Performance comparison of several priority schemes with priority jumps

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The Lazy Bootstrap. A Fast Resampling Method for Evaluating Latent Class Model Fit

The latent class model is a powerful unsupervised clustering algorithm for categorical data. Many statistics exist to test the fit of the latent class model. However, traditional methods to evaluate those fit statistics are not always useful. Asymptotic distributions are not always known, and empirical reference distributions can be very time consuming to obtain. In this paper we propose a fast resampling scheme with which any type of model fit can be assessed. We illustrate it here on the latent class model, but the methodology can be applied in any situation. The principle behind the lazy bootstrap method is to specify a statistic which captures the characteristics of the data that a model should capture correctly. If those characteristics in the observed data and in model-generated data are very different we can assume that the model could not have produced the observed data. With this method we achieve the flexibility of tests from the Bayesian framework, while only needing maximum likelihood estimates. We provide a step-wise algorithm with which the fit of a model can be assessed based on the characteristics we as researcher find important. In a Monte Carlo study we show that the method has very low type I errors, for all illustrated statistics. Power to reject a model depended largely on the type of statistic that was used and on sample size. We applied the method to an empirical data set on clinical subgroups with risk of Myocardial infarction and compared the results directly to the parametric bootstrap. The results of our method were highly similar to those obtained by the parametric bootstrap, while the required computations differed three orders of magnitude in favour of our method.Comment: This is an adaptation of chapter of a PhD dissertation available at https://pure.uvt.nl/portal/files/19030880/Kollenburg_Computer_13_11_2017.pd

Empirical Implications of Equilibrium Bidding in First-Price, Symmetric, Common Value Auctions

This paper studies federal auctions for wildcat leases on the Outer Continental Shelf from 1954 to 1970. These are leases where bidders privately acquire (at some cost) noisy, but equally informative, signals about the amount of oil and gas that may be present. We develop a test of equilibrium bidding in a common values model that is implemented using data on bids and ex post values. We compute bid markups and rents under the alternative hypotheses of private and common values and find that the data are more consistent with the latter hypothesis. Finally, we use data on tract location and ex post values to test the comparative static prediction in common value auctions that bidders may bid less aggressively when they expect more competition.

Extreme morphologies of mantis shrimp larvae

Larvae of stomatopods (mantis shrimps) are generally categorized into four larval types: antizoea, pseudozoea (both representing early larval stages), alima and erichthus (the latter two representing later larval stages). These categories, however, do not reflect the existing morphological diversity of stomatopod larvae, which is largely unstudied. We describe here four previously unknown larval types with extreme morphologies. All specimens were found in the collections of the Zoological Museum, University of Copenhagen and were collected during the Danish Dana Expedition round the world 1928-30. These new larval types all represent erichthus-type larvae, especially differing in their shield morphologies. The shield morphology ranges from almost spherical to rather disc-like, with sometimes extremely elongated spines, but only a general systematic assignment of the larvae was possible. Further investigations of these larvae are crucial to understand their life habits and ecological impact, especially as stomatopod and other crustacean larvae might have a much more important position in the marine ecosystems than their corresponding adults

Prerequisites for Affective Signal Processing (ASP)

Although emotions are embraced by science, their recognition has not reached a satisfying level. Through a concise overview of affect, its signals, features, and classification methods, we provide understanding for the problems encountered. Next, we identify the prerequisites for successful Affective Signal Processing: validation (e.g., mapping of constructs on signals), triangulation, a physiology-driven approach, and contributions of the signal processing community. Using these directives, a critical analysis of a real-world case is provided. This illustrates that the prerequisites can become a valuable guide for Affective Signal Processing (ASP)

Patient-specific CFD simulation of intraventricular haemodynamics based on 3D ultrasound imaging

Background: The goal of this paper is to present a computational fluid dynamic (CFD) model with moving boundaries to study the intraventricular flows in a patient-specific framework. Starting from the segmentation of real-time transesophageal echocardiographic images, a CFD model including the complete left ventricle and the moving 3D mitral valve was realized. Their motion, known as a function of time from the segmented ultrasound images, was imposed as a boundary condition in an Arbitrary Lagrangian-Eulerian framework. Results: The model allowed for a realistic description of the displacement of the structures of interest and for an effective analysis of the intraventricular flows throughout the cardiac cycle. The model provides detailed intraventricular flow features, and highlights the importance of the 3D valve apparatus for the vortex dynamics and apical flow. Conclusions: The proposed method could describe the haemodynamics of the left ventricle during the cardiac cycle. The methodology might therefore be of particular importance in patient treatment planning to assess the impact of mitral valve treatment on intraventricular flow dynamics