms are then reawakened during busy periods. In particular, an evolutionary based algorithm called MOEA_VRM is developed to determine where to move the virtual routers in question. The algorithm is then evaluated using a multi-layer fluid flow event-driven simulator to assess its potential.
    Sustainable Computing: Informatics and Systems. 06/2014;
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    ABSTRACT: The graphical structure of a Bayesian network (BN) makes it a technology well-suited for developing decision support models from a combination of domain knowledge and data. The domain knowledge of experts is used to determine the graphical structure of the BN, corresponding to the relationships and between variables, and data is used for learning the strength of these relationships. However, the available data seldom match the variables in the structure that is elicited from experts, whose models may be quite detailed; consequently, the structure needs to be abstracted to match the data. Up to now, this abstraction has been informal, loosening the link between the final model and the experts’ knowledge. In this paper, we propose a method for abstracting the BN structure by using four ‘abstraction’ operations: node removal, node merging, state-space collapsing and edge removal. Some of these steps introduce approximations, which can be identified from changes in the set of conditional independence (CI) assertions of a network.
    Knowledge-Based Systems 05/2014;
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    ABSTRACT: This Letter presents a method for making an uneven surface behave as a flat surface. This allows an object to be concealed (cloaked) under an uneven portion of the surface, without disturbing the wave propagation on the surface. The cloaks proposed in this Letter achieve perfect cloaking that only relies upon isotropic radially dependent refractive index profiles, contrary to those previously published. In addition, these cloaks are very thin, just a fraction of a wavelength in thickness, yet can conceal electrically large objects. While this paper focuses on cloaking electromagnetic surface waves, the theory is also valid for other types of surface waves. The performance of these cloaks is simulated using dielectric filled waveguide geometries, and the curvature of the surface is shown to be rendered invisible, hiding any object positioned underneath. Finally, a transformation of the required dielectric slab permittivity was performed for surface wave propagation, demonstrating the practical applicability of this technique.
    Physical Review Letters 11/2013; 111(21):213901.
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    ABSTRACT: Many medical conditions are only indirectly observed through symptoms and tests. Developing predictive models for such conditions is challenging since they can be thought of as 'latent' variables. They are not present in the data and often get confused with measurements. As a result, building a model that fits data well is not the same as making a prediction that is useful for decision makers. In this paper, we present a methodology for developing Bayesian network (BN) models that predict and reason with latent variables, using a combination of expert knowledge and available data. The method is illustrated by a case study into the prediction of acute traumatic coagulopathy (ATC), a disorder of blood clotting that significantly increases the risk of death following traumatic injuries. There are several measurements for ATC and previous models have predicted one of these measurements instead of the state of ATC itself. Our case study illustrates the advantages of models that distinguish between an underlying latent condition and its measurements, and of a continuing dialogue between the modeller and the domain experts as the model is developed using knowledge as well as data.
    Journal of Biomedical Informatics 11/2013;
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    ABSTRACT: This paper addresses the problem of non-rigid video registration, or the computation of optical flow from a reference frame to each of the subsequent images in a sequence, when the camera views deformable objects. We exploit the high correlation between 2D trajectories of different points on the same non-rigid surface by assuming that the displacement of any point throughout the sequence can be expressed in a compact way as a linear combination of a low-rank motion basis. This subspace constraint effectively acts as a trajectory regularization term leading to temporally consistent optical flow. We formulate it as a robust soft constraint within a variational framework by penalizing flow fields that lie outside the low-rank manifold. The resulting energy functional can be decoupled into the optimization of the brightness constancy and spatial regularization terms, leading to an efficient optimization scheme. Additionally, we propose a novel optimization scheme for the case of vector valued images, based on the dualization of the data term. This allows us to extend our approach to deal with colour images which results in significant improvements on the registration results. Finally, we provide a new benchmark dataset, based on motion capture data of a flag waving in the wind, with dense ground truth optical flow for evaluation of multi-frame optical flow algorithms for non-rigid surfaces. Our experiments show that our proposed approach outperforms state of the art optical flow and dense non-rigid registration algorithms.
    International Journal of Computer Vision 09/2013; 104:286-314.
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    ABSTRACT: How do human infants learn the causal dependencies between events? Evidence suggests that this remarkable feat can be achieved by observation of only a handful of examples. Many computational models have been produced to explain how infants perform causal inference without explicit teaching about statistics or the scientific method. Here, we propose a spiking neuronal network implementation that can be entrained to form a dynamical model of the temporal and causal relationships between events that it observes. The network uses spike-time dependent plasticity, long-term depression, and heterosynaptic competition rules to implement Rescorla-Wagner-like learning. Transmission delays between neurons allow the network to learn a forward model of the temporal relationships between events. Within this framework, biologically realistic synaptic plasticity rules account for well-known behavioral data regarding cognitive causal assumptions such as backwards blocking and screening-off. These models can then be run as emulators for state inference. Furthermore, this mechanism is capable of copying synaptic connectivity patterns between neuronal networks by observing the spontaneous spike activity from the neuronal circuit that is to be copied, and it thereby provides a powerful method for transmission of circuit functionality between brain regions.
    Cognitive Science A Multidisciplinary Journal 08/2013;
  • Proceedings of the National Academy of Sciences 07/2013;
  • Physics of Life Reviews 07/2013;
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    ABSTRACT: The recent theory of sequential games and selection functions by Escardó & Oliva is extended to games in which players move simultaneously. The Nash existence theorem for mixed-strategy equilibria of finite games is generalized to games defined by selection functions. A normal form construction is given, which generalizes the game-theoretic normal form, and its soundness is proved. Minimax strategies also generalize to the new class of games, and are computed by the Berardi-Bezem-Coquand functional, studied in proof theory as an interpretation of the axiom of countable choice.
    Proceedings of The Royal Society A Mathematical Physical and Engineering Sciences 06/2013; 469(2154):20130041.
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    ABSTRACT: In the last decade, a technique termed transformation optics has been developed for the design of novel electromagnetic devices. This method defines the exact modification of magnetic and dielectric constants required, so that the electromagnetic behaviour remains invariant after a transformation to a new coordinate system. Despite the apparently infinite possibilities that this mathematical tool introduces, one restriction has repeatedly recurred since its conception: limited frequency bands of operation. Here we circumvent this problem with the proposal of a full dielectric implementation of a transformed planar hyperbolic lens which retains the same focusing properties of an original curved lens. The redesigned lens demonstrates operation with high directivity and low side lobe levels for an ultra-wide band of frequencies, spanning over three octaves. The methodology proposed in this paper can be applied to revolutionise the design of many electromagnetic devices overcoming bandwidth limitations.
    Scientific Reports 05/2013; 3:1903.
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