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

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

Resume of Alex W. Lam, 1991-02

Naval Postgraduate School Faculty Resum

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