Using a Discrete Hidden Markov Model Kernel for lip-based biometric identification

In this paper, a novel and effective lip-based biometric identification approach with the Discrete Hidden Markov Model Kernel (DHMMK) is developed. Lips are described by shape features (both geometrical and sequential) on two different grid layouts: rectangular and polar. These features are then specifically modeled by a DHMMK, and learnt by a support vector machine classifier. Our experiments are carried out in a ten-fold cross validation fashion on three different datasets, GPDS-ULPGC Face Dataset, PIE Face Dataset and RaFD Face Dataset. Results show that our approach has achieved an average classification accuracy of 99.8%, 97.13%, and 98.10%, using only two training images per class, on these three datasets, respectively. Our comparative studies further show that the DHMMK achieved a 53% improvement against the baseline HMM approach. The comparative ROC curves also confirm the efficacy of the proposed lip contour based biometrics learned by DHMMK. We also show that the performance of linear and RBF SVM is comparable under the frame work of DHMMK.

Going beyond the identification of change facilitators to effectively implement a new model of services: lessons learned from a case example in paediatric rehabilitation

Purpose: To identify facilitators and barriers to service reorganization, how they evolved and interacted to influence change during the implementation of a new service delivery model of paediatric rehabilitation. Methods: Over 3 years, different stakeholders responded to SWOT questionnaires (n = 139) and participated in focus groups (n = 19) and telephone interviews (n = 13). A framework based on socio constructivist theories made sense of the data. Results: Facilitators related to the programme's structure (e.g. funding), the actors (e.g. willingness to test the new service model) and the change management process (e.g. participative approach). Some initial facilitators became barriers (e.g. leadership lacked at the end), while other barriers emerged (e.g. lack of tools). Understanding factor interactions requires examining the multiple actors’ intentions, actions and consequences and their relations with structural elements. Conclusions: Analysing facilitators and barriers helped better understand the change processes, but this must be followed by concrete actions to successfully implement new paediatric rehabilitation models.

Functional and histopathological identification of the respiratory failure in a DMSXL transgenic mouse model of myotonic dystrophy.

Acute and chronic respiratory failure is one of the major and potentially life-threatening features in individuals with myotonic dystrophy type 1 (DM1). Despite several clinical demonstrations showing respiratory problems in DM1 patients, the mechanisms are still not completely understood. This study was designed to investigate whether the DMSXL transgenic mouse model for DM1 exhibits respiratory disorders and, if so, to identify the pathological changes underlying these respiratory problems. Using pressure plethysmography, we assessed the breathing function in control mice and DMSXL mice generated after large expansions of the CTG repeat in successive generations of DM1 transgenic mice. Statistical analysis of breathing function measurements revealed a significant decrease in the most relevant respiratory parameters in DMSXL mice, indicating impaired respiratory function. Histological and morphometric analysis showed pathological changes in diaphragmatic muscle of DMSXL mice, characterized by an increase in the percentage of type I muscle fibers, the presence of central nuclei, partial denervation of end-plates (EPs) and a significant reduction in their size, shape complexity and density of acetylcholine receptors, all of which reflect a possible breakdown in communication between the diaphragmatic muscles fibers and the nerve terminals. Diaphragm muscle abnormalities were accompanied by an accumulation of mutant DMPK RNA foci in muscle fiber nuclei. Moreover, in DMSXL mice, the unmyelinated phrenic afferents are significantly lower. Also in these mice, significant neuronopathy was not detected in either cervical phrenic motor neurons or brainstem respiratory neurons. Because EPs are involved in the transmission of action potentials and the unmyelinated phrenic afferents exert a modulating influence on the respiratory drive, the pathological alterations affecting these structures might underlie the respiratory impairment detected in DMSXL mice. Understanding mechanisms of respiratory deficiency should guide pharmaceutical and clinical research towards better therapy for the respiratory deficits associated with DM1.

Molecular detection and identification of zygomycetes species from paraffin-embedded tissues in a murine model of disseminated zygomycosis: a collaborative European Society of Clinical Microbiology and Infectious Diseases (ESCMID) Fungal Infection Study Group (EFISG) evaluation.

The present study was performed to assess the interlaboratory reproducibility of the molecular detection and identification of species of Zygomycetes from formalin-fixed paraffin-embedded kidney and brain tissues obtained from experimentally infected mice. Animals were infected with one of five species (Rhizopus oryzae, Rhizopus microsporus, Lichtheimia corymbifera, Rhizomucor pusillus, and Mucor circinelloides). Samples with 1, 10, or 30 slide cuts of the tissues were prepared from each paraffin block, the sample identities were blinded for analysis, and the samples were mailed to each of seven laboratories for the assessment of sensitivity. A protocol describing the extraction method and the PCR amplification procedure was provided. The internal transcribed spacer 1 (ITS1) region was amplified by PCR with the fungal universal primers ITS1 and ITS2 and sequenced. As negative results were obtained for 93% of the tissue specimens infected by M. circinelloides, the data for this species were excluded from the analysis. Positive PCR results were obtained for 93% (52/56), 89% (50/56), and 27% (15/56) of the samples with 30, 10, and 1 slide cuts, respectively. There were minor differences, depending on the organ tissue, fungal species, and laboratory. Correct species identification was possible for 100% (30 cuts), 98% (10 cuts), and 93% (1 cut) of the cases. With the protocol used in the present study, the interlaboratory reproducibility of ITS sequencing for the identification of major Zygomycetes species from formalin-fixed paraffin-embedded tissues can reach 100%, when enough material is available.

Biologically motivated model for object detection and identification in real-world scenes

The classical computer vision methods can only weakly emulate some of the multi-level parallelisms in signal processing and information sharing that takes place in different parts of the primates’ visual system thus enabling it to accomplish many diverse functions of visual perception. One of the main functions of the primates’ vision is to detect and recognise objects in natural scenes despite all the linear and non-linear variations of the objects and their environment. The superior performance of the primates’ visual system compared to what machine vision systems have been able to achieve to date, motivates scientists and researchers to further explore this area in pursuit of more efficient vision systems inspired by natural models. In this paper building blocks for a hierarchical efficient object recognition model are proposed. Incorporating the attention-based processing would lead to a system that will process the visual data in a non-linear way focusing only on the regions of interest and hence reducing the time to achieve real-time performance. Further, it is suggested to modify the visual cortex model for recognizing objects by adding non-linearities in the ventral path consistent with earlier discoveries as reported by researchers in the neuro-physiology of vision.

A tunable radial basis function model for nonlinear system identification using particle swarm optimisation

A tunable radial basis function (RBF) network model is proposed for nonlinear system identification using particle swarm optimisation (PSO). At each stage of orthogonal forward regression (OFR) model construction, PSO optimises one RBF unit's centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automatically determines how many tunable RBF nodes are sufficient for modelling. Compared with the-state-of-the-art local regularisation assisted orthogonal least squares algorithm based on the LOO MSE criterion for constructing fixed-node RBF network models, the PSO tuned RBF model construction produces more parsimonious RBF models with better generalisation performance and is computationally more efficient.

Model selection approaches for non-linear system identification: a review

The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.

Bayesian model averaging and identification of structural breaks in time series

Bayesian Model Averaging (BMA) is used for testing for multiple break points in univariate series using conjugate normal-gamma priors. This approach can test for the number of structural breaks and produce posterior probabilities for a break at each point in time. Results are averaged over specifications including: stationary; stationary around trend and unit root models, each containing different types and number of breaks and different lag lengths. The procedures are used to test for structural breaks on 14 annual macroeconomic series and 11 natural resource price series. The results indicate that there are structural breaks in all of the natural resource series and most of the macroeconomic series. Many of the series had multiple breaks. Our findings regarding the existence of unit roots, having allowed for structural breaks in the data, are largely consistent with previous work.

Fast identification algorithms for Gaussian process model

A class identification algorithms is introduced for Gaussian process(GP)models.The fundamental approach is to propose a new kernel function which leads to a covariance matrix with low rank,a property that is consequently exploited for computational efficiency for both model parameter estimation and model predictions.The objective of either maximizing the marginal likelihood or the Kullback–Leibler (K–L) divergence between the estimated output probability density function(pdf)and the true pdf has been used as respective cost functions.For each cost function,an efficient coordinate descent algorithm is proposed to estimate the kernel parameters using a one dimensional derivative free search, and noise variance using a fast gradient descent algorithm. Numerical examples are included to demonstrate the effectiveness of the new identification approaches.

Predictive Usefulness of Urinary Biomarkers for the Identification of Cyclosporine A-Induced Nephrotoxicity in a Rat Model

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)