Titel Deelnemers "Korte inhoud"
"The influence of study-level inference models and study set size on coordinate-based fMRI meta-analyses" "Han Bossier, Ruth Seurinck, Simone Kuehn, Tobias Banaschewski, Gareth J Barker, Arun LW Bokde, Jean-Luc Martinot, Herve Lemaitre, Tomás Paus, Sabina Millenet, Beatrijs Moerkerke" "Given the increasing amount of neuroimaging studies, there is a growing need to summarize published results. Coordinate-based meta-analyses use the locations of statistically significant local maxima with possibly the associated effect sizes to aggregate studies. In this paper, we investigate the influence of key characteristics of a coordinate-based meta-analysis on (1) the balance between false and true positives and (2) the activation reliability of the outcome from a coordinate-based meta-analysis. More particularly, we consider the influence of the chosen group level model at the study level [fixed effects, ordinary least squares (OLS), or mixed effects models], the type of coordinate-based meta-analysis [Activation Likelihood Estimation (ALE) that only uses peak locations, fixed effects, and random effects meta-analysis that take into account both peak location and height] and the amount of studies included in the analysis (from 10 to 35). To do this, we apply a resampling scheme on a large dataset (N = 1,400) to create a test condition and compare this with an independent evaluation condition. The test condition corresponds to subsampling participants into studies and combine these using meta-analyses. The evaluation condition corresponds to a high-powered group analysis. We observe the best performance when using mixed effects models in individual studies combined with a random effects meta-analysis. Moreover the performance increases with the number of studies included in the meta-analysis. When peak height is not taken into consideration, we show that the popular ALE procedure is a good alternative in terms of the balance between type I and II errors. However, it requires more studies compared to other procedures in terms of activation reliability. Finally, we discuss the differences, interpretations, and limitations of our results."
"Lifted Probabilistic Inference by Variable Elimination (Gelifte probabilistische inferentie door variabele eliminatie)" "Nima Taghipour" "Representing, learning, and reasoning about knowledge are central to artificial intelligence (AI). A long standing goal of AI is unifying logic and probability, to benefit from the strengths of both formalisms. Probability theory allows us to represent and reason in uncertain domains, while first-order logic allows us to represent and reason about structured, relational domains. Many real-world problems exhibit both uncertainty and structure, and thus can be more naturally represented with a combination of probabilistic and logical knowledge. This observation has led to the development of probabilistic logical models (PLMs), which combine probabilistic models with elements of first-order logic, to succinctly capture uncertainty in structured, relational domains, e.g., social networks, citation graphs, etc. While PLMs provide expressive representation formalisms, efficient inference is still a major challenge in these models, as they typically involve a large number of objects and interactions among them. Among the various efforts to address this problem, a promising line of work is lifted probabilistic inference. Lifting attempts to improve the efficiency of inference by exploiting the symmetries in the model. The basic principle of lifting is to perform an inference operation once for a whole group of interchangeable objects, instead of once per individual in the group. Researchers have proposed lifted versions of various (propositional) probabilistic inference algorithms, and shown large speedups achieved by the lifted algorithms over their propositional counterparts. In this dissertation, we make a number of novel contributions to lifted inference, mainly focusing on lifted variable elimination (LVE). First, we focus on constraint processing, which is an integral part of lifted inference. Lifted inference algorithms are commonly tightly coupled to a specific constraint language. We bring more insight in LVE, by decoupling the operators from the used constraint language. We define lifted inference operations so that they operate on the semantic level rather than on the syntactic level, making them language independent. Further, we show how this flexibility allows us to improve the efficiency of inference, by enhancing LVE with a more powerful constraint representation. Second, we generalize the `lifting' tools used by LVE, by introducing a number of novel lifted operators in this algorithm. We show how these operations allow LVE to exploit a broader range of symmetries, and thus expand the range of problems it can solve in a lifted way. Third, we advance our theoretical understanding of lifted inference by providing the first completeness result for LVE. We prove that LVE is complete---always has a lifted solution---for the fragment of 2-logvar models, a model class that can represent many useful relations in PLMs, such as (anti-)symmetry and homophily. This result also shows the importance of our contributions to LVE, as we prove they are sufficient and necessary for LVE to achieve completeness. Fourth, we propose the structure of first-order decomposition trees (FO-dtrees), as a tool for symbolically analyzing lifted inference solutions. We show how FO-dtrees can be used to characterize an LVE solution, in terms of a sequence of lifted operations. We further make a theoretical analysis of the complexity of lifted inference based on a corresponding FO-dtree, which is valuable for finding and selecting among different lifted solutions. Finally, we present a pre-processing method for speeding up (lifted) inference. Our goal with this method is to speed up inference in PLMs by restricting the computations to the requisite part of the model. For this, we build on the Bayes-ball algorithm that identifies the requisite variables in a ground Bayesian network. We present a lifted version of Bayes-ball, which works with first-order Bayesian networks, and show how it applies to lifted inference."
"Lifted Inference and Learning in Statistical Relational Models (Eerste-orde inferentie en leren in statistische relationele modellen)" "Guy Van den Broeck" "Statistical relational models combine aspects of first-order logic and probabilistic graphical models, enabling them to model complex logical and probabilistic interactions between large numbers of objects. This level of expressivity comes at the cost of increased complexity of inference, motivating a new line of research in lifted probabilistic inference. By exploiting symmetries of the relational structure in the model, and reasoning about groups of objects as a whole, lifted algorithms dramatically improve the run time of inference and learning.The thesis has five main contributions. First, we propose a new method for logical inference, calledfirst-order knowledge compilation. We show that by compiling relational models into a new circuit language, hard inference problems become tractable to solve. Furthermore, we present an algorithm that compiles relational models into our circuit language. Second, we show how to use first-order knowledge compilation for statistical relational models, leading to a new state-of-the-art lifted probabilistic inference algorithm. Third, we develop a formal framework for exact lifted inference, including a definition in terms of its complexity w.r.t. the number of objects in the world. From this follows a first completeness result, showing that the two-variable class of statistical relational models always supports lifted inference. Fourth, we present an algorithm for approximate lifted inference by performing exact lifted inference in a relaxed, approximate model. Statistical relational models are receiving a lot of attention today because of their expressive power for learning. Fifth, we propose to harness the full power of relational representations for that task, by using lifted parameter learning. The techniques presented in this thesis are evaluated empirically on statistical relational models of thousands of interacting objects and millions of random variables."
"Making inference across mobilisation and influence research" "Joost Berkhout, Jan Beyers, Caelesta Braun, Marcel Hanegraaff, David Lowery" "Scholars of mobilisation and policy influence employ two quite different approaches to mapping interest group systems. Those interested in research questions on mobilisation typically rely on a bottom-up mapping strategy in order to characterise the total size and composition of interest group communities. Researchers with an interest in policy influence usually rely on a top-down strategy in which the mapping of politically active organisations depends on samples of specific policies. But some scholars also use top-down data gathered for other research questions on mobilisation (and vice versa). However, it is currently unclear how valid such large-N data for different types of research questions are. We illustrate our argument by addressing these questions using unique data sets drawn from the INTEREURO project on lobbying in the European Union and the European Unions Transparency Register. Our findings suggest that top-down and bottom-up mapping strategies lead to profoundly different maps of interest group communities."
"What I infer depends on who you are: The influence of stereotypes on trait and situational spontaneous inferences" "Kaat Van Acker"
"The influence of social categorization on trait inferences: ERP-data" "Laurens Van Der Cruyssen" "Previous research has shown that the mere categorization of individuals into minimal groups will evoke in-group favoritism. The current study combines the minimal group paradigm with an ERP-experiment of spontaneous trait inferences using an expectancy-violation paradigm. 53 subjects first performed a fake reaction-time (RT) task to allow social categorization (in-group/out-group). One group of subjects (SoCat) all received the same group label based on their results on the RT task, another group of subjects (NoCat) did not receive a group label after the RT task and continued directly to the ERP-recording. During the ERP-recording all subjects read behavioral sentences implying a personality trait of a protagonist. The protagonist either belonged to the in- or out-group, depending on the experimental condition (SoCat; NoCat) to which the subject belonged. The last sentence describing each protagonist was consistent, inconsistent or irrelevant to the previously implied trait. By comparing consistent versus inconsistent sentences, we can measure temporal as well as spatial localization of trait-inferences. We expect trait inferences about an in-group member to evoke a P3-component. Trait inferences about out-group members are expected to generate a P3-component as well, though with a later onset. A small difference in source localization is also expected, involving the medial prefrontal cortex at a more ventral location for in-group members than out-group members."
"The theoretical influence of the difference between the LUMO energy levels of donor and acceptor in organic photovoltaic triplejunction solar cells" "Ben Minnaert, Peter Veelaert" "In organic photovoltaic solar cells, light absorption does not immediately lead to free charge carriers. Instead, an exciton is created. The highest efficiency is reached when the lowest unoccupied molecular orbital (LUMO) of the donor is as close as possible to the LUMO of the acceptor. However, a necessary condition for efficient dissociation of the created excitons is that the difference between the LUMOs of donor and acceptor is higher than the exciton binding energy. The value of the exciton binding energy in different materials is a subject of discussion. The excess of this necessary minimum of the LUMO-difference corresponds with an energy loss. Moreover, it is often not possible to optimize suitable material combinations for organic photovoltaic cells to an ideal (low) LUMO difference. Another energy loss in organic solar cells is caused by their narrow absorption windows, compared to the absorption band of inorganic solar cells. A way to capture a wider band of the solar radiation is using more solar cells with different bandgaps in a row. In this article, we study three organic cells in a row, i.e. a triple-junction. More specifically, we study the theoretical influence of the difference between the LUMO energy levels of donor and acceptor for an organic triple-junction solar cell. We study as well the monolithic as the stacked configuration."
"Variability of the standard reference height and its influence on the stereometric parameters of the heidelberg retina tomograph 3" "Christophe Breusegem, Steffen Fieuws, Ingeborg Stalmans, Thierry Zeyen" "PURPOSE: To investigate the variability of the standard reference height (SRH) in consecutive Heidelberg Retina Tomograph 3 (HRT3) examinations and its influence on five main stereometric parameters.METHODS: HRT3 results of all patients attending our glaucoma center from August to October 2007 were retrospectively reviewed. Only one eye per patient with at least three consecutive HRTs and a quality control label of acceptable or better was selected. An SRH difference >or=10% compared with baseline was considered as excess SRH variability.RESULTS: A review of 641 patients yielded 150 patients (150 eyes) who met the inclusion criteria, representing a total of 556 examinations. The mean total number of HRTs per patient was 3.7 (range, 3-7), and the mean follow-up time was 3.5 years (range, 1.4-6.7). The mean proportion of consecutive HRT3 examinations with intraindividual excess SRH variability was 46% (95% CI, 40-52), whereas the mean intraindividual excess SRH variability was 24% (95% CI, 20-28). The median absolute SRH difference was 8.6% (interquartile range, 3.9%-16.2%). Furthermore, 55.5%, 75.3%, 74.7%, 0.0%, and 19.4% of the variability in rim area, rim volume, retinal nerve fiber layer thickness, cup shape measure, and height variation contour, respectively, could be attributed to SRH variability.CONCLUSIONS: There is considerable SRH variability between HRT3 examinations of the same eye, and this could explain more than half the variability of the parameters RA, RV, and RNFL. These findings indicate that changes of HRT3 parameters should be considered with caution when excess SRH variability is present."
"The theoretical influence of the difference between the LUMO energy levels of donor and acceptor in organic photovoltaic triplejunction solar cells" "Ben Minnaert, Peter Veelaert"
"The influence of indoor temperature on the difference between actual and theoretical energy consumption for space heating" "Amaryllis Audenaert, Kathleen Briffaerts, Dries De Boeck"