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.

Identification and bisection of temporal durations and tone frequencies: Common models for temporal and non-temporal stimuli.

Two experiments examined identification and bisection of tones varying in temporal duration (Experiment 1) or frequency (Experiment 2). Absolute identification of both durations and frequencies was influenced by prior stimuli and by stimulus distribution. Stimulus distribution influenced bisection for both stimulus types consistently, with more positively skewed distributions producing lower bisection points. The effect of distribution was greater when the ratio of the largest to smallest stimulus magnitude was greater. A simple mathematical model, temporal range frequency theory, was applied. It is concluded that (a) similar principles describe identification of temporal durations and other stimulus dimensions and (b) temporal bisection point shifts can be understood in terms of psychophysical principles independently developed in nontemporal domains, such as A. Parducci's (1965) range frequency theory.

DFT and in situ EXAFS investigation of gold/ceria-zirconia low-temperature water gas shift catalysts: Identification of the nature of the active form of gold

A combined experimental and theoretical investigation of the nature of the active form of gold in oxide-supported gold catalysts for the water gas shift reaction has been performed. In situ extended X-ray absorption fine structure (EXAFS) and X-ray absorption near-edge structure (XANES) experiments have shown that in the fresh catalysts the gold is in the form of highly dispersed gold ions. However, under water gas shift reaction conditions, even at temperatures as low as 100 degrees C, the evidence from EXAFS and XANES is only 14 consistent with rapid, and essentially complete, reduction of the gold to form metallic clusters containing about 50 atoms. The presence of Au-Ce distances in the EXAFS spectra, and the fact that about 15% of the gold atoms can be reoxidized after exposure to air at 150 degrees C, is indicative of a close interaction between a fraction (ca. 15%) of the gold atoms and the oxide support. Density functional theory (DFT) calculations are entirely consistent with this model and suggest that an important aspect of the active and stable form of gold under water gas shift reaction conditions is the location of a partially oxidized gold (Audelta+) species at a cerium cation vacancy in the surface of the oxide support. It is found that even with a low loading gold catalysts (0.2%) the fraction of ionic gold under water gas shift conditions is below the limit of detection by XANES (<5%). It is concluded that under water gas shift reaction conditions the active form of gold comprises small metallic gold clusters in intimate contact with the oxide support.

Economic modelling of antenatal screening and ultrasound scanning programmes for identification of fetal abnormalities

Objective Within the framework of a health technology assessment and using an economic model, to determine the most clinically and cost effective policy of scanning and screening for fetal abnormalities in early pregnancy. Design A discrete event simulation model of 50,000 singleton pregnancies. Setting Maternity services in Scotland. Population Women during the first 24 weeks of their pregnancy. Methods The mathematical model was populated with data on uptake of screening, prevalence, detection and false positive rates for eight fetal abnormalities and with costs for ultrasound scanning and serum screening. Inclusion of abnormalities was based on the relative prevalence and clinical importance of conditions and the availability of data. Six strategies for the identification of abnormalities prenatally including combinations of first and second trimester ultrasound scanning and first and second trimester screening for chromosomal abnormalities were compared. Main outcome measures The number of abnormalities detected and missed, the number of iatrogenic losses resulting from invasive tests, the total cost of strategies and the cost per abnormality detected were compared between strategies. Results First trimester screening for chromosomal abnormalities costs more than second trimester screening but results in fewer iatrogenic losses. Strategies which include a second trimester ultrasound scan result in more abnormalities being detected and have lower costs per anomaly detected. Conclusions The preferred strategy includes both first and second trimester ultrasound scans and a first trimester screening test for chromosomal abnormalities. It has been recommended that this policy is offered to all women in Scotland.

Dynamic Multivariate Statistical Process Control using Subspace Identification

This paper points out a serious flaw in dynamic multivariate statistical process control (MSPC). The principal component analysis of a linear time series model that is employed to capture auto- and cross-correlation in recorded data may produce a considerable number of variables to be analysed. To give a dynamic representation of the data (based on variable correlation) and circumvent the production of a large time-series structure, a linear state space model is used here instead. The paper demonstrates that incorporating a state space model, the number of variables to be analysed dynamically can be considerably reduced, compared to conventional dynamic MSPC techniques.

A two-stage algorithm for identification of nonlinear dynamic systems

This paper investigates the two-stage stepwise identification for a class of nonlinear dynamic systems that can be described by linear-in-the-parameters models, and the model has to be built from a very large pool of basis functions or model terms. The main objective is to improve the compactness of the model that is obtained by the forward stepwise methods, while retaining the computational efficiency. The proposed algorithm first generates an initial model using a forward stepwise procedure. The significance of each selected term is then reviewed at the second stage and all insignificant ones are replaced, resulting in an optimised compact model with significantly improved performance. The main contribution of this paper is that these two stages are performed within a well-defined regression context, leading to significantly reduced computational complexity. The efficiency of the algorithm is confirmed by the computational complexity analysis, and its effectiveness is demonstrated by the simulation results.

Making 'party identification' more versatile: operationalising the concept for the multiparty setting

The conventional operationalisation of the concept of party identification is not appropriate for the multiparty setting. I offer new measures that facilitate multiple, and negative as well as positive, identities. Using survey evidence from Northern Ireland, these new measures are validated in a number of ways and their role in a comprehensive model of voting is illustrated.

Identification of a novel population of Langerin+ dendritic cells.

Langerhans cells (LCs) are antigen-presenting cells that reside in the epidermis of the skin and traffic to lymph nodes (LNs). The general role of these cells in skin immune responses is not clear because distinct models of LC depletion resulted in opposite conclusions about their role in contact hypersensitivity (CHS) responses. While comparing these models, we discovered a novel population of LCs that resides in the dermis and does not represent migrating epidermal LCs, as previously thought. Unlike epidermal LCs, dermal Langerin(+) dendritic cells (DCs) were radiosensitive and displayed a distinct cell surface phenotype. Dermal Langerin(+) DCs migrate from the skin to the LNs after inflammation and in the steady state, and represent the majority of Langerin(+) DCs in skin draining LNs. Both epidermal and dermal Langerin(+) DCs were depleted by treatment with diphtheria toxin in Lang-DTREGFP knock-in mice. In contrast, transgenic hLang-DTA mice lack epidermal LCs, but have normal numbers of dermal Langerin(+) DCs. CHS responses were abrogated upon depletion of both epidermal and dermal LCs, but were unaffected in the absence of only epidermal LCs. This suggests that dermal LCs can mediate CHS and provides an explanation for previous differences observed in the two-model systems.

Modelling the MAPK signalling pathway using a two-stage identification algorithm

Signal transduction pathways describe the dynamics of cellular response to input signalling molecules at receptors on the cell membrane. The Mitogen-Activated Protein Kinase (MAPK) cascade is one of such pathways that are involved in many important cellular processes including cell growth and proliferation. This paper describes a black-box model of this pathway created using an advanced two-stage identification algorithm. Identification allows us to capture the unique features and dynamics of the pathway and also opens up the possibility of regulatory control design. In the approach described, an optimal model is obtained by performing model subset selection in two stages, where the terms are first determined by a forward selection method and then modified using a backward selection model refinement. The simulation results demonstrate that the model selected using the two-stage algorithm performs better than with the forward selection method alone.

Comparison of two construction algorithms for local model networks

The divide-and-conquer approach of local model (LM) networks is a common engineering approach to the identification of a complex nonlinear dynamical system. The global representation is obtained from the weighted sum of locally valid, simpler sub-models defined over small regions of the operating space. Constructing such networks requires the determination of appropriate partitioning and the parameters of the LMs. This paper focuses on the structural aspect of LM networks. It compares the computational requirements and performances of the Johansen and Foss (J&F) and LOLIMOT tree-construction algorithms. Several useful and important modifications to each algorithm are proposed. The modelling performances are evaluated using real data from a pilot plant of a pH neutralization process. Results show that while J&F achieves a more accurate nonlinear representation of the pH process, LOLIMOT requires significantly less computational effort.