2,153 research outputs found
Riemannian tangent space mapping and elastic net regularization for cost-effective EEG markers of brain atrophy in Alzheimer's disease
The diagnosis of Alzheimer's disease (AD) in routine clinical practice is
most commonly based on subjective clinical interpretations. Quantitative
electroencephalography (QEEG) measures have been shown to reflect
neurodegenerative processes in AD and might qualify as affordable and thereby
widely available markers to facilitate the objectivization of AD assessment.
Here, we present a novel framework combining Riemannian tangent space mapping
and elastic net regression for the development of brain atrophy markers. While
most AD QEEG studies are based on small sample sizes and psychological test
scores as outcome measures, here we train and test our models using data of one
of the largest prospective EEG AD trials ever conducted, including MRI
biomarkers of brain atrophy.Comment: Presented at NIPS 2017 Workshop on Machine Learning for Healt
Contamination source inference in water distribution networks
We study the inference of the origin and the pattern of contamination in
water distribution networks. We assume a simplified model for the dyanmics of
the contamination spread inside a water distribution network, and assume that
at some random location a sensor detects the presence of contaminants. We
transform the source location problem into an optimization problem by
considering discrete times and a binary contaminated/not contaminated state for
the nodes of the network. The resulting problem is solved by Mixed Integer
Linear Programming. We test our results on random networks as well as in the
Modena city network
Improving process algebra model structure and parameters in infectious disease epidemiology through data mining
Computational models are increasingly used to assist decision-making in public health epidemiology, but achieving the best model is a complex task due to the interaction of many components and variability of parameter values causing radically different dynamics. The modelling process can be enhanced through the use of data mining techniques. Here, we demonstrate this by applying association rules and clustering techniques to two stages of mod- elling: identifying pertinent structures in the initial model creation stage, and choosing optimal parameters to match that model to observed data. This is illustrated through application to the study of the circulating mumps virus in Scotland, 2004-2015
Multilayer Networks
In most natural and engineered systems, a set of entities interact with each
other in complicated patterns that can encompass multiple types of
relationships, change in time, and include other types of complications. Such
systems include multiple subsystems and layers of connectivity, and it is
important to take such "multilayer" features into account to try to improve our
understanding of complex systems. Consequently, it is necessary to generalize
"traditional" network theory by developing (and validating) a framework and
associated tools to study multilayer systems in a comprehensive fashion. The
origins of such efforts date back several decades and arose in multiple
disciplines, and now the study of multilayer networks has become one of the
most important directions in network science. In this paper, we discuss the
history of multilayer networks (and related concepts) and review the exploding
body of work on such networks. To unify the disparate terminology in the large
body of recent work, we discuss a general framework for multilayer networks,
construct a dictionary of terminology to relate the numerous existing concepts
to each other, and provide a thorough discussion that compares, contrasts, and
translates between related notions such as multilayer networks, multiplex
networks, interdependent networks, networks of networks, and many others. We
also survey and discuss existing data sets that can be represented as
multilayer networks. We review attempts to generalize single-layer-network
diagnostics to multilayer networks. We also discuss the rapidly expanding
research on multilayer-network models and notions like community structure,
connected components, tensor decompositions, and various types of dynamical
processes on multilayer networks. We conclude with a summary and an outlook.Comment: Working paper; 59 pages, 8 figure
Markov Chain Methods For Analyzing Complex Transport Networks
We have developed a steady state theory of complex transport networks used to
model the flow of commodity, information, viruses, opinions, or traffic. Our
approach is based on the use of the Markov chains defined on the graph
representations of transport networks allowing for the effective network
design, network performance evaluation, embedding, partitioning, and network
fault tolerance analysis. Random walks embed graphs into Euclidean space in
which distances and angles acquire a clear statistical interpretation. Being
defined on the dual graph representations of transport networks random walks
describe the equilibrium configurations of not random commodity flows on
primary graphs. This theory unifies many network concepts into one framework
and can also be elegantly extended to describe networks represented by directed
graphs and multiple interacting networks.Comment: 26 pages, 4 figure
The Faculty Notebook, December 2006
The Faculty Notebook is published periodically by the Office of the Provost at Gettysburg College to bring to the attention of the campus community accomplishments and activities of academic interest. Faculty are encouraged to submit materials for consideration for publication to the Associate Provost for Faculty Development. Copies of this publication are available at the Office of the Provost
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