17,551 research outputs found
Cuntz-Krieger algebras and wavelets on fractals
We consider representations of Cuntz--Krieger algebras on the Hilbert space
of square integrable functions on the limit set, identified with a Cantor set
in the unit interval. We use these representations and the associated
Perron-Frobenius and Ruelle operators to construct families of wavelets on
these Cantor sets.Comment: 37 pages, LaTe
CuntzâKrieger Algebras and Wavelets on Fractals
We consider representations of CuntzâKrieger algebras on the Hilbert space of square integrable functions on the limit set, identified with a Cantor set in the unit interval. We use these representations and the associated PerronâFrobenius and Ruelle operators to construct families of wavelets on these Cantor sets
A High-Order Scheme for Image Segmentation via a modified Level-Set method
In this paper we propose a high-order accurate scheme for image segmentation
based on the level-set method. In this approach, the curve evolution is
described as the 0-level set of a representation function but we modify the
velocity that drives the curve to the boundary of the object in order to obtain
a new velocity with additional properties that are extremely useful to develop
a more stable high-order approximation with a small additional cost. The
approximation scheme proposed here is the first 2D version of an adaptive
"filtered" scheme recently introduced and analyzed by the authors in 1D. This
approach is interesting since the implementation of the filtered scheme is
rather efficient and easy. The scheme combines two building blocks (a monotone
scheme and a high-order scheme) via a filter function and smoothness indicators
that allow to detect the regularity of the approximate solution adapting the
scheme in an automatic way. Some numerical tests on synthetic and real images
confirm the accuracy of the proposed method and the advantages given by the new
velocity.Comment: Accepted version for publication in SIAM Journal on Imaging Sciences,
86 figure
Partner selection supports reputation-based cooperation in a Public Goods Game
In dyadic models of indirect reciprocity, the receivers' history of giving
has a significant impact on the donor's decision. When the interaction involves
more than two agents things become more complicated, and in large groups
cooperation can hardly emerge. In this work we use a Public Goods Game to
investigate whether publicly available reputation scores may support the
evolution of cooperation and whether this is affected by the kind of network
structure adopted. Moreover, if agents interact on a bipartite graph with
partner selection cooperation can thrive in large groups and in a small amount
of time.Comment: 6 pages, 10 figures. In press for Springer E
Introduction to the Special Section on Reputation in Agent Societies
This special section includes papers from the 'Reputation in Agent Societies' workshop held as part of 2004 IEEE/WIC/ACM International Joint Conference on Intelligent Agent Technology (IAT'04) and Web Intelligence (WI'04), September 20, 2004 in Beijing, China. The purpose of this workshop was to promote multidisciplinary collaboration for Reputation Systems modeling and implementation. Reputation is increasingly at the centre of attention in many fields of science and domains of application, including economics, organisations science, policy-making, (e-)governance, cultural evolution, social dilemmas, socio-dynamics, innofusion, etc. However, the result of all this attention is a great number of ad hoc models and little integration of instruments for the implementation, management and optimisation of reputation. On the one hand, entrepreneurs and administrators manage corporate and firm reputation without contributing to or accessing a solid, general and integrated body of scientific knowledge on the subject matter. On the other hand, software designers believe they can design and implement online reputation reporting systems without investigating what the properties, requirements and dynamics of reputation in natural societies are and why it evolved. We promoted the workshop and this special section with the hope of setting the first steps in the direction of a new, cross-disciplinary approach to reputation, accounting for the social cognitive mechanisms and processes that support it and working towards t a consensus on essential guidelines for designing or shaping reputation technologies.Reputation, Agent Systems
Flaw Selection Strategies for Partial-Order Planning
Several recent studies have compared the relative efficiency of alternative
flaw selection strategies for partial-order causal link (POCL) planning. We
review this literature, and present new experimental results that generalize
the earlier work and explain some of the discrepancies in it. In particular, we
describe the Least-Cost Flaw Repair (LCFR) strategy developed and analyzed by
Joslin and Pollack (1994), and compare it with other strategies, including
Gerevini and Schubert's (1996) ZLIFO strategy. LCFR and ZLIFO make very
different, and apparently conflicting claims about the most effective way to
reduce search-space size in POCL planning. We resolve this conflict, arguing
that much of the benefit that Gerevini and Schubert ascribe to the LIFO
component of their ZLIFO strategy is better attributed to other causes. We show
that for many problems, a strategy that combines least-cost flaw selection with
the delay of separable threats will be effective in reducing search-space size,
and will do so without excessive computational overhead. Although such a
strategy thus provides a good default, we also show that certain domain
characteristics may reduce its effectiveness.Comment: See http://www.jair.org/ for an online appendix and other files
accompanying this articl
Learning Features that Predict Cue Usage
Our goal is to identify the features that predict the occurrence and
placement of discourse cues in tutorial explanations in order to aid in the
automatic generation of explanations. Previous attempts to devise rules for
text generation were based on intuition or small numbers of constructed
examples. We apply a machine learning program, C4.5, to induce decision trees
for cue occurrence and placement from a corpus of data coded for a variety of
features previously thought to affect cue usage. Our experiments enable us to
identify the features with most predictive power, and show that machine
learning can be used to induce decision trees useful for text generation.Comment: 10 pages, 2 Postscript figures, uses aclap.sty, psfig.te
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