Formula juramenti Judaeorum, oder der Juden Eyd, darinn ihre bissherige Art gegen die Christen zu schwören als falsch verworffen ... : Nebst e. Anh. von unterschiedl. Missbräuchen d. Juden ... ; anjetzo mit e. Zugabe ... vermehret

hrsg. von Friedrich Wilhelm Christoph Tauffenberg

Formula juramenti Judaeorum, oder der Juden Eyd, darinn ihre bissherige Art gegen die Christen zu schwören als falsch verworffen ... : Nebst e. Anh. von unterschiedl. Missbräuchen d. Juden ... ; anjetzo mit e. Zugabe von d. greuchlichen Wucher d. Juden vermehret

hrsg. von Friedrich Wilhelm Christoph Tauffenberg

Using Bayes' rule to define the value of evidence from syndromic surveillance

In this work we propose the adoption of a statistical framework used in the evaluation of forensic evidence as a tool for evaluating and presenting circumstantial "evidence" of a disease outbreak from syndromic surveillance. The basic idea is to exploit the predicted distributions of reported cases to calculate the ratio of the likelihood of observing n cases given an ongoing outbreak over the likelihood of observing n cases given no outbreak. The likelihood ratio defines the Value of Evidence (V). Using Bayes' rule, the prior odds for an ongoing outbreak are multiplied by V to obtain the posterior odds. This approach was applied to time series on the number of horses showing clinical respiratory symptoms or neurological symptoms. The separation between prior beliefs about the probability of an outbreak and the strength of evidence from syndromic surveillance offers a transparent reasoning process suitable for supporting decision makers. The value of evidence can be translated into a verbal statement, as often done in forensics or used for the production of risk maps. Furthermore, a Bayesian approach offers seamless integration of data from syndromic surveillance with results from predictive modeling and with information from other sources such as disease introduction risk assessments.

Effects of colostrum versus formula feeding on hepatic glucocorticoid and α1- and β2-adrenergic receptors in neonatal calves and their effect on glucose and lipid metabolism

Neonatal energy metabolism in calves has to adapt to extrauterine life and depends on colostrum feeding. The adrenergic and glucocorticoid systems are involved in postnatal maturation of pathways related to energy metabolism and calves show elevated plasma concentrations of cortisol and catecholamines during perinatal life. We tested the hypothesis that hepatic glucocorticoid receptors (GR) and α₁- and β₂-adrenergic receptors (AR) in neonatal calves are involved in adaptation of postnatal energy metabolism and that respective binding capacities depend on colostrum feeding. Calves were fed colostrum (CF; n=7) or a milk-based formula (FF; n=7) with similar nutrient content up to d 4 of life. Blood samples were taken daily before feeding and 2h after feeding on d 4 of life to measure metabolites and hormones related to energy metabolism in blood plasma. Liver tissue was obtained 2 h after feeding on d 4 to measure hepatic fat content and binding capacity of AR and GR. Maximal binding capacity and binding affinity were calculated by saturation binding assays using [(3)H]-prazosin and [(3)H]-CGP-12177 for determination of α₁- and β₂-AR and [(3)H]-dexamethasone for determination of GR in liver. Additional liver samples were taken to measure mRNA abundance of AR and GR, and of key enzymes related to hepatic glucose and lipid metabolism. Plasma concentrations of albumin, triacylglycerides, insulin-like growth factor I, leptin, and thyroid hormones changed until d 4 and all these variables except leptin and thyroid hormones responded to feed intake on d 4. Diet effects were determined for albumin, insulin-like growth factor I, leptin, and thyroid hormones. Binding capacity for GR was greater and for α₁-AR tended to be greater in CF than in FF calves. Binding affinities were in the same range for each receptor type. Gene expression of α₁-AR (ADRA1) tended to be lower in CF than FF calves. Binding capacity of GR was related to parameters of glucose and lipid metabolism, whereas β₂-AR binding capacity was negatively associated with glucose metabolism. In conclusion, our results indicate a dependence of GR and α₁-AR on milk feeding immediately after birth and point to an involvement of hepatic GR and AR in postnatal adaptation of glucose and lipid metabolism in calves.

Twenty questions with noise: Bayes optimal policies for entropy loss

We consider the problem of twenty questions with noisy answers, in which we seek to find a target by repeatedly choosing a set, asking an oracle whether the target lies in this set, and obtaining an answer corrupted by noise. Starting with a prior distribution on the target's location, we seek to minimize the expected entropy of the posterior distribution. We formulate this problem as a dynamic program and show that any policy optimizing the one-step expected reduction in entropy is also optimal over the full horizon. Two such Bayes optimal policies are presented: one generalizes the probabilistic bisection policy due to Horstein and the other asks a deterministic set of questions. We study the structural properties of the latter, and illustrate its use in a computer vision application.

Steiner’s formula in the Heisenberg group

Steiner’s tube formula states that the volume of an ϵ-neighborhood of a smooth regular domain in Rn is a polynomial of degree n in the variable ϵ whose coefficients are curvature integrals (also called quermassintegrals). We prove a similar result in the sub-Riemannian setting of the first Heisenberg group. In contrast to the Euclidean setting, we find that the volume of an ϵ-neighborhood with respect to the Heisenberg metric is an analytic function of ϵ that is generally not a polynomial. The coefficients of the series expansion can be explicitly written in terms of integrals of iteratively defined canonical polynomials of just five curvature terms.

Frobenius Solution for Legendre's Equation, Rodrigue's Formula and Normalization

The Frobenius solution to Legendre/s equation is developed in detail as is Rodrigue's formula, which is employed to normalize Legendre polynomials.

A Universal Formula for Continuous Utility

A single formula assigns a continuous utility function to every representable preference relation.

Ms. Praed. 31 - Formula novitiorum, p. III

Vorbesitzer: Dominikanerkloster Frankfurt am Main

Forward Stagewise Naive Bayes

The naïve Bayes approach is a simple but often satisfactory method for supervised classification. In this paper, we focus on the naïve Bayes model and propose the application of regularization techniques to learn a naïve Bayes classifier. The main contribution of the paper is a stagewise version of the selective naïve Bayes, which can be considered a regularized version of the naïve Bayes model. We call it forward stagewise naïve Bayes. For comparison’s sake, we also introduce an explicitly regularized formulation of the naïve Bayes model, where conditional independence (absence of arcs) is promoted via an L 1/L 2-group penalty on the parameters that define the conditional probability distributions. Although already published in the literature, this idea has only been applied for continuous predictors. We extend this formulation to discrete predictors and propose a modification that yields an adaptive penalization. We show that, whereas the L 1/L 2 group penalty formulation only discards irrelevant predictors, the forward stagewise naïve Bayes can discard both irrelevant and redundant predictors, which are known to be harmful for the naïve Bayes classifier. Both approaches, however, usually improve the classical naïve Bayes model’s accuracy.

Publishing Linked Data - There is no One-Size-Fits-All Formula

Publishing Linked Data is a process that involves several design decisions and technologies. Although some initial guidelines have been already provided by Linked Data publishers, these are still far from covering all the steps that are necessary (from data source selection to publication) or giving enough details about all these steps, technologies, intermediate products, etc. Furthermore, given the variety of data sources from which Linked Data can be generated, we believe that it is possible to have a single and uni�ed method for publishing Linked Data, but we should rely on di�erent techniques, technologies and tools for particular datasets of a given domain. In this paper we present a general method for publishing Linked Data and the application of the method to cover di�erent sources from di�erent domains.