Matching With Doses in an Observational Study of a Media Campaign Against Drug Abuse

Multivariate matching with doses of treatment differs from the treatment-control matching in three ways. First, pairs must not only balance covariates, but also must differ markedly in dose. Second, any two subjects may be paired, so that the matching is nonbipartite, and different algorithms are required. Finally, a propensity score with doses must be used in place of the conventional propensity score. We illustrate multivariate matching with doses using pilot data from a media campaign against drug abuse. The media campaign is intended to change attitudes and intentions related to illegal drugs, and the evaluation compares stated intentions among ostensibly comparable teens who reported markedly different exposures to the media campaign

Applying machine learning to the problem of choosing a heuristic to select the variable ordering for cylindrical algebraic decomposition

Cylindrical algebraic decomposition(CAD) is a key tool in computational algebraic geometry, particularly for quantifier elimination over real-closed fields. When using CAD, there is often a choice for the ordering placed on the variables. This can be important, with some problems infeasible with one variable ordering but easy with another. Machine learning is the process of fitting a computer model to a complex function based on properties learned from measured data. In this paper we use machine learning (specifically a support vector machine) to select between heuristics for choosing a variable ordering, outperforming each of the separate heuristics.Comment: 16 page

Functional Federated Learning in Erlang (ffl-erl)

The functional programming language Erlang is well-suited for concurrent and distributed applications. Numerical computing, however, is not seen as one of its strengths. The recent introduction of Federated Learning, a concept according to which client devices are leveraged for decentralized machine learning tasks, while a central server updates and distributes a global model, provided the motivation for exploring how well Erlang is suited to that problem. We present ffl-erl, a framework for Federated Learning, written in Erlang, and explore how well it performs in two scenarios: one in which the entire system has been written in Erlang, and another in which Erlang is relegated to coordinating client processes that rely on performing numerical computations in the programming language C. There is a concurrent as well as a distributed implementation of each case. Erlang incurs a performance penalty, but for certain use cases this may not be detrimental, considering the trade-off between conciseness of the language and speed of development (Erlang) versus performance (C). Thus, Erlang may be a viable alternative to C for some practical machine learning tasks.Comment: 16 pages, accepted for publication in the WFLP 2018 conference proceedings; final post-prin

Probability of local bifurcation type from a fixed point: A random matrix perspective

Results regarding probable bifurcations from fixed points are presented in the context of general dynamical systems (real, random matrices), time-delay dynamical systems (companion matrices), and a set of mappings known for their properties as universal approximators (neural networks). The eigenvalue spectra is considered both numerically and analytically using previous work of Edelman et. al. Based upon the numerical evidence, various conjectures are presented. The conclusion is that in many circumstances, most bifurcations from fixed points of large dynamical systems will be due to complex eigenvalues. Nevertheless, surprising situations are presented for which the aforementioned conclusion is not general, e.g. real random matrices with Gaussian elements with a large positive mean and finite variance.Comment: 21 pages, 19 figure

Neural parameters estimation for brain tumor growth modeling

Understanding the dynamics of brain tumor progression is essential for optimal treatment planning. Cast in a mathematical formulation, it is typically viewed as evaluation of a system of partial differential equations, wherein the physiological processes that govern the growth of the tumor are considered. To personalize the model, i.e. find a relevant set of parameters, with respect to the tumor dynamics of a particular patient, the model is informed from empirical data, e.g., medical images obtained from diagnostic modalities, such as magnetic-resonance imaging. Existing model-observation coupling schemes require a large number of forward integrations of the biophysical model and rely on simplifying assumption on the functional form, linking the output of the model with the image information. In this work, we propose a learning-based technique for the estimation of tumor growth model parameters from medical scans. The technique allows for explicit evaluation of the posterior distribution of the parameters by sequentially training a mixture-density network, relaxing the constraint on the functional form and reducing the number of samples necessary to propagate through the forward model for the estimation. We test the method on synthetic and real scans of rats injected with brain tumors to calibrate the model and to predict tumor progression

Insulin, Hyperglycemia, and Severe Retinopathy of Prematurity in Extremely Low-Birth-Weight Infants

Objective This study aims to determine the association between hyperglycemia, insulin therapy, and severe retinopathy of prematurity (ROP) in extremely low-birth-weight (ELBW) infants. Study Design In this retrospective database study, we included all ELBW infants who were 180 mg/dL. Covariates were GA, small for GA status, discharge year, sex, Apgar score at 5 minutes, mechanical ventilation, oxygen use, bacteremia, and postnatal steroid exposure. We defined severe ROP as ROP requiring bevacizumab, cryotherapy, laser therapy, or vitrectomy. Sensitivity analysis using BG > 150 mg/dL and > 200 mg/dL was performed. Results A total of 24,548 infants were included; 2,547 (10%) had severe ROP. Hyperglycemia alone was not associated with severe ROP (odds ratio [OR], 0.88; 95% confidence interval [CI], 0.66-1.17). Hyperglycemia and insulin use were not associated with severe ROP (OR, 1.43; 95% CI, 0.91-2.23). BG > 150 mg/dL and insulin use were associated with severe ROP (OR, 1.34; 95% CI, 1.02-1.76). Conclusions Hyperglycemia alone was not associated with severe ROP in ELBW infants. However, we did observe a possible trend between the use of insulin and severe ROP.National Center for Advancing Translational Sciences of the National Institutes of Health (NIH)U.S. governmentNational Institute of Child Health and Human DevelopmentNIHNational Center for Advancing Translational Sciences of the NIHU.S. Food and Drug AdministrationCempra PharmaceuticalsDuke Univ, Dept Pediat, Sch Med, Durham, NC 27706 USADuke Univ, Sch Med, Duke Clin Res Inst, Durham, NC USAKK Womens & Childrens Hosp, Childrens Intens Care Unit, Singapore, SingaporeUniv Fed Sao Paulo, Ecola Paulista Med, Div Neonatal Med, Sao Paulo, BrazilUniv N Carolina, Dept Pediat, Chapel Hill, NC USAMEDNAX Inc, Pediat Med Grp, Jacksonville, FL USAUniv Fed Sao Paulo, Ecola Paulista Med, Div Neonatal Med, Sao Paulo, BrazilNIH: UL1TR001117U.S. government: HHSN267200700051CNational Institute of Child Health and Human Development: K23HD068497National Institute of Child Health and Human Development: HHSN275201000003INational Institute of Child Health and Human Development: 1R01-HD081044-01National Center for Advancing Translational Sciences of the NIH: UL1TR001117U.S. Food and Drug Administration: 1R18-FD005292-01Cempra Pharmaceuticals: HHS0100201300009CWeb of Scienc

Insulin, Hyperglycemia, and Severe Retinopathy of Prematurity in Extremely Low-Birth-Weight Infants

To determine the association between hyperglycemia, insulin therapy, and severe retinopathy of prematurity (ROP) in extremely-low-birth-weight (ELBW) infants

Foreground removal from CMB temperature maps using an MLP neural network

One of the main obstacles in extracting the Cosmic Microwave Background (CMB) signal from observations in the mm-submm range is the foreground contamination by emission from galactic components: mainly synchrotron, free-free and thermal dust emission. Due to the statistical nature of the intrinsic CMB signal it is essential to minimize the systematic errors in the CMB temperature determinations. Following the available knowledge of the spectral behavior of the galactic foregrounds simple, power law-like spectra have been assumed. The feasibility of using a simple neural network for extracting the CMB temperature signal from the combined CMB and foreground signals has been investigated. As a specific example, we have analysed simulated data, like that expected from the ESA Planck Surveyor mission. A simple multilayer perceptron neural network with 2 hidden layers can provide temperature estimates, over more than 80 percent of the sky, that are to a high degree uncorrelated with the foreground signals. A single network will be able to cover the dynamic range of the Planck noise level over the entire sky.Comment: Accepted for publication in Astrophysics and Space Scienc

Representing complex data using localized principal components with application to astronomical data

Often the relation between the variables constituting a multivariate data space might be characterized by one or more of the terms: ``nonlinear'', ``branched'', ``disconnected'', ``bended'', ``curved'', ``heterogeneous'', or, more general, ``complex''. In these cases, simple principal component analysis (PCA) as a tool for dimension reduction can fail badly. Of the many alternative approaches proposed so far, local approximations of PCA are among the most promising. This paper will give a short review of localized versions of PCA, focusing on local principal curves and local partitioning algorithms. Furthermore we discuss projections other than the local principal components. When performing local dimension reduction for regression or classification problems it is important to focus not only on the manifold structure of the covariates, but also on the response variable(s). Local principal components only achieve the former, whereas localized regression approaches concentrate on the latter. Local projection directions derived from the partial least squares (PLS) algorithm offer an interesting trade-off between these two objectives. We apply these methods to several real data sets. In particular, we consider simulated astrophysical data from the future Galactic survey mission Gaia.Comment: 25 pages. In "Principal Manifolds for Data Visualization and Dimension Reduction", A. Gorban, B. Kegl, D. Wunsch, and A. Zinovyev (eds), Lecture Notes in Computational Science and Engineering, Springer, 2007, pp. 180--204, http://www.springer.com/dal/home/generic/search/results?SGWID=1-40109-22-173750210-