In this article, we introduce a method to apply ideas from electrostatics to parameterize the open space around an object. By simulating the object as a virtually charged conductor, we can define an object-centric coordinate system which we call Electric Coordinates. It parameterizes the outer space of a reference object in a way analogous to polar coordinates. We also introduce a measure that quantifies the extent to which an object is wrapped by a surface. This measure can be computed as the electric flux through the wrapping surface due to the electric field around the charged conductor. The electrostatic parameters, which comprise the Electric Coordinates and flux, have several applications in computer graphics, including: texturing, morphing, meshing, path planning relative to a target object, mesh parameterization, designing deformable objects, and computing coverage. Our method works for objects of arbitrary geometry and topology, and thus is applicable in a wide variety of scenarios.

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Providing an explanation of the operation of CNNs that is both accurate and interpretable is becoming essential in fields like medical image analysis, surveillance, and autonomous driving. In these areas, it is important to have confidence that the CNN is working as expected and explanations from saliency maps provide an efficient way of doing this. In this paper, we propose a pair of complementary contributions that improve upon the state of the art for region-based explanations in both accuracy and utility. The first is SWAG, a method for generating accurate explanations quickly using superpixels for discriminative regions which is meant to be a more accurate, efficient, and tunable drop in replacement method for Grad-CAM, LIME, or other region-based methods. The second contribution is based on an investigation into how to best generate the superpixels used to represent the features found within the image. Using SWAG, we compare using superpixels created from the image, a combination of the image and backpropagated gradients, and the gradients themselves. To the best of our knowledge, this is the first method proposed to generate explanations using superpixels explicitly created to represent the discriminative features important to the network. To compare we use both ImageNet and challenging fine-grained datasets over a range of metrics. We demonstrate experimentally that our methods provide the best local and global accuracy compared to Grad-CAM, Grad-CAM++, LIME, XRAI, and RISE.

The allocation of resources to challenge city centre violent crime traditionally relies on historical data to identify hot-spots. The usefulness of such data-driven approaches is limited when historical data is scarce or unavailable (e.g. planning of a new city) or insufficiently representative (e.g. does not account for novel events, such as Olympic Games). In some cities, crime data is not systematically accumulated at all.We present a graph-constrained agent based simulation model of alcohol-related violent crime that is capable of predicting areas of likely violent crime without requiring any historical data. The only inputs to our simulation are publicly available geographical data, which makes our method immediately applicable to a wide range of tasks, such as optimal city planning, police patrol optimisation, devising alcohol licensing policies.In experiments, we evaluate our model and demonstrate agreement of our model's predictions on where and when violence will occur with real-world violent crime data. Analyses indicate that our agent based model may be able to make a significant contribution to attempts to prevent violence through deterrence or by design.

The groupwise approach to non-rigid image registration, solving the dense correspondence problem, has recently been shown to be a useful tool in many applications, including medical imaging, automatic construction of statistical models of appearance and analysis of facial dynamics. Such an approach overcomes limitations of traditional pairwise methods but at a cost of having to search for the solution (optimal registration) in a space of much higher dimensionality which grows rapidly with the number of examples (images) being registered. Techniques to overcome this dimensionality problem have not been addressed sufficiently in the groupwise registration literature.In this paper, we propose a novel, fast and reliable, fully unsupervised stochastic algorithm to search for optimal groupwise dense correspondence in large sets of unmarked images. The efficiency of our approach stems from novel dimensionality reduction techniques specific to the problem of groupwise image registration and from comparative insensitivity of the adopted optimisation scheme (Simultaneous Perturbation Stochastic Approximation (SPSA)) to the high dimensionality of the search space. Additionally, our algorithm is formulated in way readily suited to implementation on graphics processing units (GPU).In evaluation of our method we show a high robustness and success rate, fast convergence on various types of test data, including facial images featuring large degrees of both inter-and intra-person variation, and show considerable improvement in terms of accuracy of solution and speed compared to traditional methods.

Paralinguistic analysis of speech remains a challenging task due to the many confounding factors which affect speech production. In this paper, we address the Interspeech 2018 Computational Paralinguistics Challenge (ComParE) which aims to push the boundaries of sensitivity to non-textual information that is conveyed in the acoustics of speech. We attack the problem on several fronts. We posit that a substantial amount of paralinguistic information is contained in spectral features alone. To this end, we use a large ensemble of Extreme Learning Machines for classification of spectral features. We further investigate the applicability of (an ensemble of) CNN-GRUs networks to model temporal variations therein. We report on the details of the experiments and the results for three ComParE sub-challenges: Atypical Affect, Self-Assessed Affect, and Crying. Our results compare favourably and in some cases exceed the published state-of-the-art performance.

The groupwise approach to non-rigid image registration, solving the dense correspondence problem, has recently been shown to be a useful tool in many applications, including medical imaging, automatic construction of statistical models of appearance and analysis of facial dynamics. Such an approach overcomes limitations of traditional pairwise methods but at a cost of having to search for the solution (optimal registration) in a space of much higher dimensionality which grows rapidly with the number of examples (images) being registered. Techniques to overcome this dimensionality problem have not been addressed sufficiently in the groupwise registration literature.In this paper, we propose a novel, fast and reliable, fully unsupervised stochastic algorithm to search for optimal groupwise dense correspondence in large sets of unmarked images. The efficiency of our approach stems from novel dimensionality reduction techniques specific to the problem of groupwise image registration and from comparative insensitivity of the adopted optimisation scheme (Simultaneous Perturbation Stochastic Approximation (SPSA)) to the high dimensionality of the search space. Additionally, our algorithm is formulated in way readily suited to implementation on graphics processing units (GPU).In evaluation of our method we show a high robustness and success rate, fast convergence on various types of test data, including facial images featuring large degrees of both inter-and intra-person variation, and show considerable improvement in terms of accuracy of solution and speed compared to traditional methods.