59,577 research outputs found
A model of brain morphological changes related to aging and Alzheimer's disease from cross-sectional assessments
In this study we propose a deformation-based framework to jointly model the
influence of aging and Alzheimer's disease (AD) on the brain morphological
evolution. Our approach combines a spatio-temporal description of both
processes into a generative model. A reference morphology is deformed along
specific trajectories to match subject specific morphologies. It is used to
define two imaging progression markers: 1) a morphological age and 2) a disease
score. These markers can be computed locally in any brain region. The approach
is evaluated on brain structural magnetic resonance images (MRI) from the ADNI
database. The generative model is first estimated on a control population,
then, for each subject, the markers are computed for each acquisition. The
longitudinal evolution of these markers is then studied in relation with the
clinical diagnosis of the subjects and used to generate possible morphological
evolution. In the model, the morphological changes associated with normal aging
are mainly found around the ventricles, while the Alzheimer's disease specific
changes are more located in the temporal lobe and the hippocampal area. The
statistical analysis of these markers highlights differences between clinical
conditions even though the inter-subject variability is quiet high. In this
context, the model can be used to generate plausible morphological trajectories
associated with the disease. Our method gives two interpretable scalar imaging
biomarkers assessing the effects of aging and disease on brain morphology at
the individual and population level. These markers confirm an acceleration of
apparent aging for Alzheimer's subjects and can help discriminate clinical
conditions even in prodromal stages. More generally, the joint modeling of
normal and pathological evolutions shows promising results to describe
age-related brain diseases over long time scales.Comment: NeuroImage, Elsevier, In pres
Preference-Based Learning for Exoskeleton Gait Optimization
This paper presents a personalized gait optimization framework for lower-body exoskeletons. Rather than optimizing numerical objectives such as the mechanical cost of transport, our approach directly learns from user preferences, e.g., for comfort. Building upon work in preference-based interactive learning, we present the CoSpar algorithm. CoSpar prompts the user to give pairwise preferences between trials and suggest improvements; as exoskeleton walking is a non-intuitive behavior, users can provide preferences more easily and reliably than numerical feedback. We show that CoSpar performs competitively in simulation and demonstrate a prototype implementation of CoSpar on a lower-body exoskeleton to optimize human walking trajectory features. In the experiments, CoSpar consistently found user-preferred parameters of the exoskeleton’s walking gait, which suggests that it is a promising starting point for adapting and personalizing exoskeletons (or other assistive devices) to individual users
Reproducibility, accuracy and performance of the Feltor code and library on parallel computer architectures
Feltor is a modular and free scientific software package. It allows
developing platform independent code that runs on a variety of parallel
computer architectures ranging from laptop CPUs to multi-GPU distributed memory
systems. Feltor consists of both a numerical library and a collection of
application codes built on top of the library. Its main target are two- and
three-dimensional drift- and gyro-fluid simulations with discontinuous Galerkin
methods as the main numerical discretization technique. We observe that
numerical simulations of a recently developed gyro-fluid model produce
non-deterministic results in parallel computations. First, we show how we
restore accuracy and bitwise reproducibility algorithmically and
programmatically. In particular, we adopt an implementation of the exactly
rounded dot product based on long accumulators, which avoids accuracy losses
especially in parallel applications. However, reproducibility and accuracy
alone fail to indicate correct simulation behaviour. In fact, in the physical
model slightly different initial conditions lead to vastly different end
states. This behaviour translates to its numerical representation. Pointwise
convergence, even in principle, becomes impossible for long simulation times.
In a second part, we explore important performance tuning considerations. We
identify latency and memory bandwidth as the main performance indicators of our
routines. Based on these, we propose a parallel performance model that predicts
the execution time of algorithms implemented in Feltor and test our model on a
selection of parallel hardware architectures. We are able to predict the
execution time with a relative error of less than 25% for problem sizes between
0.1 and 1000 MB. Finally, we find that the product of latency and bandwidth
gives a minimum array size per compute node to achieve a scaling efficiency
above 50% (both strong and weak)
Exit polling and racial bloc voting: Combining individual-level and RC ecological data
Despite its shortcomings, cross-level or ecological inference remains a
necessary part of some areas of quantitative inference, including in United
States voting rights litigation. Ecological inference suffers from a lack of
identification that, most agree, is best addressed by incorporating
individual-level data into the model. In this paper we test the limits of such
an incorporation by attempting it in the context of drawing inferences about
racial voting patterns using a combination of an exit poll and precinct-level
ecological data; accurate information about racial voting patterns is needed to
assess triggers in voting rights laws that can determine the composition of
United States legislative bodies. Specifically, we extend and study a hybrid
model that addresses two-way tables of arbitrary dimension. We apply the hybrid
model to an exit poll we administered in the City of Boston in 2008. Using the
resulting data as well as simulation, we compare the performance of a pure
ecological estimator, pure survey estimators using various sampling schemes and
our hybrid. We conclude that the hybrid estimator offers substantial benefits
by enabling substantive inferences about voting patterns not practicably
available without its use.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS353 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Toward future 'mixed reality' learning spaces for STEAM education
Digital technology is becoming more integrated and part of modern society. As this begins to happen, technologies including augmented reality, virtual reality, 3d printing and user supplied mobile devices (collectively referred to as mixed reality) are often being touted as likely to become more a part of the classroom and learning environment. In the discipline areas of STEAM education, experts are expected to be at the forefront of technology and how it might fit into their classroom. This is especially important because increasingly, educators are finding themselves surrounded by new learners that expect to be engaged with participatory, interactive, sensory-rich, experimental activities with greater opportunities for student input and creativity. This paper will explore learner and academic perspectives on mixed reality case studies in 3d spatial design (multimedia and architecture), paramedic science and information technology, through the use of existing data as well as additional one-on-one interviews around the use of mixed reality in the classroom. Results show that mixed reality can provide engagement, critical thinking and problem solving benefits for students in line with this new generation of learners, but also demonstrates that more work needs to be done to refine mixed reality solutions for the classroom
Optimization as an analysis tool for human complex decision making
We present a problem class of mixed-integer nonlinear programs (MINLPs) with nonconvex continuous relaxations which stem from economic test scenarios that are used in the analysis of human complex problem solving. In a round-based scenario participants hold an executive function. A posteriori a performance indicator is calculated and correlated to personal measures such as intelligence, working memory, or emotion regulation. Altogether, we investigate 2088 optimization problems that differ in size and initial conditions, based on real-world experimental data from 12 rounds of 174 participants. The goals are twofold. First, from the optimal solutions we gain additional insight into a complex system, which facilitates the analysis of a participant’s performance in the test. Second, we propose a methodology to automatize this process by providing a new criterion based on the solution of a series of optimization problems. By providing a mathematical optimization model and this methodology, we disprove the assumption that the “fruit fly of complex problem solving,” the Tailorshop scenario that has been used for dozens of published studies, is not mathematically accessible—although it turns out to be extremely challenging even for advanced state-of-the-art global optimization algorithms and we were not able to solve all instances to global optimality in reasonable time in this study. The publicly available computational tool Tobago [TOBAGO web site https://sourceforge.net/projects/tobago] can be used to automatically generate problem instances of various complexity, contains interfaces to AMPL and GAMS, and is hence ideally suited as a testbed for different kinds of algorithms and solvers. Computational practice is reported with respect to the influence of integer variables, problem dimension, and local versus global optimization with different optimization codes
An Immersive Telepresence System using RGB-D Sensors and Head Mounted Display
We present a tele-immersive system that enables people to interact with each
other in a virtual world using body gestures in addition to verbal
communication. Beyond the obvious applications, including general online
conversations and gaming, we hypothesize that our proposed system would be
particularly beneficial to education by offering rich visual contents and
interactivity. One distinct feature is the integration of egocentric pose
recognition that allows participants to use their gestures to demonstrate and
manipulate virtual objects simultaneously. This functionality enables the
instructor to ef- fectively and efficiently explain and illustrate complex
concepts or sophisticated problems in an intuitive manner. The highly
interactive and flexible environment can capture and sustain more student
attention than the traditional classroom setting and, thus, delivers a
compelling experience to the students. Our main focus here is to investigate
possible solutions for the system design and implementation and devise
strategies for fast, efficient computation suitable for visual data processing
and network transmission. We describe the technique and experiments in details
and provide quantitative performance results, demonstrating our system can be
run comfortably and reliably for different application scenarios. Our
preliminary results are promising and demonstrate the potential for more
compelling directions in cyberlearning.Comment: IEEE International Symposium on Multimedia 201
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