37,217 research outputs found
A deterministic algorithm for fitting a step function to a weighted point-set
Given a set of n points in the plane, each point having a positive weight,
and an integer k>0, we present an optimal O(n \log n)-time deterministic
algorithm to compute a step function with k steps that minimizes the maximum
weighted vertical distance to the input points. It matches the expected time
bound of the best known randomized algorithm for this problem. Our approach
relies on Cole's improved parametric searching technique.Comment: 5 pages, 2 figure
Parameter estimation for Boolean models of biological networks
Boolean networks have long been used as models of molecular networks and play
an increasingly important role in systems biology. This paper describes a
software package, Polynome, offered as a web service, that helps users
construct Boolean network models based on experimental data and biological
input. The key feature is a discrete analog of parameter estimation for
continuous models. With only experimental data as input, the software can be
used as a tool for reverse-engineering of Boolean network models from
experimental time course data.Comment: Web interface of the software is available at
http://polymath.vbi.vt.edu/polynome
Interpolating point spread function anisotropy
Planned wide-field weak lensing surveys are expected to reduce the
statistical errors on the shear field to unprecedented levels. In contrast,
systematic errors like those induced by the convolution with the point spread
function (PSF) will not benefit from that scaling effect and will require very
accurate modeling and correction. While numerous methods have been devised to
carry out the PSF correction itself, modeling of the PSF shape and its spatial
variations across the instrument field of view has, so far, attracted much less
attention. This step is nevertheless crucial because the PSF is only known at
star positions while the correction has to be performed at any position on the
sky. A reliable interpolation scheme is therefore mandatory and a popular
approach has been to use low-order bivariate polynomials. In the present paper,
we evaluate four other classical spatial interpolation methods based on splines
(B-splines), inverse distance weighting (IDW), radial basis functions (RBF) and
ordinary Kriging (OK). These methods are tested on the Star-challenge part of
the GRavitational lEnsing Accuracy Testing 2010 (GREAT10) simulated data and
are compared with the classical polynomial fitting (Polyfit). We also test all
our interpolation methods independently of the way the PSF is modeled, by
interpolating the GREAT10 star fields themselves (i.e., the PSF parameters are
known exactly at star positions). We find in that case RBF to be the clear
winner, closely followed by the other local methods, IDW and OK. The global
methods, Polyfit and B-splines, are largely behind, especially in fields with
(ground-based) turbulent PSFs. In fields with non-turbulent PSFs, all
interpolators reach a variance on PSF systematics better than
the upper bound expected by future space-based surveys, with
the local interpolators performing better than the global ones
New vertex reconstruction algorithms for CMS
The reconstruction of interaction vertices can be decomposed into a pattern
recognition problem (``vertex finding'') and a statistical problem (``vertex
fitting''). We briefly review classical methods. We introduce novel approaches
and motivate them in the framework of high-luminosity experiments like at the
LHC. We then show comparisons with the classical methods in relevant physics
channelsComment: Talk from the 2003 Computing in High Energy and Nuclear Physics
(CHEP03), La Jolla, Ca, USA, March 2003, 5 pages, LaTeX, 3 eps figures. PSN
TULT01
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