205,978 research outputs found
Random template banks and relaxed lattice coverings
Template-based searches for gravitational waves are often limited by the
computational cost associated with searching large parameter spaces. The study
of efficient template banks, in the sense of using the smallest number of
templates, is therefore of great practical interest. The "traditional" approach
to template-bank construction requires every point in parameter space to be
covered by at least one template, which rapidly becomes inefficient at higher
dimensions. Here we study an alternative approach, where any point in parameter
space is covered only with a given probability < 1. We find that by giving up
complete coverage in this way, large reductions in the number of templates are
possible, especially at higher dimensions. The prime examples studied here are
"random template banks", in which templates are placed randomly with uniform
probability over the parameter space. In addition to its obvious simplicity,
this method turns out to be surprisingly efficient. We analyze the statistical
properties of such random template banks, and compare their efficiency to
traditional lattice coverings. We further study "relaxed" lattice coverings
(using Zn and An* lattices), which similarly cover any signal location only
with probability < 1. The relaxed An* lattice is found to yield the most
efficient template banks at low dimensions (n < 10), while random template
banks increasingly outperform any other method at higher dimensions.Comment: 13 pages, 10 figures, submitted to PR
Certainty Closure: Reliable Constraint Reasoning with Incomplete or Erroneous Data
Constraint Programming (CP) has proved an effective paradigm to model and
solve difficult combinatorial satisfaction and optimisation problems from
disparate domains. Many such problems arising from the commercial world are
permeated by data uncertainty. Existing CP approaches that accommodate
uncertainty are less suited to uncertainty arising due to incomplete and
erroneous data, because they do not build reliable models and solutions
guaranteed to address the user's genuine problem as she perceives it. Other
fields such as reliable computation offer combinations of models and associated
methods to handle these types of uncertain data, but lack an expressive
framework characterising the resolution methodology independently of the model.
We present a unifying framework that extends the CP formalism in both model
and solutions, to tackle ill-defined combinatorial problems with incomplete or
erroneous data. The certainty closure framework brings together modelling and
solving methodologies from different fields into the CP paradigm to provide
reliable and efficient approches for uncertain constraint problems. We
demonstrate the applicability of the framework on a case study in network
diagnosis. We define resolution forms that give generic templates, and their
associated operational semantics, to derive practical solution methods for
reliable solutions.Comment: Revised versio
Learning from networked examples
Many machine learning algorithms are based on the assumption that training
examples are drawn independently. However, this assumption does not hold
anymore when learning from a networked sample because two or more training
examples may share some common objects, and hence share the features of these
shared objects. We show that the classic approach of ignoring this problem
potentially can have a harmful effect on the accuracy of statistics, and then
consider alternatives. One of these is to only use independent examples,
discarding other information. However, this is clearly suboptimal. We analyze
sample error bounds in this networked setting, providing significantly improved
results. An important component of our approach is formed by efficient sample
weighting schemes, which leads to novel concentration inequalities
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