An approach to statistical lip modelling for speaker identification via chromatic feature extraction

This paper presents a novel technique for the tracking of moving lips for the purpose of speaker identification. In our system, a model of the lip contour is formed directly from chromatic information in the lip region. Iterative refinement of contour point estimates is not required. Colour features are extracted from the lips via concatenated profiles taken around the lip contour. Reduction of order in lip features is obtained via principal component analysis (PCA) followed by linear discriminant analysis (LDA). Statistical speaker models are built from the lip features based on the Gaussian mixture model (GMM). Identification experiments performed on the M2VTS¹ database, show encouraging results

Stochastic modelling of T cell homeostasis for two competing clonotypes via the master equation

Stochastic models for competing clonotypes of T cells by multivariate, continuous-time, discrete state, Markov processes have been proposed in the literature by Stirk, Molina-París and van den Berg (2008). A stochastic modelling framework is important because of rare events associated with small populations of some critical cell types. Usually, computational methods for these problems employ a trajectory-based approach, based on Monte Carlo simulation. This is partly because the complementary, probability density function (PDF) approaches can be expensive but here we describe some efficient PDF approaches by directly solving the governing equations, known as the Master Equation. These computations are made very efficient through an approximation of the state space by the Finite State Projection and through the use of Krylov subspace methods when evolving the matrix exponential. These computational methods allow us to explore the evolution of the PDFs associated with these stochastic models, and bimodal distributions arise in some parameter regimes. Time-dependent propensities naturally arise in immunological processes due to, for example, age-dependent effects. Incorporating time-dependent propensities into the framework of the Master Equation significantly complicates the corresponding computational methods but here we describe an efficient approach via Magnus formulas. Although this contribution focuses on the example of competing clonotypes, the general principles are relevant to multivariate Markov processes and provide fundamental techniques for computational immunology.

Computational approaches for modelling intrinsic noise and delays in genetic regulatory networks

This chapter focuses on the interactions and roles between delays and intrinsic noise effects within cellular pathways and regulatory networks. We address these aspects by focusing on genetic regulatory networks that share a common network motif, namely the negative feedback loop, leading to oscillatory gene expression and protein levels. In this context, we discuss computational simulation algorithms for addressing the interplay of delays and noise within the signaling pathways based on biological data. We address implementational issues associated with efficiency and robustness. In a molecular biology setting we present two case studies of temporal models for the Hes1 gene (Monk, 2003; Hirata et al., 2002), known to act as a molecular clock, and the Her1/Her7 regulatory system controlling the periodic somite segmentation in vertebrate embryos (Giudicelli and Lewis, 2004; Horikawa et al., 2006).

Mapping the language of science and science teaching practices : a case study of early childhood school science

Concerns raised in educational reports about school science in terms of students. outcomes and attitudes, as well as science teaching practices prompted investigation into science learning and teaching practices at the foundational level of school science. Without science content and process knowledge, understanding issues of modern society and active participation in decision-making is difficult. This study contended that a focus on the development of the language of science could enable learners to engage more effectively in learning science and enhance their interest and attitudes towards science. Furthermore, it argued that explicit teaching practices where science language is modelled and scaffolded would facilitate the learning of science by young children at the beginning of their formal schooling. This study aimed to investigate science language development at the foundational level of school science learning in the preparatory-school with students aged five and six years. It focussed on the language of science and science teaching practices in early childhood. In particular, the study focussed on the capacity for young students to engage with and understand science language. Previous research suggests that students have difficulty with the language of science most likely because of the complexities and ambiguities of science language. Furthermore, literature indicates that tensions transpire between traditional science teaching practices and accepted early childhood teaching practices. This contention prompted investigation into means and models of pedagogy for learning foundational science language, knowledge and processes in early childhood. This study was positioned within qualitative assumptions of research and reported via descriptive case study. It was located in a preparatory-school classroom with the class teacher, teacher-aide, and nineteen students aged four and five years who participated with the researcher in the study. Basil Bernstein.s pedagogical theory coupled with Halliday.s Systemic Functional Linguistics (SFL) framed an examination of science pedagogical practices for early childhood science learning. Students. science learning outcomes were gauged by focussing a Hallydayan lens on their oral and reflective language during 12 science-focussed episodes of teaching. Data were collected throughout the 12 episodes. Data included video and audio-taped science activities, student artefacts, journal and anecdotal records, semi-structured interviews and photographs. Data were analysed according to Bernstein.s visible and invisible pedagogies and performance and competence models. Additionally, Halliday.s SFL provided the resource to examine teacher and student language to determine teacher/student interpersonal relationships as well as specialised science and everyday language used in teacher and student science talk. Their analysis established the socio-linguistic characteristics that promoted science competencies in young children. An analysis of the data identified those teaching practices that facilitate young children.s acquisition of science meanings. Positive indications for modelling science language and science text types to young children have emerged. Teaching within the studied setting diverged from perceived notions of common early childhood practices and the benefits of dynamic shifting pedagogies were validated. Significantly, young students demonstrated use of particular specialised components of school-science language in terms of science language features and vocabulary. As well, their use of language demonstrated the students. knowledge of science concepts, processes and text types. The young students made sense of science phenomena through their incorporation of a variety of science language and text-types in explanations during both teacher-directed and independent situations. The study informs early childhood science practices as well as practices for foundational school science teaching and learning. It has exposed implications for science education policy, curriculum and practices. It supports other findings in relation to the capabilities of young students. The study contributes to Systemic Functional Linguistic theory through the development of a specific resource to determine the technicality of teacher language used in teaching young students. Furthermore, the study contributes to methodology practices relating to Bernsteinian theoretical perspectives and has demonstrated new ways of depicting and reporting teaching practices. It provides an analytical tool which couples Bernsteinian and Hallidayan theoretical perspectives. Ultimately, it defines directions for further research in terms of foundation science language learning, ongoing learning of the language of science and learning science, science teaching and learning practices, specifically in foundational school science, and relationships between home and school science language experiences.

Dynamic texture reconstruction from sparse codes for unusual event detection in crowded scenes

Unusual event detection in crowded scenes remains challenging because of the diversity of events and noise. In this paper, we present a novel approach for unusual event detection via sparse reconstruction of dynamic textures over an overcomplete basis set, with the dynamic texture described by local binary patterns from three orthogonal planes (LBPTOP). The overcomplete basis set is learnt from the training data where only the normal items observed. In the detection process, given a new observation, we compute the sparse coefficients using the Dantzig Selector algorithm which was proposed in the literature of compressed sensing. Then the reconstruction errors are computed, based on which we detect the abnormal items. Our application can be used to detect both local and global abnormal events. We evaluate our algorithm on UCSD Abnormality Datasets for local anomaly detection, which is shown to outperform current state-of-the-art approaches, and we also get promising results for rapid escape detection using the PETS2009 dataset.

Modelling the activation of words in human memory : the spreading activation, spooky-activation-at-a-distance and the entanglement models compared

Modelling how a word is activated in human memory is an important requirement for determining the probability of recall of a word in an extra-list cueing experiment. The spreading activation, spooky-action-at-a-distance and entanglement models have all been used to model the activation of a word. Recently a hypothesis was put forward that the mean activation levels of the respective models are as follows: Spreading � Entanglment � Spooking-action-at-a-distance This article investigates this hypothesis by means of a substantial empirical analysis of each model using the University of South Florida word association, rhyme and word norms.

Modelling travelling waves in spatially constrained environment solutions

In this study, we consider how Fractional Differential Equations (FDEs) can be used to study the travelling wave phenomena in parabolic equations. As our method is conducted under intracellular environments that are highly crowded, it was discovered that there is a simple relationship between the travelling wave speed and obstacle density.

A Krylov-based finite state projection algorithm for solving the chemical master equation arising in the discrete modelling of biological systems

Biochemical reactions underlying genetic regulation are often modelled as a continuous-time, discrete-state, Markov process, and the evolution of the associated probability density is described by the so-called chemical master equation (CME). However the CME is typically difficult to solve, since the state-space involved can be very large or even countably infinite. Recently a finite state projection method (FSP) that truncates the state-space was suggested and shown to be effective in an example of a model of the Pap-pili epigenetic switch. However in this example, both the model and the final time at which the solution was computed, were relatively small. Presented here is a Krylov FSP algorithm based on a combination of state-space truncation and inexact matrix-vector product routines. This allows larger-scale models to be studied and solutions for larger final times to be computed in a realistic execution time. Additionally the new method computes the solution at intermediate times at virtually no extra cost, since it is derived from Krylov-type methods for computing matrix exponentials. For the purpose of comparison the new algorithm is applied to the model of the Pap-pili epigenetic switch, where the original FSP was first demonstrated. Also the method is applied to a more sophisticated model of regulated transcription. Numerical results indicate that the new approach is significantly faster and extendable to larger biological models.

Activity modelling in crowded environments : a soft decision approach

Probabilistic topic models have recently been used for activity analysis in video processing, due to their strong capacity to model both local activities and interactions in crowded scenes. In those applications, a video sequence is divided into a collection of uniform non-overlaping video clips, and the high dimensional continuous inputs are quantized into a bag of discrete visual words. The hard division of video clips, and hard assignment of visual words leads to problems when an activity is split over multiple clips, or the most appropriate visual word for quantization is unclear. In this paper, we propose a novel algorithm, which makes use of a soft histogram technique to compensate for the loss of information in the quantization process; and a soft cut technique in the temporal domain to overcome problems caused by separating an activity into two video clips. In the detection process, we also apply a soft decision strategy to detect unusual events.We show that the proposed soft decision approach outperforms its hard decision counterpart in both local and global activity modelling.

Unusual Event Detection in Crowded Scenes Using Bag of LBPs in spatio-temporal patches

Modelling events in densely crowded environments remains challenging, due to the diversity of events and the noise in the scene. We propose a novel approach for anomalous event detection in crowded scenes using dynamic textures described by the Local Binary Patterns from Three Orthogonal Planes (LBP-TOP) descriptor. The scene is divided into spatio-temporal patches where LBP-TOP based dynamic textures are extracted. We apply hierarchical Bayesian models to detect the patches containing unusual events. Our method is an unsupervised approach, and it does not rely on object tracking or background subtraction. We show that our approach outperforms existing state of the art algorithms for anomalous event detection in UCSD dataset.