Residual Minimizing Model Interpolation for Parameterized Nonlinear Dynamical Systems

We present a method for approximating the solution of a parameterized, nonlinear dynamical system using an affine combination of solutions computed at other points in the input parameter space. The coefficients of the affine combination are computed with a nonlinear least squares procedure that minimizes the residual of the governing equations. The approximation properties of this residual minimizing scheme are comparable to existing reduced basis and POD-Galerkin model reduction methods, but its implementation requires only independent evaluations of the nonlinear forcing function. It is particularly appropriate when one wishes to approximate the states at a few points in time without time marching from the initial conditions. We prove some interesting characteristics of the scheme including an interpolatory property, and we present heuristics for mitigating the effects of the ill-conditioning and reducing the overall cost of the method. We apply the method to representative numerical examples from kinetics - a three state system with one parameter controlling the stiffness - and conductive heat transfer - a nonlinear parabolic PDE with a random field model for the thermal conductivity.Comment: 28 pages, 8 figures, 2 table

Is intimate partner violence more common in pregnant women with severe mental illness? A retrospective study

Objective: To examine the risk of past and current experiences of intimate partner violence (IPV) in women with severe mental illness (SMI) in pregnancy. Methods: We examined past and current experiences of IPV in women with SMI in pregnancy. The data of 304 women with SMI including schizophrenia and related psychotic disorders and Bipolar Disorder meeting International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification (ICD-10-AM) criteria were extracted from hospital records at King Edward Memorial Hospital, Western Australia. Comparisons were made between our study data and the Australian population data reported by the Australian Bureau of Statistics, which included data on pregnant women in Western Australia. Additional measures included reported demographics, substance use and pregnancy variables. Results: Around 48% of pregnant women with SMI had experienced IPV and were three times the risk when compared with the general pregnant population in Australia. There was no difference in rates of IPV in those women with psychotic disorders when compared with bipolar disorder. Furthermore, the rates of smoking and illicit substance use were significantly higher in pregnant women with SMI who experienced IPV compared with those who have not experienced IPV. Conclusion: These findings suggest women with SMI in pregnancy are at significantly higher risk of having experienced or experiencing IPV. In addition, IPV in pregnant women with SMI may increase the risk of smoking and illicit substance use. Together this suggests that maternity and mental health services should ensure there are both screening and support pathways for IPV that are developed and evaluated specifically for pregnant women with SMI

The global atmospheric budget of ethanol revisited

Ethanol is an important biogenic volatile organic compound, which is increasingly used as a fuel for motor vehicles; therefore, an improved understanding of its atmospheric cycle is important. In this paper we use three sets of observational data, measured emissions of ethanol from living plants, measured concentrations of ethanol in the atmosphere and measured hydroxyl concentrations in the atmosphere (by methyl chloroform titration), to make two independent estimates related to the rate of cycling of ethanol through the atmosphere. In the first estimate, simple calculations give the emission rate of ethanol from living plants as 26 (range, 10–38) Tg yr<sup>−1</sup>. This contributes significantly to the total global ethanol source of 42 (range, 25–56) Tg yr<sup>−1</sup>. In the second estimate, the total losses of ethanol from the global atmosphere are 70 (range, 50–90) Tg yr<sup>−1</sup>, with about three-quarters of the ethanol removed by reaction with hydroxyl radicals in the gaseous and aqueous phases of the atmosphere, and the remainder lost through wet and dry deposition to land. These values of both the source of ethanol from living plants and the removal of atmospheric ethanol via oxidation by hydroxyl radicals (derived entirely from observations) are significantly larger than those in recent literature. We suggest that a revision of the estimate of global ethanol emissions from plants to the atmosphere to a value comparable with this analysis is warranted

Image quality assessment for fake biometric detection: Application to Iris, fingerprint, and face recognition

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.To ensure the actual presence of a real legitimate trait in contrast to a fake self-manufactured synthetic or reconstructed sample is a significant problem in biometric authentication, which requires the development of new and efficient protection measures. In this paper, we present a novel software-based fake detection method that can be used in multiple biometric systems to detect different types of fraudulent access attempts. The objective of the proposed system is to enhance the security of biometric recognition frameworks, by adding liveness assessment in a fast, user-friendly, and non-intrusive manner, through the use of image quality assessment. The proposed approach presents a very low degree of complexity, which makes it suitable for real-time applications, using 25 general image quality features extracted from one image (i.e., the same acquired for authentication purposes) to distinguish between legitimate and impostor samples. The experimental results, obtained on publicly available data sets of fingerprint, iris, and 2D face, show that the proposed method is highly competitive compared with other state-of-the-art approaches and that the analysis of the general image quality of real biometric samples reveals highly valuable information that may be very efficiently used to discriminate them from fake traits.This work has been partially supported by projects Contexts (S2009/TIC-1485) from CAM, Bio-Shield (TEC2012-34881) from Spanish MECD, TABULA RASA (FP7-ICT-257289) and BEAT (FP7-SEC-284989) from EU, and Cátedra UAM-Telefónic

FaceQnet: Quality Assessment for Face Recognition based on Deep Learning

In this paper we develop a Quality Assessment approach for face recognition based on deep learning. The method consists of a Convolutional Neural Network, FaceQnet, that is used to predict the suitability of a specific input image for face recognition purposes. The training of FaceQnet is done using the VGGFace2 database. We employ the BioLab-ICAO framework for labeling the VGGFace2 images with quality information related to their ICAO compliance level. The groundtruth quality labels are obtained using FaceNet to generate comparison scores. We employ the groundtruth data to fine-tune a ResNet-based CNN, making it capable of returning a numerical quality measure for each input image. Finally, we verify if the FaceQnet scores are suitable to predict the expected performance when employing a specific image for face recognition with a COTS face recognition system. Several conclusions can be drawn from this work, most notably: 1) we managed to employ an existing ICAO compliance framework and a pretrained CNN to automatically label data with quality information, 2) we trained FaceQnet for quality estimation by fine-tuning a pre-trained face recognition network (ResNet-50), and 3) we have shown that the predictions from FaceQnet are highly correlated with the face recognition accuracy of a state-of-the-art commercial system not used during development. FaceQnet is publicly available in GitHub.Comment: Preprint version of a paper accepted at ICB 201

An AFIS functionality for the European Criminal Records Information System: A preliminary assessment of DG JUST decentralised option supported by pseudonymised index-filter

At the request of DG JUST, the JRC presented in February 2016 this short report with a preliminary assessment of a possible successful introduction of a new Automatic Fingerprint Identification System (AFIS) functionality in ECRIS. The report provides a technical assessment of the proposed decentralized privacy-preserving approach based on a pseudonymized index-filter shared by all Member States (MS) and lists the main technical and architectural challenges.JRC.E.3-Cyber and Digital Citizens' Securit

Nonlinear Model Reduction for Uncertainty Quantification in Large-Scale Inverse Problems

We present a model reduction approach to the solution of large-scale statistical inverse problems in a Bayesian inference setting. A key to the model reduction is an efficient representation of the non-linear terms in the reduced model. To achieve this, we present a formulation that employs masked projection of the discrete equations; that is, we compute an approximation of the non-linear term using a select subset of interpolation points. Further, through this formulation we show similarities among the existing techniques of gappy proper orthogonal decomposition, missing point estimation, and empirical interpolation via coefficient-function approximation. The resulting model reduction methodology is applied to a highly non-linear combustion problem governed by an advection–diffusion-reaction partial differential equation (PDE). Our reduced model is used as a surrogate for a finite element discretization of the non-linear PDE within the Markov chain Monte Carlo sampling employed by the Bayesian inference approach. In two spatial dimensions, we show that this approach yields accurate results while reducing the computational cost by several orders of magnitude. For the full three-dimensional problem, a forward solve using a reduced model that has high fidelity over the input parameter space is more than two million times faster than the full-order finite element model, making tractable the solution of the statistical inverse problem that would otherwise require many years of CPU time.MIT-Singapore Alliance. Computational Engineering ProgrammeUnited States. Air Force Office of Scientific Research (Contract Nos. FA9550-06-0271)National Science Foundation (U.S.) (Grant No. CNS-0540186)National Science Foundation (U.S.) (Grant No. CNS-0540372)Caja Madrid Foundation (Graduate Fellowship

Decisions about the use of psychotropic medication during pregnancy: a qualitative study

OBJECTIVE: To understand the perspectives of women with severe mental illness concerning the use of psychotropic medicines while pregnant. DESIGN: Interviews conducted by female peer researchers with personal experience of making or considering decisions about using psychotropic medicines in pregnancy, supported by professional researchers. PARTICIPANTS: 12 women who had had a baby in the past 5 years and had taken antipsychotics or mood-stabilisers for severe mental illness within the 12-month period immediately prior to that pregnancy. Recruitment to the study was via peer networks and the women interviewed came from different regions of England. SETTING: Interviews were arranged in places where women felt comfortable and that accommodated their childcare needs including their home, local library and the research office. RESULTS: The views expressed demonstrated complex attempts to engage with decision-making about the use of psychotropic medicines in pregnancy. In nearly all cases, the women expressed the view that healthcare professionals had access to limited information leaving women to rely on experiential and common sense evidence when making decisions about medicine taking during pregnancy. CONCLUSIONS: The findings complement existing work using electronic health records by providing explanations for the discontinuation of psychotropic medicines in pregnancy. Further work is necessary to understand health professionals’ perspectives on the provision of services and care to women with severe mental illness during pregnancy

Sobre cómo varían las firmas manuscritas con el tiempo: una modelización Sigma Lognormal

Comunicación presentada en las Jornadas de Reconocimiento Biométrico de Personas (JRBP 2013)En el presente trabajo se analiza la variación de las firmas dinámicas con el tiempo usando la Teoría Cinemática, siguiendo un protocolo general, consistente y completamente reproducible. Los experimentos se llevan a cabo sobre una nueva base de datos a largo plazo, capturada, bajo condiciones casi idénticas, en 6 sesiones uniformemente distribuidas durante un periodo de 15 meses. Las firmas se han representado con el modelo Sigma Lonormal, el cual tiene en cuenta los efectos del envejecimiento del cuerpo más relacionados con la escritura, como los tiempos de respuesta neuromusculares. Tras estudiar la evolución de las firmas con el tiempo, se ha llevado a cabo un análisis de distintos grupos de edad basado en los parámetros del modelo.Este trabajo ha sido parcialmente nanciado por los proyectos Contexts (S2009/TIC-1485) de la CAM, Bio-Challenge (TEC2009-11186) y Bio-Shield (TEC2012-34881) del MINECO, Guardia Civil y C atedra UAM-Telef onica