29,655 research outputs found
An isogeometric finite element formulation for phase transitions on deforming surfaces
This paper presents a general theory and isogeometric finite element
implementation for studying mass conserving phase transitions on deforming
surfaces. The mathematical problem is governed by two coupled fourth-order
nonlinear partial differential equations (PDEs) that live on an evolving
two-dimensional manifold. For the phase transitions, the PDE is the
Cahn-Hilliard equation for curved surfaces, which can be derived from surface
mass balance in the framework of irreversible thermodynamics. For the surface
deformation, the PDE is the (vector-valued) Kirchhoff-Love thin shell equation.
Both PDEs can be efficiently discretized using -continuous interpolations
without derivative degrees-of-freedom (dofs). Structured NURBS and unstructured
spline spaces with pointwise -continuity are utilized for these
interpolations. The resulting finite element formulation is discretized in time
by the generalized- scheme with adaptive time-stepping, and it is fully
linearized within a monolithic Newton-Raphson approach. A curvilinear surface
parameterization is used throughout the formulation to admit general surface
shapes and deformations. The behavior of the coupled system is illustrated by
several numerical examples exhibiting phase transitions on deforming spheres,
tori and double-tori.Comment: fixed typos, extended literature review, added clarifying notes to
the text, added supplementary movie file
Towards a New Science of a Clinical Data Intelligence
In this paper we define Clinical Data Intelligence as the analysis of data
generated in the clinical routine with the goal of improving patient care. We
define a science of a Clinical Data Intelligence as a data analysis that
permits the derivation of scientific, i.e., generalizable and reliable results.
We argue that a science of a Clinical Data Intelligence is sensible in the
context of a Big Data analysis, i.e., with data from many patients and with
complete patient information. We discuss that Clinical Data Intelligence
requires the joint efforts of knowledge engineering, information extraction
(from textual and other unstructured data), and statistics and statistical
machine learning. We describe some of our main results as conjectures and
relate them to a recently funded research project involving two major German
university hospitals.Comment: NIPS 2013 Workshop: Machine Learning for Clinical Data Analysis and
Healthcare, 201
Ask, and shall you receive?: Understanding Desire Fulfillment in Natural Language Text
The ability to comprehend wishes or desires and their fulfillment is
important to Natural Language Understanding. This paper introduces the task of
identifying if a desire expressed by a subject in a given short piece of text
was fulfilled. We propose various unstructured and structured models that
capture fulfillment cues such as the subject's emotional state and actions. Our
experiments with two different datasets demonstrate the importance of
understanding the narrative and discourse structure to address this task
Scientific Visualization Using the Flow Analysis Software Toolkit (FAST)
Over the past few years the Flow Analysis Software Toolkit (FAST) has matured into a useful tool for visualizing and analyzing scientific data on high-performance graphics workstations. Originally designed for visualizing the results of fluid dynamics research, FAST has demonstrated its flexibility by being used in several other areas of scientific research. These research areas include earth and space sciences, acid rain and ozone modelling, and automotive design, just to name a few. This paper describes the current status of FAST, including the basic concepts, architecture, existing functionality and features, and some of the known applications for which FAST is being used. A few of the applications, by both NASA and non-NASA agencies, are outlined in more detail. Described in the Outlines are the goals of each visualization project, the techniques or 'tricks' used lo produce the desired results, and custom modifications to FAST, if any, done to further enhance the analysis. Some of the future directions for FAST are also described
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