43,806 research outputs found
Incremental Cycle Detection, Topological Ordering, and Strong Component Maintenance
We present two on-line algorithms for maintaining a topological order of a
directed -vertex acyclic graph as arcs are added, and detecting a cycle when
one is created. Our first algorithm handles arc additions in
time. For sparse graphs (), this bound improves the best previous
bound by a logarithmic factor, and is tight to within a constant factor among
algorithms satisfying a natural {\em locality} property. Our second algorithm
handles an arbitrary sequence of arc additions in time. For
sufficiently dense graphs, this bound improves the best previous bound by a
polynomial factor. Our bound may be far from tight: we show that the algorithm
can take time by relating its performance to a
generalization of the -levels problem of combinatorial geometry. A
completely different algorithm running in time was given
recently by Bender, Fineman, and Gilbert. We extend both of our algorithms to
the maintenance of strong components, without affecting the asymptotic time
bounds.Comment: 31 page
A Computational Algebra Approach to the Reverse Engineering of Gene Regulatory Networks
This paper proposes a new method to reverse engineer gene regulatory networks
from experimental data. The modeling framework used is time-discrete
deterministic dynamical systems, with a finite set of states for each of the
variables. The simplest examples of such models are Boolean networks, in which
variables have only two possible states. The use of a larger number of possible
states allows a finer discretization of experimental data and more than one
possible mode of action for the variables, depending on threshold values.
Furthermore, with a suitable choice of state set, one can employ powerful tools
from computational algebra, that underlie the reverse-engineering algorithm,
avoiding costly enumeration strategies. To perform well, the algorithm requires
wildtype together with perturbation time courses. This makes it suitable for
small to meso-scale networks rather than networks on a genome-wide scale. The
complexity of the algorithm is quadratic in the number of variables and cubic
in the number of time points. The algorithm is validated on a recently
published Boolean network model of segment polarity development in Drosophila
melanogaster.Comment: 28 pages, 5 EPS figures, uses elsart.cl
Low Power Depth Estimation of Rigid Objects for Time-of-Flight Imaging
Depth sensing is useful in a variety of applications that range from
augmented reality to robotics. Time-of-flight (TOF) cameras are appealing
because they obtain dense depth measurements with minimal latency. However, for
many battery-powered devices, the illumination source of a TOF camera is power
hungry and can limit the battery life of the device. To address this issue, we
present an algorithm that lowers the power for depth sensing by reducing the
usage of the TOF camera and estimating depth maps using concurrently collected
images. Our technique also adaptively controls the TOF camera and enables it
when an accurate depth map cannot be estimated. To ensure that the overall
system power for depth sensing is reduced, we design our algorithm to run on a
low power embedded platform, where it outputs 640x480 depth maps at 30 frames
per second. We evaluate our approach on several RGB-D datasets, where it
produces depth maps with an overall mean relative error of 0.96% and reduces
the usage of the TOF camera by 85%. When used with commercial TOF cameras, we
estimate that our algorithm can lower the total power for depth sensing by up
to 73%
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