1,642 research outputs found
Neural processes of proactive and reactive controls modulated by motor-skill experiences
This study investigated the experience of open and closed motor skills on modulating proactive and reactive control processes in task switching. Fifty-four participants who were open-skilled
Randomized strategies and prospect theory in a dynamic context
When prospect theory (PT) is applied in a dynamic context, the probability weighting com- ponent brings new challenges. We study PT agents facing optimal timing decisions and consider the impact of allowing them to follow randomized strategies. In a continuous-time model of gam- bling and optimal stopping, Ebert and Strack (2015) show that a naive PT investor with access only to pure strategies never stops. We show that allowing randomization can signi cantly alter the predictions of their model, and can result in voluntary cessation of gambling
Resume of Alex W. Lam, 1991-02
Naval Postgraduate School Faculty Resum
Design and analysis of genetical genomics studies and their potential applications in livestock research
Quantitative Trait Loci (QTL) mapping has been widely used to identify
genetic loci attributable to the variation observed in complex traits. In recent years,
gene expression phenotypes have emerged as a new type of quantitative trait for
which QTL can be mapped. Locating sequence variation that has an effect on gene
expression (eQTL) is thought to be a promising way to elucidate the genetic
architecture of quantitative traits. This thesis explores a number of methodological
aspects of eQTL mapping (also known as “genetical genomics”) and considers some
practical strategies for applying this approach to livestock populations.
One of the exciting prospects of genetical genomics is that the combination of
expression studies with fine mapping of functional trait loci can guide the
reconstruction of gene networks. The thesis begins with an analysis in which
correlations between gene expression and meat quality traits in pigs are investigated
in relation to a pork meat quality QTL previously identified. The influence on power
due to factors including sample size and records of matched subjects is discussed. An
efficient experimental design for two-colour microarrays is then put forward, and it is
shown to be an effective use of microarrays for mapping additive eQTL in outbred
crosses under simulation. However, designs optimised for detecting both additive
and dominance eQTL are found to be less effective.
Data collected from livestock populations usually have a pedigreed structure.
Many family-based association mapping methods are rather computationally
intensive, hence are time-consuming when analysing very large numbers of traits.
The application of a novel family-based association method is demonstrated; it is
shown to be fast, accurate and flexible for genetical genomics. Furthermore, the
results show that multiple testing correction alone is not sufficient to control type I
errors in genetical genomics and that careful data filtering is essential. While it is
important to limit false positives, it is desirable not to miss many true signals. A
multi-trait analysis based on grouping of functionally related genes is devised to
detect some of the signals overlooked by a univariate analysis. Using an inbred rat
dataset, 13 loci are identified with significant linkage to gene sets of various
functions defined by Gene Ontology. Applying this method to livestock species is
possible, but the current level of annotations is a limiting factor. Finally, the thesis
concludes with some current opinions on the development of genetical genomics and
its impact on livestock genetics research
Analyzing preferences ranking when there are too many alternatives.
Consumer preferences can be measured by rankings of alternatives. When there are too many alternatives, this consumer task becomes complex. One option is to have consumers rank only a subset of the available alternatives. This has an impact on subsequent statistical analysis, as now a large amount of ties is observed. We propose a simple methodology to perform proper statistical analysis in this case. It also allows to test whether (parts of the) rankings are random or not. An illustration shows its ease of application
Confidence intervals for maximal reliability of probability judgments
Subjective probabilities play an important role in marketing
research, for example where individuals rate the likelihood that
they will purchase a new to develop product. The tau-equivalent
model can describe the joint behaviour of multiple test items
measuring the same subjective probability. It improves the
reliability of the subjective probability estimate by using a
weighted sum as the outcome of the test rather than an unweighted
sum. One can choose the weights to obtain maximal reliability.
In this paper we stress the use of confidence intervals to assess
maximal reliability, as this allows for a more critical assessment
of the items as measurement instruments. Furthermore, two new
confidence intervals for the maximal reliability are derived and
compared to intervals derived earlier in \\citet{YuanBentler2002,
RaykovPenev2006}. The comparison involves coverage curves, a
methodology that is new in the field of reliability. The existing
Yuan-Bentler and Raykov-Penev intervals are shown to overestimate
the maximal reliability, whereas one of our proposed intervals, the
stable interval, performs very well. This stable interval hardly
shows any bias, and has a coverage for the true value which is
approximately equal to the confidence level
Ranking Models in Conjoint Analysis
In this paper we consider the estimation of probabilistic
ranking models in the context of conjoint experiments. By using
approximate rather than exact ranking probabilities, we do not
need to compute high-dimensional integrals. We extend the
approximation technique proposed by \\citet{Henery1981} in the
Thurstone-Mosteller-Daniels model for any Thurstone order
statistics model and we show that our approach allows for a
unified approach. Moreover, our approach also allows for the
analysis of any partial ranking. Partial rankings are essential
in practical conjoint analysis to collect data efficiently to
relieve respondents' task burden
Traceable and Authenticable Image Tagging for Fake News Detection
To prevent fake news images from misleading the public, it is desirable not
only to verify the authenticity of news images but also to trace the source of
fake news, so as to provide a complete forensic chain for reliable fake news
detection. To simultaneously achieve the goals of authenticity verification and
source tracing, we propose a traceable and authenticable image tagging approach
that is based on a design of Decoupled Invertible Neural Network (DINN). The
designed DINN can simultaneously embed the dual-tags, \textit{i.e.},
authenticable tag and traceable tag, into each news image before publishing,
and then separately extract them for authenticity verification and source
tracing. Moreover, to improve the accuracy of dual-tags extraction, we design a
parallel Feature Aware Projection Model (FAPM) to help the DINN preserve
essential tag information. In addition, we define a Distance Metric-Guided
Module (DMGM) that learns asymmetric one-class representations to enable the
dual-tags to achieve different robustness performances under malicious
manipulations. Extensive experiments, on diverse datasets and unseen
manipulations, demonstrate that the proposed tagging approach achieves
excellent performance in the aspects of both authenticity verification and
source tracing for reliable fake news detection and outperforms the prior
works
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