Adversarial Stacked Auto-Encoders for Fair Representation Learning

Training machine learning models with the only accuracy as a final goal may promote prejudices and discriminatory behaviors embedded in the data. One solution is to learn latent representations that fulfill specific fairness metrics. Different types of learning methods are employed to map data into the fair representational space. The main purpose is to learn a latent representation of data that scores well on a fairness metric while maintaining the usability for the downstream task. In this paper, we propose a new fair representation learning approach that leverages different levels of representation of data to tighten the fairness bounds of the learned representation. Our results show that stacking different auto-encoders and enforcing fairness at different latent spaces result in an improvement of fairness compared to other existing approaches.Comment: ICML2021 ML4data Workshop Pape

On the Fairness of Generative Adversarial Networks (GANs)

Generative adversarial networks (GANs) are one of the greatest advances in AI in recent years. With their ability to directly learn the probability distribution of data, and then sample synthetic realistic data. Many applications have emerged, using GANs to solve classical problems in machine learning, such as data augmentation, class unbalance problems, and fair representation learning. In this paper, we analyze and highlight fairness concerns of GANs model. In this regard, we show empirically that GANs models may inherently prefer certain groups during the training process and therefore they're not able to homogeneously generate data from different groups during the testing phase. Furthermore, we propose solutions to solve this issue by conditioning the GAN model towards samples' group or using ensemble method (boosting) to allow the GAN model to leverage distributed structure of data during the training phase and generate groups at equal rate during the testing phase.Comment: submitted to International Joint Conference on Neural Networks (IJCNN) 202

Effect of a collector bag for measurement of postpartum blood loss after vaginal delivery: cluster randomised trial in 13 European countries

Objective To evaluate the effectiveness of the systematic use of a transparent plastic collector bag to measure postpartum blood loss after vaginal delivery in reducing the incidence of severe postpartum haemorrhage

Computational Design and Analysis of a Transonic Natural Laminar Flow Wing for a Wind Tunnel Model

A natural laminar flow (NLF) wind tunnel model has been designed and analyzed for a wind tunnel test in the National Transonic Facility (NTF) at the NASA Langley Research Center. The NLF design method is built into the CDISC design module and uses a Navier-Stokes flow solver, a boundary layer profile solver, and stability analysis and transition prediction software. The NLF design method alters the pressure distribution to support laminar flow on the upper surface of wings with high sweep and flight Reynolds numbers. The method addresses transition due to attachment line contamination/transition, Gortler vortices, and crossflow and Tollmien-Schlichting modal instabilities. The design method is applied to the wing of the Common Research Model (CRM) at transonic flight conditions. Computational analysis predicts significant extents of laminar flow on the wing upper surface, which results in drag savings. A 5.2 percent scale semispan model of the CRM NLF wing will be built and tested in the NTF. This test will aim to validate the NLF design method, as well as characterize the laminar flow testing capabilities in the wind tunnel facility

Understanding Antipsychotic Drug Use in the Nursing Home Setting

Introduction: The increasing prevalence of antipsychotic medication use in residents of nursing homes (NH) in the absence of psychiatric diagnoses is concerning. To address these concerns, it is essential to explore how these medications are being prescribed and managed in the NH setting. Our objectives were to understand the decision-making process that influences prescribing and factors that trigger administration of antipsychotic medications to residents with dementia in NHs and to explore why residents remain on antipsychotic medications over an extended period of time. Methods: Interviews with prescribers, caregivers, and family members, on-site observations in study facilities, and review of NH resident medical records. Facilities were selected to obtain a diverse sample of NHs. Results: 204 NH residents with dementia in 26 facilities distributed across five selected Centers for Medicaid and Medicare Services regions were included. Problematic behaviors were the dominant reasons offered as influencing prescribing of antipsychotic medications. Providers indicated that they chose an antipsychotic, rather than another drug class, because they believed that antipsychotic medications were more likely to be effective. There was no standard approach to taper attempts. Family members identified a lack of communication as a barrier to their involvement in decision-making. Conclusions: There is widespread perception that antipsychotic medications are effective and beneficial in managing problematic behaviors in NH residents with dementia. Little attention is given to planning for antipsychotic tapering or discontinuation. There may be opportunities to involve family members more fully in decision-making around the use of antipsychotic medications

Optimizing management of invasions in an uncertain world using dynamic spatial models

Dispersal drives invasion dynamics of nonnative species and pathogens. Applying knowledge of dispersal to optimize the management of invasions can mean the difference between a failed and a successful control program and dramatically improve the return on investment of control efforts. A common approach to identifying optimal management solutions for invasions is to optimize dynamic spatial models that incorporate dispersal. Optimizing these spatial models can be very challenging because the interaction of time, space, and uncertainty rapidly amplifies the number of dimensions being considered. Addressing such problems requires advances in and the integration of techniques from multiple fields, including ecology, decision analysis, bioeconomics, natural resource management, and optimization. By synthesizing recent advances from these diverse fields, we provide a workflow for applying ecological theory to advance optimal management science and highlight priorities for optimizing the control of invasions. One of the striking gaps we identify is the extremely limited consideration of dispersal uncertainty in optimal management frameworks, even though dispersal estimates are highly uncertain and greatly influence invasion outcomes. In addition, optimization frameworks rarely consider multiple types of uncertainty (we describe five major types) and their interrelationships. Thus, feedbacks from management or other sources that could magnify uncertainty in dispersal are rarely considered. Incorporating uncertainty is crucial for improving transparency in decision risks and identifying optimal management strategies. We discuss gaps and solutions to the challenges of optimization using dynamic spatial models to increase the practical application of these important tools and improve the consistency and robustness of management recommendations for invasions

Progress in Classical and Quantum Variational Principles

We review the development and practical uses of a generalized Maupertuis least action principle in classical mechanics, in which the action is varied under the constraint of fixed mean energy for the trial trajectory. The original Maupertuis (Euler-Lagrange) principle constrains the energy at every point along the trajectory. The generalized Maupertuis principle is equivalent to Hamilton's principle. Reciprocal principles are also derived for both the generalized Maupertuis and the Hamilton principles. The Reciprocal Maupertuis Principle is the classical limit of Schr\"{o}dinger's variational principle of wave mechanics, and is also very useful to solve practical problems in both classical and semiclassical mechanics, in complete analogy with the quantum Rayleigh-Ritz method. Classical, semiclassical and quantum variational calculations are carried out for a number of systems, and the results are compared. Pedagogical as well as research problems are used as examples, which include nonconservative as well as relativistic systems

Quality of life and neck pain in nurses

Objectives: To investigate the association between neck pain and psychological stress in nurses. Material and Methods: Nurses from the Avon Orthopaedic Centre completed 2 questionnaires: the Short Form-36 (SF-36) and 1 exploring neck pain and associated psychological stress. Results: Thirty four nurses entered the study (68% response). Twelve (35.3%) had current neck pain, 13 (38.2%) reported neck pain within the past year and 9 (26.5%) had no neck pain. Subjects with current neck pain had significantly lower mental health (47.1 vs. 70.4; p = 0.002), physical health (60.8 vs. 76.8; p = 0.010) and overall SF-36 scores (56.8 vs. 74.9; p = 0.003). Five (41.7%) subjects with current neck pain and 5 (38.5%) subjects with neck pain in the previous year attributed it to psychological stress. Conclusions: Over 1/3 of nurses have symptomatic neck pain and significantly lower mental and physical health scores. Managing psychological stress may reduce neck pain, leading to improved quality of life for nurses, financial benefits for the NHS, and improved patient care