74,376 research outputs found
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
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