As humans we possess an intuitive ability for navigation which we master through years of practice; however existing approaches to model this trait for diverse tasks including monitoring pedestrian flow and detecting abnormal events have been limited by using a variety of hand-crafted features. Recent research in the area of deeplearning has demonstrated the power of learning features directly from the data; and related research in recurrent neural networks has shown exemplary results in sequenceto-sequence problems such as neural machine translation and neural image caption generation. Motivated by these approaches, we propose a novel method to predict the future motion of a pedestrian given a short history of their, and their neighbours, past behaviour. The novelty of the proposed method is the combined attention model which utilises both "soft attention" as well as "hard-wired" attention in order to map the trajectory information from the local neighbourhood to the future positions of the pedestrian of interest. We illustrate how a simple approximation of attention weights (i.e hard-wired) can be merged together with soft attention weights in order to make our model applicable for challenging real world scenarios with hundreds of neighbours. The navigational capability of the proposed method is tested on two challenging publicly available surveillance databases where our model outperforms the currentstate-of-the-art methods. Additionally, we illustrate how the proposed architecture can be directly applied for the task of abnormal event detection without handcrafting the features.

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In a clinical setting, pain is reported either through patient self-report or via an observer. Such measures are problematic as they are: 1) subjective, and 2) give no specific timing information. Coding pain as a series of facial action units (AUs) can avoid these issues as it can be used to gain an objective measure of pain on a frame-by-frame basis. Using video data from patients with shoulder injuries, in this paper, we describe an active appearance model (AAM)-based system that can automatically detect the frames in video in which a patient is in pain. This pain data set highlights the many challenges associated with spontaneous emotion detection, particularly that of expression and head movement due to the patient's reaction to pain. In this paper, we show that the AAM can deal with these movements and can achieve significant improvements in both the AU and pain detection performance compared to the current-state-of-the-art approaches which utilize similarity-normalized appearance features only.

SummaryLittle is known about the intracellular events that occur following the initial inhibition of Mycobacterium tuberculosis by the first-line antituberculosis drugs isoniazid (INH) and ethambutol (EMB). Understanding these pathways should provide significant insights into the adaptive strategies M. tuberculosis undertakes to survive antibiotics. We have discovered that the M. tuberculosis iniA gene (

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This article describes a general and powerful approach to modelling mismatch in speaker recognition by including an explicit session term in the Gaussian mixture speaker modelling framework. Under this approach, the Gaussian mixture model (GMM) that best represents the observations of a particular recording is the combination of the true speaker model with an additional session-dependent offset constrained to lie in a low-dimensional subspace representing session variability.A novel and efficient model training procedure is proposed in this work to perform the simultaneous optimisation of the speaker model and session variables required for speaker training. Using a similar iterative approach to the Gauss-Seidel method for solving linear systems, this procedure greatly reduces the memory and computational resources required by a direct solution.Extensive experimentation demonstrates that the explicit session modelling provides up to a 68% reduction in detection cost over a standard GMM-based system and significant improvements over a system utilising feature mapping, and is shown to be effective on the corpora of recent National Institute of Standards and Technology (NIST) Speaker Recognition Evaluations, exhibiting different session mismatch conditions.