2,219 research outputs found
Efficient enumeration of solutions produced by closure operations
In this paper we address the problem of generating all elements obtained by
the saturation of an initial set by some operations. More precisely, we prove
that we can generate the closure of a boolean relation (a set of boolean
vectors) by polymorphisms with a polynomial delay. Therefore we can compute
with polynomial delay the closure of a family of sets by any set of "set
operations": union, intersection, symmetric difference, subsets, supersets
). To do so, we study the problem: for a set
of operations , decide whether an element belongs to the closure
by of a family of elements. In the boolean case, we prove that
is in P for any set of boolean operations
. When the input vectors are over a domain larger than two
elements, we prove that the generic enumeration method fails, since
is NP-hard for some . We also study the
problem of generating minimal or maximal elements of closures and prove that
some of them are related to well known enumeration problems such as the
enumeration of the circuits of a matroid or the enumeration of maximal
independent sets of a hypergraph. This article improves on previous works of
the same authors.Comment: 30 pages, 1 figure. Long version of the article arXiv:1509.05623 of
the same name which appeared in STACS 2016. Final version for DMTCS journa
DIAGaRa: An Incremental Algorithm for Inferring Implicative Rules from Examples
An approach is proposed for inferring implicative logical rules from examples. The concept of a good
diagnostic test for a given set of positive examples lies in the basis of this approach. The process of inferring
good diagnostic tests is considered as a process of inductive common sense reasoning. The incremental
approach to learning algorithms is implemented in an algorithm DIAGaRa for inferring implicative rules
from examples
A review of associative classification mining
Associative classification mining is a promising approach in data mining that utilizes the
association rule discovery techniques to construct classification systems, also known as
associative classifiers. In the last few years, a number of associative classification algorithms
have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. These algorithms
employ several different rule discovery, rule ranking, rule pruning, rule prediction and rule
evaluation methods. This paper focuses on surveying and comparing the state-of-the-art associative
classification techniques with regards to the above criteria. Finally, future directions in associative
classification, such as incremental learning and mining low-quality data sets, are also
highlighted in this paper
Advances in the numerical treatment of grain-boundary migration: Coupling with mass transport and mechanics
This work is based upon a coupled, lattice-based continuum formulation that
was previously applied to problems involving strong coupling between mechanics
and mass transport; e.g. diffusional creep and electromigration. Here we
discuss an enhancement of this formulation to account for migrating grain
boundaries. The level set method is used to model grain-boundary migration in
an Eulerian framework where a grain boundary is represented as the zero level
set of an evolving higher-dimensional function. This approach can easily be
generalized to model other problems involving migrating interfaces; e.g. void
evolution and free-surface morphology evolution. The level-set equation is
recast in a remarkably simple form which obviates the need for spatial
stabilization techniques. This simplified level-set formulation makes use of
velocity extension and field re-initialization techniques. In addition, a
least-squares smoothing technique is used to compute the local curvature of a
grain boundary directly from the level-set field without resorting to
higher-order interpolation. A notable feature is that the coupling between mass
transport, mechanics and grain-boundary migration is fully accounted for. The
complexities associated with this coupling are highlighted and the
operator-split algorithm used to solve the coupled equations is described.Comment: 28 pages, 9 figures, LaTeX; Accepted for publication in Computer
Methods in Applied Mechanics and Engineering. [Style and formatting
modifications made, references added.
An Approach to Incremental Learning Good Classification Tests
An algorithm of incremental mining implicative logical rules is pro-posed. This algorithm is based on constructing good classification tests. The in-cremental approach to constructing these rules allows revealing the interde-pendence between two fundamental components of human thinking: pattern recognition and knowledge acquisition
Real-time motion planning methods for autonomous on-road driving: state-of-the-art and future research directions
Currently autonomous or self-driving vehicles are at the heart of academia and industry research because of its multi-faceted advantages that includes improved safety, reduced congestion, lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a given origin to a given destination, motion planning methods incorporate searching for a path to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have been proposed in the literature. The purpose of this paper is to review existing approaches and then compare and contrast different methods employed for the motion planning of autonomous on-road driving that consists of (1) finding a path, (2) searching for the safest manoeuvre and (3) determining the most feasible trajectory. Methods developed by researchers in each of these three levels exhibit varying levels of complexity and performance accuracy. This paper presents a critical evaluation of each of these methods, in terms of their advantages/disadvantages, inherent limitations, feasibility, optimality, handling of obstacles and testing operational environments. Based on a critical review of existing methods, research challenges to address current limitations are identified and future research directions are suggested so as to enhance the performance of planning algorithms at all three levels. Some promising areas of future focus have been identified as the use of vehicular communications (V2V and V2I) and the incorporation of transport engineering aspects in order to improve the look-ahead horizon of current sensing technologies that are essential for planning with the aim of reducing the total cost of driverless vehicles. This critical review on planning techniques presented in this paper, along with the associated discussions on their constraints and limitations, seek to assist researchers in accelerating development in the emerging field of autonomous vehicle research
- …