Software Usability:A Comparison Between Two Tree-Structured Data Transformation Languages

This paper presents the results of a software usability study, involving both subjective and objective evaluation. It compares a popular XML data transformation language (XSLT) and a general purpose rule-based tree manipulation language which addresses some of the XML and XSLT limitations. The benefits of the evaluation study are discussed

Non-parametric Bayesian modeling of complex networks

Modeling structure in complex networks using Bayesian non-parametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This paper provides a gentle introduction to non-parametric Bayesian modeling of complex networks: Using an infinite mixture model as running example we go through the steps of deriving the model as an infinite limit of a finite parametric model, inferring the model parameters by Markov chain Monte Carlo, and checking the model's fit and predictive performance. We explain how advanced non-parametric models for complex networks can be derived and point out relevant literature

GLAND CELLS IN HYDRA

The proliferative capacity of gland cells in Hydra attenuata was investigated. The results indicate that both gland cell proliferation and interstitial cell differentiation to gland cells contribute to the maintenance of the whole population. On the basis of [3H]thymidine incorporation and nuclear DNA measurements, gland cells consist of at least three different populations. One population consists of rapidly proliferating cells with a cell cycle of about 72 h. These cells are distributed throughout the body column. In the lower gastric region there is a population of non-cycling cells in G2 while in the upper gastric region there is a population of noncycling cells in G1. About half the G1 population becomes a new antigen, SEC 1, which is typical of mucus cells

Transonic wing DFVLR-F4 as European test model

A transonic wing, the DFVLR-F4 was designed and tested as a model in European transonic wind tunnels and was found to give performance improvements over conventional wings. One reason for the improvement was the reduction of compression shocks in the transonic region as the result of improved wing design

Summary of working group g: beam material interaction

For the first time, the workshop on High-Intensity and High-Brightness Hadron Beams (HB2010), held at Morschach, Switzerland and organized by the Paul Scherrer Institute, included a Working group dealing with the interaction between beam and material. Due to the high power beams of existing and future facilities, this topic is already of great relevance for such machines and is expected to become even more important in the future. While more specialized workshops related to topics of radiation damage, activation or thermo - mechanical calculations, already exist, HB2010 provided the occasion to discuss the interplay of these topics, focusing on components like targets, beam dumps and collimators, whose reliability are crucial for a user facility. In addition, a broader community of people working on a variety of issues related to the operation of accelerators could be informed and their interest sparked.Comment: 3 pp. 46th ICFA Advanced Beam Dynamics Workshop HB2010, Sep 27 - Oct 1 2010: Morschach, Switzerlan

Bayesian Dropout

Dropout has recently emerged as a powerful and simple method for training neural networks preventing co-adaptation by stochastically omitting neurons. Dropout is currently not grounded in explicit modelling assumptions which so far has precluded its adoption in Bayesian modelling. Using Bayesian entropic reasoning we show that dropout can be interpreted as optimal inference under constraints. We demonstrate this on an analytically tractable regression model providing a Bayesian interpretation of its mechanism for regularizing and preventing co-adaptation as well as its connection to other Bayesian techniques. We also discuss two general approximate techniques for applying Bayesian dropout for general models, one based on an analytical approximation and the other on stochastic variational techniques. These techniques are then applied to a Baysian logistic regression problem and are shown to improve performance as the model become more misspecified. Our framework roots dropout as a theoretically justified and practical tool for statistical modelling allowing Bayesians to tap into the benefits of dropout training.Comment: 21 pages, 3 figures. Manuscript prepared 2014 and awaiting submissio