8,598 research outputs found
Using blind analysis for software engineering experiments
Context: In recent years there has been growing concern about conflicting experimental results in empirical software engineering. This has been paralleled by awareness of how bias can impact research results. Objective: To explore the practicalities of blind analysis of experimental results to reduce bias. Method : We apply blind analysis to a real software engineering experiment that compares three feature weighting approaches with a na ̈ıve benchmark (sample mean) to the Finnish software effort data set. We use this experiment as an example to explore blind analysis as a method to reduce researcher bias. Results: Our experience shows that blinding can be a relatively straightforward procedure. We also highlight various statistical analysis decisions which ought not be guided by the hunt for statistical significance and show that results can be inverted merely through a seemingly inconsequential statistical nicety (i.e., the degree of trimming). Conclusion: Whilst there are minor challenges and some limits to the degree of blinding possible, blind analysis is a very practical and easy to implement method that supports more objective analysis of experimental results. Therefore we argue that blind analysis should be the norm for analysing software engineering experiments
Scientific applications of radio and radar tracking in the space program Conference proceedings
Radar and radio tracking applications in space progra
Frequentist and Bayesian measures of confidence via multiscale bootstrap for testing three regions
A new computation method of frequentist -values and Bayesian posterior
probabilities based on the bootstrap probability is discussed for the
multivariate normal model with unknown expectation parameter vector. The null
hypothesis is represented as an arbitrary-shaped region. We introduce new
parametric models for the scaling-law of bootstrap probability so that the
multiscale bootstrap method, which was designed for one-sided test, can also
computes confidence measures of two-sided test, extending applicability to a
wider class of hypotheses. Parameter estimation is improved by the two-step
multiscale bootstrap and also by including higher-order terms. Model selection
is important not only as a motivating application of our method, but also as an
essential ingredient in the method. A compromise between frequentist and
Bayesian is attempted by showing that the Bayesian posterior probability with
an noninformative prior is interpreted as a frequentist -value of
``zero-sided'' test
Universality Class of One-Dimensional Directed Sandpile Models
A general n-state directed `sandpile' model is introduced. The stationary
properties of the n-state model are derived for n < infty, and analytical
arguments based on a central limit theorem show that the model belongs to the
universality class of the totally asymmetric Oslo model, with a crossover to
uncorrelated branching process behavior for small system sizes. Hence, the
central limit theorem allows us to identify the existence of a large
universality class of one-dimensional directed sandpile models.Comment: 4 pages, 2 figure
Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation
Image segmentation is a fundamental problem in biomedical image analysis.
Recent advances in deep learning have achieved promising results on many
biomedical image segmentation benchmarks. However, due to large variations in
biomedical images (different modalities, image settings, objects, noise, etc),
to utilize deep learning on a new application, it usually needs a new set of
training data. This can incur a great deal of annotation effort and cost,
because only biomedical experts can annotate effectively, and often there are
too many instances in images (e.g., cells) to annotate. In this paper, we aim
to address the following question: With limited effort (e.g., time) for
annotation, what instances should be annotated in order to attain the best
performance? We present a deep active learning framework that combines fully
convolutional network (FCN) and active learning to significantly reduce
annotation effort by making judicious suggestions on the most effective
annotation areas. We utilize uncertainty and similarity information provided by
FCN and formulate a generalized version of the maximum set cover problem to
determine the most representative and uncertain areas for annotation. Extensive
experiments using the 2015 MICCAI Gland Challenge dataset and a lymph node
ultrasound image segmentation dataset show that, using annotation suggestions
by our method, state-of-the-art segmentation performance can be achieved by
using only 50% of training data.Comment: Accepted at MICCAI 201
Some Aspects of Measurement Error in Linear Regression of Astronomical Data
I describe a Bayesian method to account for measurement errors in linear
regression of astronomical data. The method allows for heteroscedastic and
possibly correlated measurement errors, and intrinsic scatter in the regression
relationship. The method is based on deriving a likelihood function for the
measured data, and I focus on the case when the intrinsic distribution of the
independent variables can be approximated using a mixture of Gaussians. I
generalize the method to incorporate multiple independent variables,
non-detections, and selection effects (e.g., Malmquist bias). A Gibbs sampler
is described for simulating random draws from the probability distribution of
the parameters, given the observed data. I use simulation to compare the method
with other common estimators. The simulations illustrate that the Gaussian
mixture model outperforms other common estimators and can effectively give
constraints on the regression parameters, even when the measurement errors
dominate the observed scatter, source detection fraction is low, or the
intrinsic distribution of the independent variables is not a mixture of
Gaussians. I conclude by using this method to fit the X-ray spectral slope as a
function of Eddington ratio using a sample of 39 z < 0.8 radio-quiet quasars. I
confirm the correlation seen by other authors between the radio-quiet quasar
X-ray spectral slope and the Eddington ratio, where the X-ray spectral slope
softens as the Eddington ratio increases.Comment: 39 pages, 11 figures, 1 table, accepted by ApJ. IDL routines
(linmix_err.pro) for performing the Markov Chain Monte Carlo are available at
the IDL astronomy user's library, http://idlastro.gsfc.nasa.gov/homepage.htm
Comprehensive quantum Monte Carlo study of the quantum critical points in planar dimerized/quadrumerized Heisenberg models
We study two planar square lattice Heisenberg models with explicit
dimerization or quadrumerization of the couplings in the form of ladder and
plaquette arrangements. We investigate the quantum critical points of those
models by means of (stochastic series expansion) quantum Monte Carlo
simulations as a function of the coupling ratio . The
critical point of the order-disorder quantum phase transition in the ladder
model is determined as improving on previous
studies. For the plaquette model we obtain
establishing a first benchmark for this model from quantum Monte Carlo
simulations. Based on those values we give further convincing evidence that the
models are in the three-dimensional (3D) classical Heisenberg universality
class. The results of this contribution shall be useful as references for
future investigations on planar Heisenberg models such as concerning the
influence of non-magnetic impurities at the quantum critical point.Comment: 10+ pages, 7 figures, 4 table
Nonparametric Regression using the Concept of Minimum Energy
It has recently been shown that an unbinned distance-based statistic, the
energy, can be used to construct an extremely powerful nonparametric
multivariate two sample goodness-of-fit test. An extension to this method that
makes it possible to perform nonparametric regression using multiple
multivariate data sets is presented in this paper. The technique, which is
based on the concept of minimizing the energy of the system, permits
determination of parameters of interest without the need for parametric
expressions of the parent distributions of the data sets. The application and
performance of this new method is discussed in the context of some simple
example analyses.Comment: 10 pages, 4 figure
Critical behavior of the three-dimensional bond-diluted Ising spin glass: Finite-size scaling functions and Universality
We study the three-dimensional (3D) bond-diluted Edwards-Anderson (EA) model
with binary interactions at a bond occupation of 45% by Monte Carlo (MC)
simulations. Using an efficient cluster MC algorithm we are able to determine
the universal finite-size scaling (FSS) functions and the critical exponents
with high statistical accuracy. We observe small corrections to scaling for the
measured observables. The critical quantities and the FSS functions indicate
clearly that the bond-diluted model for dilutions above the critical dilution
p*, at which a spin glass (SG) phase appears, lies in the same universality
class as the 3D undiluted EA model with binary interactions. A comparison with
the FSS functions of the 3D site-diluted EA model with Gaussian interactions at
a site occupation of 62.5% gives very strong evidence for the universality of
the SG transition in the 3D EA model.Comment: Revised version. 10 pages, 9 figures, 2 table
Collective motion of self-propelled particles interacting without cohesion
We present a comprehensive study of Vicsek-style self-propelled particle
models in two and three space dimensions. The onset of collective motion in
such stochastic models with only local alignment interactions is studied in
detail and shown to be discontinuous (first-order like). The properties of the
ordered, collectively moving phase are investigated. In a large domain of
parameter space including the transition region, well-defined high-density and
high-order propagating solitary structures are shown to dominate the dynamics.
Far enough from the transition region, on the other hand, these objects are not
present. A statistically-homogeneous ordered phase is then observed, which is
characterized by anomalously-strong density fluctuations, superdiffusion, and
strong intermittency.Comment: Submitted to Physical Review
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