187,088 research outputs found
Optimizing compilation with preservation of structural code coverage metrics to support software testing
Code-coverage-based testing is a widely-used testing strategy with the aim of providing a meaningful decision criterion for the adequacy of a test suite. Code-coverage-based testing is also mandated for the development of safety-critical applications; for example, the DO178b document requires the application of the modified condition/decision coverage. One critical issue of code-coverage testing is that structural code coverage criteria are typically applied to source code whereas the generated machine code may result in a different code structure because of code optimizations performed by a compiler. In this work, we present the automatic calculation of coverage profiles describing which structural code-coverage criteria are preserved by which code optimization, independently of the concrete test suite. These coverage profiles allow to easily extend compilers with the feature of preserving any given code-coverage criteria by enabling only those code optimizations that preserve it. Furthermore, we describe the integration of these coverage profile into the compiler GCC. With these coverage profiles, we answer the question of how much code optimization is possible without compromising the error-detection likelihood of a given test suite. Experimental results conclude that the performance cost to achieve preservation of structural code coverage in GCC is rather low.Peer reviewedSubmitted Versio
Guiding Transformation: How Medical Practices Can Become Patient-Centered Medical Homes
Describes in detail eight change concepts as a guide to transforming a practice into a patient-centered medical home, including engaged leadership, quality improvement strategy, continuous and team-based healing relationships, and enhanced access
Metamodel Instance Generation: A systematic literature review
Modelling and thus metamodelling have become increasingly important in
Software Engineering through the use of Model Driven Engineering. In this paper
we present a systematic literature review of instance generation techniques for
metamodels, i.e. the process of automatically generating models from a given
metamodel. We start by presenting a set of research questions that our review
is intended to answer. We then identify the main topics that are related to
metamodel instance generation techniques, and use these to initiate our
literature search. This search resulted in the identification of 34 key papers
in the area, and each of these is reviewed here and discussed in detail. The
outcome is that we are able to identify a knowledge gap in this field, and we
offer suggestions as to some potential directions for future research.Comment: 25 page
Participatory Patterns in an International Air Quality Monitoring Initiative
The issue of sustainability is at the top of the political and societal
agenda, being considered of extreme importance and urgency. Human individual
action impacts the environment both locally (e.g., local air/water quality,
noise disturbance) and globally (e.g., climate change, resource use). Urban
environments represent a crucial example, with an increasing realization that
the most effective way of producing a change is involving the citizens
themselves in monitoring campaigns (a citizen science bottom-up approach). This
is possible by developing novel technologies and IT infrastructures enabling
large citizen participation. Here, in the wider framework of one of the first
such projects, we show results from an international competition where citizens
were involved in mobile air pollution monitoring using low cost sensing
devices, combined with a web-based game to monitor perceived levels of
pollution. Measures of shift in perceptions over the course of the campaign are
provided, together with insights into participatory patterns emerging from this
study. Interesting effects related to inertia and to direct involvement in
measurement activities rather than indirect information exposure are also
highlighted, indicating that direct involvement can enhance learning and
environmental awareness. In the future, this could result in better adoption of
policies towards decreasing pollution.Comment: 17 pages, 6 figures, 1 supplementary fil
Improving Orbit Estimates for Incomplete Orbits with a New Approach to Priors -- with Applications from Black Holes to Planets
We propose a new approach to Bayesian prior probability distributions
(priors) that can improve orbital solutions for low-phase-coverage orbits,
where data cover less than approximately 40% of an orbit. In instances of low
phase coverage such as with stellar orbits in the Galactic center or with
directly-imaged exoplanets, data have low constraining power and thus priors
can bias parameter estimates and produce under-estimated confidence intervals.
Uniform priors, which are commonly assumed in orbit fitting, are notorious for
this. We propose a new observable-based prior paradigm that is based on
uniformity in observables. We compare performance of this observable-based
prior and of commonly assumed uniform priors using Galactic center and
directly-imaged exoplanet (HR 8799) data. The observable-based prior can reduce
biases in model parameters by a factor of two and helps avoid under-estimation
of confidence intervals for simulations with less than about 40% phase
coverage. Above this threshold, orbital solutions for objects with sufficient
phase coverage such as S0-2, a short-period star at the Galactic center with
full phase coverage, are consistent with previously published results. Below
this threshold, the observable-based prior limits prior influence in regions of
prior dominance and increases data influence. Using the observable-based prior,
HR 8799 orbital analyses favor lower eccentricity orbits and provide stronger
evidence that the four planets have a consistent inclination around 30 degrees
to within 1-sigma. This analysis also allows for the possibility of
coplanarity. We present metrics to quantify improvements in orbital estimates
with different priors so that observable-based prior frameworks can be tested
and implemented for other low-phase-coverage orbits.Comment: Published in AJ. 23 pages, 14 figures. Monte Carlo chains are
available in the published article, or are available upon reques
Non-Parametric Estimation of Forecast Distributions in Non-Gaussian, Non-linear State Space Models
The object of this paper is to produce non-parametric maximum likelihood estimates of forecast distributions in a general non-Gaussian, non-linear state space setting. The transition densities that define the evolution of the dynamic state process are represented in parametric form, but the conditional distribution of the non-Gaussian variable is estimated non-parametrically. The filtering and prediction distributions are estimated via a computationally efficient algorithm that exploits the functional relationship between the observed variable, the state variable and a measurement error with an invariant distribution. Simulation experiments are used to document the accuracy of the non-parametric method relative to both correctly and incorrectly specified parametric alternatives. In an empirical illustration, the method is used to produce sequential estimates of the forecast distribution of realized volatility on the S&P500 stock index during the recent financial crisis. A resampling technique for measuring sampling variation in the estimated forecast distributions is also demonstrated.Probabilistic Forecasting; Non-Gaussian Time Series; Grid-based Filtering; Penalized Likelihood; Subsampling; Realized Volatility.
Implementation Choices for the Children's Health Insurance Program Reauthorization Act of 2009
Synthesizes policy analyses and discussions with experts of provisions in the Children's Health Insurance Program Reauthorization Act to strengthen outreach and enrollment and improve quality of care. Recommends steps to ensure effective implementation
Likelihood based observability analysis and confidence intervals for predictions of dynamic models
Mechanistic dynamic models of biochemical networks such as Ordinary
Differential Equations (ODEs) contain unknown parameters like the reaction rate
constants and the initial concentrations of the compounds. The large number of
parameters as well as their nonlinear impact on the model responses hamper the
determination of confidence regions for parameter estimates. At the same time,
classical approaches translating the uncertainty of the parameters into
confidence intervals for model predictions are hardly feasible.
In this article it is shown that a so-called prediction profile likelihood
yields reliable confidence intervals for model predictions, despite arbitrarily
complex and high-dimensional shapes of the confidence regions for the estimated
parameters. Prediction confidence intervals of the dynamic states allow a
data-based observability analysis. The approach renders the issue of sampling a
high-dimensional parameter space into evaluating one-dimensional prediction
spaces. The method is also applicable if there are non-identifiable parameters
yielding to some insufficiently specified model predictions that can be
interpreted as non-observability. Moreover, a validation profile likelihood is
introduced that should be applied when noisy validation experiments are to be
interpreted.
The properties and applicability of the prediction and validation profile
likelihood approaches are demonstrated by two examples, a small and instructive
ODE model describing two consecutive reactions, and a realistic ODE model for
the MAP kinase signal transduction pathway. The presented general approach
constitutes a concept for observability analysis and for generating reliable
confidence intervals of model predictions, not only, but especially suitable
for mathematical models of biological systems
Estimating a Signal In the Presence of an Unknown Background
We describe a method for fitting distributions to data which only requires
knowledge of the parametric form of either the signal or the background but not
both. The unknown distribution is fit using a non-parametric kernel density
estimator. The method returns parameter estimates as well as errors on those
estimates. Simulation studies show that these estimates are unbiased and that
the errors are correct
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