A survey of fingerprint classification Part I: taxonomies on feature extraction methods and learning models

This paper reviews the fingerprint classification literature looking at the problem from a double perspective. We first deal with feature extraction methods, including the different models considered for singular point detection and for orientation map extraction. Then, we focus on the different learning models considered to build the classifiers used to label new fingerprints. Taxonomies and classifications for the feature extraction, singular point detection, orientation extraction and learning methods are presented. A critical view of the existing literature have led us to present a discussion on the existing methods and their drawbacks such as difficulty in their reimplementation, lack of details or major differences in their evaluations procedures. On this account, an experimental analysis of the most relevant methods is carried out in the second part of this paper, and a new method based on their combination is presented.

Vibrato in singing voice: the link between source-filter and sinusoidal models

The application of inverse filtering techniques for high-quality singing voice analysis/synthesis is discussed. In the context of source-filter models, inverse filtering provides a noninvasive method to extract the voice source, and thus to study voice quality. Although this approach is widely used in speech synthesis, this is not the case in singing voice. Several studies have proved that inverse filtering techniques fail in the case of singing voice, the reasons being unclear. In order to shed light on this problem, we will consider here an additional feature of singing voice, not present in speech: the vibrato. Vibrato has been traditionally studied by sinusoidal modeling. As an alternative, we will introduce here a novel noninteractive source filter model that incorporates the mechanisms of vibrato generation. This model will also allow the comparison of the results produced by inverse filtering techniques and by sinusoidal modeling, as they apply to singing voice and not to speech. In this way, the limitations of these conventional techniques, described in previous literature, will be explained. Both synthetic signals and singer recordings are used to validate and compare the techniques presented in the paper.

Simultaneous Learning of Nonlinear Manifold and Dynamical Models for High-dimensional Time Series

The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.

Simultaneous Learning of Non-Linear Manifold and Dynamical Models for High-Dimensional Time Series

The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. The model structure setup and parameter learning are done using a variational Bayesian approach, which enables automatic Bayesian model structure selection, hence solving the problem of over-fitting. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.

Differential and numerical models of hysteretic systems with stochastic and deterministic inputs

Many deterministic models with hysteresis have been developed in the areas of economics, finance, terrestrial hydrology and biology. These models lack any stochastic element which can often have a strong effect in these areas. In this work stochastically driven closed loop systems with hysteresis type memory are studied. This type of system is presented as a possible stochastic counterpart to deterministic models in the areas of economics, finance, terrestrial hydrology and biology. Some price dynamics models are presented as a motivation for the development of this type of model. Numerical schemes for solving this class of stochastic differential equation are developed in order to examine the prototype models presented. As a means of further testing the developed numerical schemes, numerical examination is made of the behaviour near equilibrium of coupled ordinary differential equations where the time derivative of the Preisach operator is included in one of the equations. A model of two phenotype bacteria is also presented. This model is examined to explore memory effects and related hysteresis effects in the area of biology. The memory effects found in this model are similar to that found in the non-ideal relay. This non-ideal relay type behaviour is used to model a colony of bacteria with multiple switching thresholds. This model contains a Preisach type memory with a variable Preisach weight function. Shown numerically for this multi-threshold model is a pattern formation for the distribution of the phenotypes among the available thresholds.

Integration of micro- and macroscopic models for pedestrian evacuation simulation

Simulation of pedestrian evacuations of smart buildings in emergency is a powerful tool for building analysis, dynamic evacuation planning and real-time response to the evolving state of evacuations. Macroscopic pedestrian models are low-complexity models that are and well suited to algorithmic analysis and planning, but are quite abstract. Microscopic simulation models allow for a high level of simulation detail but can be computationally intensive. By combining micro- and macro- models we can use each to overcome the shortcomings of the other and enable new capability and applications for pedestrian evacuation simulation that would not be possible with either alone. We develop the EvacSim multi-agent pedestrian simulator and procedurally generate macroscopic flow graph models of building space, integrating micro- and macroscopic approaches to simulation of the same emergency space. By “coupling” flow graph parameters to microscopic simulation results, the graph model captures some of the higher detail and fidelity of the complex microscopic simulation model. The coupled flow graph is used for analysis and prediction of the movement of pedestrians in the microscopic simulation, and investigate the performance of dynamic evacuation planning in simulated emergencies using a variety of strategies for allocation of macroscopic evacuation routes to microscopic pedestrian agents. The predictive capability of the coupled flow graph is exploited for the decomposition of microscopic simulation space into multiple future states in a scalable manner. By simulating multiple future states of the emergency in short time frames, this enables sensing strategy based on simulation scenario pattern matching which we show to achieve fast scenario matching, enabling rich, real-time feedback in emergencies in buildings with meagre sensing capabilities.

Practice-based personas: integrated user and practice models

Practices are routinised behaviours with social and material components and complex relationships over space and time. Practice-based design goes beyond interaction design to consider how these components and their relationships impact on the formation and enactment of a practice, where technology is just one part of the practice. Though situated user-centred design methods such as participatory design are employed for the design of practice, demand exists for additional methods and tools in this area. This paper introduces practice-based personas as an extension of the persona approach popular in interaction design, and demonstrates how a set of practice-based personas was developed for a given domain – academic practice. The three practice-based personas developed here are linked to a catalogue of forty practices, offering designers both a user perspective and a practice perspective when designing for the domain.

Reason, risk, and reward: Models for libraries and other stakeholders in an evolving scholarly publishing ecosystem

Scholarly publishing, and scholarly communication more generally, are based on patterns established over many decades and even centuries. Some of these patterns are clearly valuable and intimately related to core values of the academy, but others were based on the exigencies of the past, and new opportunities have brought into question whether it makes sense to persist in supporting old models. New technologies and new publishing models raise the question of how we should fund and operate scholarly publishing and scholarly communication in the future, moving away from a scarcity model based on the exchange of physical goods that restricts access to scholarly literature unless a market-based exchange takes place. This essay describes emerging models that attempt to shift scholarly communication to a more open-access and mission-based approach and that try to retain control of scholarship by academics and the institutions and scholarly societies that support them. It explores changing practices for funding scholarly journals and changing services provided by academic libraries, changes instituted with the end goal of providing more access to more readers, stimulating new scholarship, and removing inefficiencies from a system ready for change. © 2014 by the American Anthropological Association.

Making modelling count - increasing the contribution of shelf-seas community and ecosystem models to policy development and management

Marine legislation is becoming more complex and marine ecosystem-based management is specified in national and regional legislative frameworks. Shelf-seas community and ecosystem models (hereafter termed ecosystem models) are central to the delivery of ecosystem-based management, but there is limited uptake and use of model products by decision makers in Europe and the UK in comparison with other countries. In this study, the challenges to the uptake and use of ecosystem models in support of marine environmental management are assessed using the UK capability as an example. The UK has a broad capability in marine ecosystem modelling, with at least 14 different models that support management, but few examples exist of ecosystem modelling that underpin policy or management decisions. To improve understanding of policy and management issues that can be addressed using ecosystem models, a workshop was convened that brought together advisors, assessors, biologists, social scientists, economists, modellers, statisticians, policy makers, and funders. Some policy requirements were identified that can be addressed without further model development including: attribution of environmental change to underlying drivers, integration of models and observations to develop more efficient monitoring programmes, assessment of indicator performance for different management goals, and the costs and benefit of legislation. Multi-model ensembles are being developed in cases where many models exist, but model structures are very diverse making a standardised approach of combining outputs a significant challenge, and there is a need for new methodologies for describing, analysing, and visualising uncertainties. A stronger link to social and economic systems is needed to increase the range of policy-related questions that can be addressed. It is also important to improve communication between policy and modelling communities so that there is a shared understanding of the strengths and limitations of ecosystem models.

Investigation of the effect of intrauterine inflammation and infection on fetal brain injury using human and animal models

In recent years, increased focus has been placed on the role of intrauterine infection and inflammation in the pathogenesis of fetal brain injury leading to neurodevelopmental disorders such as cerebral palsy. At present, the mechanisms by which inflammatory processes during pregnancy cause this effect on the fetus are poorly understood. Our previous work has indicated an association between experimentally-induced intrauterine infection, increased proinflammatory cytokines, and increased white matter injury in the guinea pig fetus. In order to further elucidate the pathways by which inflammation in the maternal system or the fetal membranes leads to fetal impairment, a number of studies investigating aspects of the disease process have been performed. These studies represent a body of work encompassing novel research and results in a number of human and animal studies. Using a guinea pig model of inflammation, increased amniotic fluid proinflammatory cytokines and fetal brain injury were found after a maternal inflammatory response was initiated using endotoxin. In order to more closely monitor the fetal response to chorioamnionitis, a model using the chronically catheterized fetal ovine was carried out. This study demonstrated the adverse effects on fetal white matter after intrauterine exposure to bacterial inoculation, though the physiological parameters of the fetus were relatively stable throughout the experimental protocol, even when challenged with intermittent hypoxic episodes. The placenta is an important mediator between mother and fetus during gestation, though its role in the inflammatory process is largely undefined. Studies on the placental role in the inflammatory process were undertaken, and the limited ability of proinflammatory cytokines and endotoxin to cross the placenta are detailed herein. Neurodevelopmental disorders can be monitored in animal models in order to determine effective disease models for characterization of injury and use in therapeutic strategies. Our characterizations of postnatal behaviour in the guinea pig model using motility monitoring and spatial memory testing have shown small but significant differences in pups exposed to inflammatory processes in utero. The data presented herein contributes a breadth of knowledge to the ongoing elucidation of the pathways by which fetal brain injury occurs. Determining the pathway of damage will lead to discovery of diagnostic criteria, while determining the vulnerabilities of the developing fetus is essential in formulating therapeutic options.

Fast and frugal models of clinical judgement in novice and expert physicians

Our objective was to study whether “compensatory” models provide better descriptions of clinical judgment than fast and frugal models, according to expertise and experience. Fifty practitioners appraised 60 vignettes describing a child with an exacerbation of asthma and rated their propensities to admit the child. Linear logistic (LL) models of their judgments were compared with a matching heuristic (MH) model that searched available cues in order of importance for a critical value indicating an admission decision. There was a small difference between the 2 models in the proportion of patients allocated correctly (admit or not-admit decisions), 91.2% and 87.8%, respectively. The proportion allocated correctly by the LL model was lower for consultants than juniors, whereas the MH model performed equally well for both. In this vignette study, neither model provided any better description of judgments made by consultants or by pediatricians compared to other grades and specialties.

IMPACT OF RNA VIRUSES ON THE REGULATION OF IL-23 IN MOUSE AND HUMAN MODELS OF INFECTION.

Interluekin-23 (IL-23) is a pro-inflammatory cytokine critical to the regulation of innate and adaptive immune responses. The main role for this cytokine is in the proliferation and differentiation of the IL-17 producing CD4 T helper cell, Th17. Virus infection deregulates IL-23 expression and function, but little is known about the mechanism behind this phenomena. Here, I demonstrate a reduction of Toll like receptor (TLR) ligand-induced IL-23 expression in lymphocytic choriomeningitis virus (LCMV)-infected bone marrow-derived dendritic cells (BMDCs), indicating that a function of these cells is disrupted during virus infection. I propose a mechanism of TLR ligand-induced IL-23 expression inhibition upon LCMV infection via the deactivation of p38, AP-1, and NF-κB. Further analysis revealed a direct relationship between LCMV infection with the IL-10 and SOCS3 expression. To understand IL-23 function, I characterized IL-23-induced JAK/STAT signalling pathway and IL-23 receptor expression on human CD4 T cells. My results demonstrate that IL-23 induces activation of p-JAK2, p-Tyk2, p-STAT1, p-STAT3, and p-STAT4 in CD4 T cells. For the first time I show that IL-23 alone induces the expression of its own receptor components, IL-12Rβ1 and IL-23Rα, in CD4 T cells. Blocking JAK2, STAT1, and STAT3 activation with specific inhibitors detrimentally effected expression of IL-23 receptor demonstrating that activation of JAK/STAT signalling is important for IL-23 receptor expression. I also addressed the effect of viral infection on IL-23 function and receptor expression in CD4 T cells using cells isolated from HIV positive individuals. These studies were based on earlier reports that the expression of IL-23 and the IL-23 receptor are impaired during HIV infection. I demonstrate that the phosphorylation of JAK2, STAT1, and STAT3 induced by IL-23, as well as IL-23 receptor expression are deregulated in CD4 T cells isolated from HIV positive individuals. This study has furthered the understanding of how the expression and function of IL-23 is regulated during viral infections.