454 research outputs found
Power Euclidean metrics for covariance matrices with application to diffusion tensor imaging
Various metrics for comparing diffusion tensors have been recently proposed
in the literature. We consider a broad family of metrics which is indexed by a
single power parameter. A likelihood-based procedure is developed for choosing
the most appropriate metric from the family for a given dataset at hand. The
approach is analogous to using the Box-Cox transformation that is frequently
investigated in regression analysis. The methodology is illustrated with a
simulation study and an application to a real dataset of diffusion tensor
images of canine hearts
Detection of brain functional-connectivity difference in post-stroke patients using group-level covariance modeling
Functional brain connectivity, as revealed through distant correlations in
the signals measured by functional Magnetic Resonance Imaging (fMRI), is a
promising source of biomarkers of brain pathologies. However, establishing and
using diagnostic markers requires probabilistic inter-subject comparisons.
Principled comparison of functional-connectivity structures is still a
challenging issue. We give a new matrix-variate probabilistic model suitable
for inter-subject comparison of functional connectivity matrices on the
manifold of Symmetric Positive Definite (SPD) matrices. We show that this model
leads to a new algorithm for principled comparison of connectivity coefficients
between pairs of regions. We apply this model to comparing separately
post-stroke patients to a group of healthy controls. We find
neurologically-relevant connection differences and show that our model is more
sensitive that the standard procedure. To the best of our knowledge, these
results are the first report of functional connectivity differences between a
single-patient and a group and thus establish an important step toward using
functional connectivity as a diagnostic tool
Characterizing Distances of Networks on the Tensor Manifold
At the core of understanding dynamical systems is the ability to maintain and
control the systems behavior that includes notions of robustness,
heterogeneity, or regime-shift detection. Recently, to explore such functional
properties, a convenient representation has been to model such dynamical
systems as a weighted graph consisting of a finite, but very large number of
interacting agents. This said, there exists very limited relevant statistical
theory that is able cope with real-life data, i.e., how does perform analysis
and/or statistics over a family of networks as opposed to a specific network or
network-to-network variation. Here, we are interested in the analysis of
network families whereby each network represents a point on an underlying
statistical manifold. To do so, we explore the Riemannian structure of the
tensor manifold developed by Pennec previously applied to Diffusion Tensor
Imaging (DTI) towards the problem of network analysis. In particular, while
this note focuses on Pennec definition of geodesics amongst a family of
networks, we show how it lays the foundation for future work for developing
measures of network robustness for regime-shift detection. We conclude with
experiments highlighting the proposed distance on synthetic networks and an
application towards biological (stem-cell) systems.Comment: This paper is accepted at 8th International Conference on Complex
Networks 201
Magnetic relaxation of exchange biased (Pt/Co) multilayers studied by time-resolved Kerr microscopy
Magnetization relaxation of exchange biased (Pt/Co)5/Pt/IrMn multilayers with
perpendicular anisotropy was investigated by time-resolved Kerr microscopy.
Magnetization reversal occurs by nucleation and domain wall propagation for
both descending and ascending applied fields, but a much larger nucleation
density is observed for the descending branch, where the field is applied
antiparallel to the exchange bias field direction. These results can be
explained by taking into account the presence of local inhomogeneities of the
exchange bias field.Comment: To appear in Physical Review B (October 2005
Interplay between magnetic anisotropy and interlayer coupling in nanosecond magnetization reversal of spin-valve trilayers
The influence of magnetic anisotropy on nanosecond magnetization reversal in
coupled FeNi/Cu/Co trilayers was studied using a photoelectron emission
microscope combined with x-ray magnetic circular dicroism. In quasi-isotropic
samples the reversal of the soft FeNi layer is determined by domain wall
pinning that leads to the formation of small and irregular domains. In samples
with uniaxial magnetic anisotropy, the domains are larger and the influence of
local interlayer coupling dominates the domain structure and the reversal of
the FeNi layer
Stochastic Development Regression on Non-Linear Manifolds
We introduce a regression model for data on non-linear manifolds. The model
describes the relation between a set of manifold valued observations, such as
shapes of anatomical objects, and Euclidean explanatory variables. The approach
is based on stochastic development of Euclidean diffusion processes to the
manifold. Defining the data distribution as the transition distribution of the
mapped stochastic process, parameters of the model, the non-linear analogue of
design matrix and intercept, are found via maximum likelihood. The model is
intrinsically related to the geometry encoded in the connection of the
manifold. We propose an estimation procedure which applies the Laplace
approximation of the likelihood function. A simulation study of the performance
of the model is performed and the model is applied to a real dataset of Corpus
Callosum shapes
Bonobo Conservation as a means for Local Development: an Innovative Local Initiative of Community-based Conservation in the DemocraticRepublic of the Congo
International audienceThe Democratic Republic of the Congo (DRC) ranks fifth in the world in terms of biodiversity (fauna and flora) and first for mammal diversity in Africa. There are numerous endemic species including bolobos (Pan paniscus). Bolobos are endangered, threatened mainly by deforestation, poaching and diseases, and the current population is estimated from 15,000 to 50,000 individuals. Nowadays, one national park (Salonga National Park) and six reserves exist for wild bolobo conservation, representing about 73,000 km2 of protected areas over an estimated distribution area of 565,000 km2. In the Bolobo Territory, an original local project of bolobo conservation was initiated in 2001 by the Congolese NGO Mbou-Mon-Tour (MMT). From 2008 to 2013, we studied bolobo-habitat-human interactions in this forest-savanna mosaic habitat, totalizing 12 months of survey over six periods. Besides eco-ethological studies, an ethnoecological approach was developed in order to better understand how the MMT project emerged and how it has evolved. We performed semi-structured interviews, participant observation, and informal discussions with local people. We also used grey literature associated to the region. MMT use the bolobo conservation as a means to reach local development goals, which is the opposite of what is frequent in the “community-based conservation” project managed by environmental NGOs. The location of the community forests and the rules established for regulating activities in these forests were decided by the villagers under the organization of traditional chiefs according to their knowledge on bolobo ecology and range, and the disturbance they perceived of the traditional activities in the forest. Such a process is a novel and promising approach for wildlife conservation that maintains the place of local people and traditional authorities in the decision-making process and the governance
Second-order Democratic Aggregation
Aggregated second-order features extracted from deep convolutional networks
have been shown to be effective for texture generation, fine-grained
recognition, material classification, and scene understanding. In this paper,
we study a class of orderless aggregation functions designed to minimize
interference or equalize contributions in the context of second-order features
and we show that they can be computed just as efficiently as their first-order
counterparts and they have favorable properties over aggregation by summation.
Another line of work has shown that matrix power normalization after
aggregation can significantly improve the generalization of second-order
representations. We show that matrix power normalization implicitly equalizes
contributions during aggregation thus establishing a connection between matrix
normalization techniques and prior work on minimizing interference. Based on
the analysis we present {\gamma}-democratic aggregators that interpolate
between sum ({\gamma}=1) and democratic pooling ({\gamma}=0) outperforming both
on several classification tasks. Moreover, unlike power normalization, the
{\gamma}-democratic aggregations can be computed in a low dimensional space by
sketching that allows the use of very high-dimensional second-order features.
This results in a state-of-the-art performance on several datasets
Shock waves in two-dimensional granular flow: effects of rough walls and polydispersity
We have studied the two-dimensional flow of balls in a small angle funnel,
when either the side walls are rough or the balls are polydisperse. As in
earlier work on monodisperse flows in smooth funnels, we observe the formation
of kinematic shock waves/density waves. We find that for rough walls the flows
are more disordered than for smooth walls and that shock waves generally
propagate more slowly. For rough wall funnel flow, we show that the shock
velocity and frequency obey simple scaling laws. These scaling laws are
consistent with those found for smooth wall flow, but here they are cleaner
since there are fewer packing-site effects and we study a wider range of
parameters. For pipe flow (parallel side walls), rough walls support many shock
waves, while smooth walls exhibit fewer or no shock waves. For funnel flows of
balls with varying sizes, we find that flows with weak polydispersity behave
qualitatively similar to monodisperse flows. For strong polydispersity, scaling
breaks down and the shock waves consist of extended areas where the funnel is
blocked completely.Comment: 11 pages, 15 figures; accepted for PR
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