Parasites and other exploiters of herbivore populations
can provide tremendous benefits to plant life by
protecting it from destruction, in a process known as a
trophic cascade. Thus, the preservation of certain
parasites, such as entomopathogenic nematodes, can be a
potent form of natural pest control both in the wild and
in agricultural situations. Past mathematical modeling of
one such nematode-host interaction showed that the
parasitic population should undergo violent two-year
cycles, with dangerously low numbers every other year.
However, field observations indicate that the interaction
may often be fairly persistent. Building upon the
previous model, we investigate the possible effects of an
alternate host on nematode persistence, and find that the
persistence time of the parasitic nematode is extended
only if the second host is sufficiently inaccessible.
White Matter Hyperintensities (WMHs) are dysfunctional
regions of the brain whose occurrence strongly
correlates to the presence and progression of several
neurological conditions such as Alzheimer's disease.
This work presents a method that employs a Markov Random
Field (MRF) approach for detection of these WMHs based
on run-time PD-, T1-, and T2-weighted structural
magnetic resonance (MR) images of the brain along with
labeled training examples. Unlike most prior
approaches, the method is able to reliably detect WMHs
in the absence of fluid-attenuated (FLAIR) images. Its
success is due to the learning of probabilistic models
of WMH spatial distribution and neighborhood
dependencies from ground-truth examples of FLAIR-based
WMH detections. These models are combined with a
probabilistic lognormal mixture model of the PD, T1, and
T2 intensities of WMHs in a MRF framework that provides
the machinery for inferring the positions of WMHs in
novel test images. The method is shown to accurately
detect WMHs in a set of 126 elderly subjects from an
academic dementia clinic. The use of anatomical prior
knowledge that captures the known asymmetric anatomical
course of WMH progression is shown to increase the
accuracy of WMH detection over MRFs that smooth the WMH
detections isotropically.
Networks, and neural networks in particular, are a rapidly expanding field of
study, with many promising applications. However, the relation between
topology and function in neural networks is still poorly understood. Using
two measures of functional similarity and exploring three topological classes
of connectivity, we have examined the extent to which a spiking neural
network.s function changes as its topology is modified. We find that the
degree to which functionality is altered depends on both network size and
the underlying base topology of the original reference network. In general,
smaller networks tend to show less average functional change for a given
number of edge modifications, but also much larger variability in the
amount of this change. Additionally, networks generated using a small-
world model [1] display significantly less functional change than networks
with scale-free degree distributions or simple Erdos-Renyi random graphs.
It is not well understood how individual privacy concerns
and trust influence social interactions within social
networking sites. A survey of a popular social networking
site, Facebook, compared perceptions of trust and privacy
concerns, along with the willingness to share information
to develop new relationships. The survey data reported
different levels of privacy concern and the use of the
Facebook account. Facebook members expressed
significantly greater trust in Facebook and in other
Facebook members, and hence were more willing to share
identifying information even though many Facebook users
(in this study) have not read the .Facebook.s Privacy
Policy. but still feel that their privacy is well
protected by Facebook. Many in this study use Facebook
more than e-mail as the .new medium. to communicate.
These results suggest that in online interaction, trust
is not as necessary in building new relationships unlike
face to face encounters. This study also shows that in a
social networking site, trust and the willingness to
share information do not automatically translate into new
social interaction. This study demonstrates the direct
relationship that online interaction has with the amount
of time spent on that site.
Systems of polynomial equations over an
algebraically-closed field K can be concisely used to
model many combinatorial problems. In this way, a
combinatorial problem is feasible (e.g., a graph is
3-colorable or has an independent set of size k) if and
only if a related system of polynomial equations has a
solution over K. If the combinatorial problem is
infeasible, Hilbert's Nullstellensatz and a large-scale
linear algebra computation yields a certificate of
infeasibility. Thus, unless P = NP, there must exist an
infinite sequence of infeasible instances for each hard
combinatorial problem where the minimum-degree of a
Hilbert Nullstellensatz infeasibility certificate grows.
Fully-Automated White Matter Hyperintensity Detection With Anatomical Prior Knowledge and Without FLAIR
Christopher Schwarz, Evan Fletcher, Charles DeCarli, Owen Carmichael
Topological Influences on Function in a Spiking Neural Network
Richard Watson, Nick Travers
An Evaluation of Identity-Sharing Behavior, Privacy Concerns and Trust in a Social Network Community
Avinash Nayak, Raissa D'Souza
P, NP and the Nullstellensatz: Independent Set and Graph-3-Coloring Infeasibility Certificates
Susan Margulies, Jesus De Loera, Jon Lee, Peter Malkin
We show that the minimum-degree of a Nullstellensatz
certificate for the non-existence of an independent set
of size greater than the size of the largest independent
set in the graph is the size of the largest independent
set in the graph. Moreover, such a certificate contains
at least one term per independent set in G. By contrast,
for graph-3-colorability, the Nullstellensatz-Linear
Algebra (NulLA) algorithm proves the infeasibility of
instances having thousands of nodes and tens of thousands
of edges.