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

Messing up research: A dialogical account of gender, reflexivity, and governance in auto-ethnography

This paper aims to contribute to a growing critical and reflexive awareness of the implications of gendered assumptions about ontology, epistemology, and ethics in academic research governance and practice. It provides a retrospective account of the authors' shared experiences of an autoethnographic study of lap dancing clubs, focusing on critical or “sticky moments” encountered, and considering the implications of these for research more widely. It does so by highlighting the gendered power relations shaping academic research, showing how Judith Butler's critique of the heterosexual matrix can be applied to a critical, reflexive understanding of the impact of binary, hierarchical gender power relations. The analysis provides insight into some of the ways in which autoethnographic research on sexualized work may become messy, dirty, and sticky in ways that accentuate power inequalities but also open up moments of opportunity for gender binaries and hierarchies to be revealed, challenged, and resisted. Using a Butlerian lens to reflect on our experiences, we contribute to understanding how heteronormative assumptions shape perceptions of what makes “good,” “clean,” and ethically (formally) approved research that conforms to the governmental norms of the heterosexual matrix and, by implication, those contaminating forms of research that disrupt or resist its disciplinary effects. As ethnographic research is often messy by its very nature, and particularly so when situated within sex/sexualized work, we aim to show how gendered assumptions can inhibit reflexivity in academic knowledge production, resulting in research processes that are (paradoxically) unethical. In response, we suggest three ways in which gender reflexive research might be pursued, by: (i) identifying gendered assumptions reflexively and dialogically, (ii) adopting an anti-essentialist approach that foregrounds experiential, embodied knowledge, and (iii) developing an anti-hierarchical methodology. We do so in the hope of opening up ways that might enable others to avoid heteronormative assumptions having potentially detrimental consequences for their research and to offer a starting point for developing gender reflexive knowledge production in the future

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

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

Depression and parenting: The need for improved intervention models

The impact of maternal depression on parenting is well established and there is a clear interaction between maternal depression and parenting that is predictive of child outcomes. The research on paternal depression is more limited but suggests the father’s mental health may be an independent risk factor for both parenting and child outcomes. There is insufficient evidence that treatment of depression alone – be it through pharmacological or psychological interventions – is able to substantially reduce the impact of depression on child outcomes. The evidence of interventions aimed at parenting and/or child outcomes in the context of depression is limited and the findings that are available are mixed

Pilot Study of the Dynamic Early Literacy Framework for Implementation of Science of Reading Aligned Instruction

A growing, research-based consensus supports addressing our nation’s literacy crisis through instruction aligned with the Science of Reading (SoR). We recognize, however, that the complexity of SoR content, alongside the multiplicity of instructional decisions and practices, and the unique features of each classroom and school, make simple implementation designs unlikely to achieve desired results. To guide schools in developing higher levels of SoR-aligned early literacy instruction, we developed the Dynamic Early Literacy Framework (DELF). The DELF primarily serves as a framework to guide inquiry and innovation of SoR-aligned early literacy drivers, while documenting progress as well as identifying change priorities. The framework addresses growth across four defined stages for each of the seven drivers: School Leadership & Culture; Comprehensive Early Literacy Assessment System; Evidenced-Based Core and Intervention Curriculum; Evidenced-Based Instructional Practices; Supervision & Evaluation; Coaching and Professional Development; and Family and Community Engagement. This article reports findings from a one year qualitative pilot investigation of the DELF in three school sites and the K-2 central instructional support team in kindergarten through second grades. Findings suggest the use of the tool had a positive impact in framing inquiry of the current literacy model and development of action plans across various stakeholders. Discussion includes lessons learned and implications for future researc