9,523 research outputs found
Two novel evolutionary formulations of the graph coloring problem
We introduce two novel evolutionary formulations of the problem of coloring
the nodes of a graph. The first formulation is based on the relationship that
exists between a graph's chromatic number and its acyclic orientations. It
views such orientations as individuals and evolves them with the aid of
evolutionary operators that are very heavily based on the structure of the
graph and its acyclic orientations. The second formulation, unlike the first
one, does not tackle one graph at a time, but rather aims at evolving a
`program' to color all graphs belonging to a class whose members all have the
same number of nodes and other common attributes. The heuristics that result
from these formulations have been tested on some of the Second DIMACS
Implementation Challenge benchmark graphs, and have been found to be
competitive when compared to the several other heuristics that have also been
tested on those graphs.Comment: To appear in Journal of Combinatorial Optimizatio
Towards an Intelligent Workflow Designer based on the Reuse of Workflow Patterns
In order to perform process-aware information systems we need sophisticated methods and concepts for designing and modeling processes. Recently, research on workflow patterns has emerged in order to increase the reuse of recurring workflow structures. However, current workflow modeling tools do not provide functionalities that enable users to define, query, and reuse workflow patterns properly. In this paper we gather a suite for both process modeling and normalization based on workflow patterns reuse. This suite must be used in the extension of some workflow design tool. The suite comprises components for the design of processes
from both legacy systems and process modeling
Some Extended Classes of Distributions: Characterizations and Properties
Based on a simple relationship between two truncated moments and certain functions of the th order statistic, we characterize some extended classes of distributions recently proposed in the statistical literature, videlicet Beta-G, Gamma-G, Kumaraswamy-G and McDonald-G. Several properties of these extended classes and some special cases are discussed. We compare these classes in terms of goodness-of-fit criteria using some baseline distributions by means of two real data sets
Time-Reversal Symmetry Breaking and Decoherence in Chaotic Dirac Billiards
In this work, we perform a statistical study on Dirac Billiards in the
extreme quantum limit (a single open channel on the leads). Our numerical
analysis uses a large ensemble of random matrices and demonstrates the
preponderant role of dephasing mechanisms in such chaotic billiards. Physical
implementations of these billiards range from quantum dots of graphene to
topological insulators structures. We show, in particular, that the role of
finite crossover fields between the universal symmetries quickly leaves the
conductance to the asymptotic limit of unitary ensembles. Furthermore, we show
that the dephasing mechanisms strikingly lead Dirac billiards from the extreme
quantum regime to the semiclassical Gaussian regime
Importância da avaliação microbiológica na qualidade e segurança dos alimentos.
Abordagem técnológica. Abordagem metodológica. Relevância econômica, social e ambiental. Referências bibliográficasbitstream/CNPAB-2010/27380/1/doc120.pd
Top-down segmentation of non-rigid visual objects using derivative-based search on sparse manifolds
The solution for the top-down segmentation of non rigid visual objects using machine learning techniques is generally regarded as too complex to be solved in its full generality given the large dimensionality of the search space of the explicit representation of the segmentation contour. In order to reduce this complexity, the problem is usually divided into two stages: rigid detection and non-rigid segmentation. The rationale is based on the fact that the rigid detection can be run in a lower dimensionality space (i.e., less complex and faster) than the original contour space, and its result is then used to constrain the non-rigid segmentation. In this paper, we propose the use of sparse manifolds to reduce the dimensionality of the rigid detection search space of current state-of-the-art top-down segmentation methodologies. The main goals targeted by this smaller dimensionality search space are the decrease of the search running time complexity and the reduction of the training complexity of the rigid detector. These goals are attainable given that both the search and training complexities are function of the dimensionality of the rigid search space. We test our approach in the segmentation of the left ventricle from ultrasound images and lips from frontal face images. Compared to the performance of state-of-the-art non-rigid segmentation system, our experiments show that the use of sparse manifolds for the rigid detection leads to the two goals mentioned above. © 2013 IEEE.Jacinto C. Nascimento, Gustavo Carneirohttp://www.pamitc.org/cvpr13
Efficient Computation of Multiple Density-Based Clustering Hierarchies
HDBSCAN*, a state-of-the-art density-based hierarchical clustering method,
produces a hierarchical organization of clusters in a dataset w.r.t. a
parameter mpts. While the performance of HDBSCAN* is robust w.r.t. mpts in the
sense that a small change in mpts typically leads to only a small or no change
in the clustering structure, choosing a "good" mpts value can be challenging:
depending on the data distribution, a high or low value for mpts may be more
appropriate, and certain data clusters may reveal themselves at different
values of mpts. To explore results for a range of mpts values, however, one has
to run HDBSCAN* for each value in the range independently, which is
computationally inefficient. In this paper, we propose an efficient approach to
compute all HDBSCAN* hierarchies for a range of mpts values by replacing the
graph used by HDBSCAN* with a much smaller graph that is guaranteed to contain
the required information. An extensive experimental evaluation shows that with
our approach one can obtain over one hundred hierarchies for the computational
cost equivalent to running HDBSCAN* about 2 times.Comment: A short version of this paper appears at IEEE ICDM 2017. Corrected
typos. Revised abstrac
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