69,818 research outputs found
Stigmergy-based modeling to discover urban activity patterns from positioning data
Positioning data offer a remarkable source of information to analyze crowds
urban dynamics. However, discovering urban activity patterns from the emergent
behavior of crowds involves complex system modeling. An alternative approach is
to adopt computational techniques belonging to the emergent paradigm, which
enables self-organization of data and allows adaptive analysis. Specifically,
our approach is based on stigmergy. By using stigmergy each sample position is
associated with a digital pheromone deposit, which progressively evaporates and
aggregates with other deposits according to their spatiotemporal proximity.
Based on this principle, we exploit positioning data to identify high density
areas (hotspots) and characterize their activity over time. This
characterization allows the comparison of dynamics occurring in different days,
providing a similarity measure exploitable by clustering techniques. Thus, we
cluster days according to their activity behavior, discovering unexpected urban
activity patterns. As a case study, we analyze taxi traces in New York City
during 2015
Visualizing dimensionality reduction of systems biology data
One of the challenges in analyzing high-dimensional expression data is the
detection of important biological signals. A common approach is to apply a
dimension reduction method, such as principal component analysis. Typically,
after application of such a method the data is projected and visualized in the
new coordinate system, using scatter plots or profile plots. These methods
provide good results if the data have certain properties which become visible
in the new coordinate system and which were hard to detect in the original
coordinate system. Often however, the application of only one method does not
suffice to capture all important signals. Therefore several methods addressing
different aspects of the data need to be applied. We have developed a framework
for linear and non-linear dimension reduction methods within our visual
analytics pipeline SpRay. This includes measures that assist the interpretation
of the factorization result. Different visualizations of these measures can be
combined with functional annotations that support the interpretation of the
results. We show an application to high-resolution time series microarray data
in the antibiotic-producing organism Streptomyces coelicolor as well as to
microarray data measuring expression of cells with normal karyotype and cells
with trisomies of human chromosomes 13 and 21
Identifying phase synchronization clusters in spatially extended dynamical systems
We investigate two recently proposed multivariate time series analysis
techniques that aim at detecting phase synchronization clusters in spatially
extended, nonstationary systems with regard to field applications. The starting
point of both techniques is a matrix whose entries are the mean phase coherence
values measured between pairs of time series. The first method is a mean field
approach which allows to define the strength of participation of a subsystem in
a single synchronization cluster. The second method is based on an eigenvalue
decomposition from which a participation index is derived that characterizes
the degree of involvement of a subsystem within multiple synchronization
clusters. Simulating multiple clusters within a lattice of coupled Lorenz
oscillators we explore the limitations and pitfalls of both methods and
demonstrate (a) that the mean field approach is relatively robust even in
configurations where the single cluster assumption is not entirely fulfilled,
and (b) that the eigenvalue decomposition approach correctly identifies the
simulated clusters even for low coupling strengths. Using the eigenvalue
decomposition approach we studied spatiotemporal synchronization clusters in
long-lasting multichannel EEG recordings from epilepsy patients and obtained
results that fully confirm findings from well established neurophysiological
examination techniques. Multivariate time series analysis methods such as
synchronization cluster analysis that account for nonlinearities in the data
are expected to provide complementary information which allows to gain deeper
insights into the collective dynamics of spatially extended complex systems
Forman-Ricci flow for change detection in large dynamic data sets
We present a viable solution to the challenging question of change detection
in complex networks inferred from large dynamic data sets. Building on Forman's
discretization of the classical notion of Ricci curvature, we introduce a novel
geometric method to characterize different types of real-world networks with an
emphasis on peer-to-peer networks. Furthermore we adapt the classical Ricci
flow that already proved to be a powerful tool in image processing and
graphics, to the case of undirected and weighted networks. The application of
the proposed method on peer-to-peer networks yields insights into topological
properties and the structure of their underlying data.Comment: Conference paper, accepted at ICICS 2016. (Updated version
The K2 Mission: Characterization and Early results
The K2 mission will make use of the Kepler spacecraft and its assets to
expand upon Kepler's groundbreaking discoveries in the fields of exoplanets and
astrophysics through new and exciting observations. K2 will use an innovative
way of operating the spacecraft to observe target fields along the ecliptic for
the next 2-3 years. Early science commissioning observations have shown an
estimated photometric precision near 400 ppm in a single 30 minute observation,
and a 6-hour photometric precision of 80 ppm (both at V=12). The K2 mission
offers long-term, simultaneous optical observation of thousands of objects at a
precision far better than is achievable from ground-based telescopes. Ecliptic
fields will be observed for approximately 75-days enabling a unique exoplanet
survey which fills the gaps in duration and sensitivity between the Kepler and
TESS missions, and offers pre-launch exoplanet target identification for JWST
transit spectroscopy. Astrophysics observations with K2 will include studies of
young open clusters, bright stars, galaxies, supernovae, and asteroseismology.Comment: 25 pages, 11 figures, Accepted to PAS
- …