78,573 research outputs found
On applying the set covering model to reseeding
The Functional BIST approach is a rather new BIST technique based on exploiting embedded system functionality to generate deterministic test patterns during BIST. The approach takes advantages of two well-known testing techniques, the arithmetic BIST approach and the reseeding method. The main contribution of the present paper consists in formulating the problem of an optimal reseeding computation as an instance of the set covering problem. The proposed approach guarantees high flexibility, is applicable to different functional modules, and, in general, provides a more efficient test set encoding then previous techniques. In addition, the approach shorts the computation time and allows to better exploiting the tradeoff between area overhead and global test length as well as to deal with larger circuits
Optimal Substring-Equality Queries with Applications to Sparse Text Indexing
We consider the problem of encoding a string of length from an integer
alphabet of size so that access and substring equality queries (that
is, determining the equality of any two substrings) can be answered
efficiently. Any uniquely-decodable encoding supporting access must take
bits. We describe a new data
structure matching this lower bound when while supporting
both queries in optimal time. Furthermore, we show that the string can
be overwritten in-place with this structure. The redundancy of
bits and the constant query time break exponentially a lower bound that is
known to hold in the read-only model. Using our new string representation, we
obtain the first in-place subquadratic (indeed, even sublinear in some cases)
algorithms for several string-processing problems in the restore model: the
input string is rewritable and must be restored before the computation
terminates. In particular, we describe the first in-place subquadratic Monte
Carlo solutions to the sparse suffix sorting, sparse LCP array construction,
and suffix selection problems. With the sole exception of suffix selection, our
algorithms are also the first running in sublinear time for small enough sets
of input suffixes. Combining these solutions, we obtain the first
sublinear-time Monte Carlo algorithm for building the sparse suffix tree in
compact space. We also show how to derandomize our algorithms using small
space. This leads to the first Las Vegas in-place algorithm computing the full
LCP array in time and to the first Las Vegas in-place algorithms
solving the sparse suffix sorting and sparse LCP array construction problems in
time. Running times of these Las Vegas
algorithms hold in the worst case with high probability.Comment: Refactored according to TALG's reviews. New w.h.p. bounds and Las
Vegas algorithm
Construct, Merge, Solve and Adapt: Application to the repetition-free longest common subsequence problem
In this paper we present the application of a recently proposed, general, algorithm for combinatorial optimization to the repetition-free longest common subsequence problem. The applied algorithm, which is labelled Construct, Merge, Solve & Adapt, generates sub-instances based on merging the solution components found in randomly constructed solutions. These sub-instances are subsequently solved by means of an exact solver. Moreover, the considered sub-instances are dynamically changing due to adding new solution components at each iteration, and removing existing solution components on the basis of indicators about their usefulness. The results of applying this algorithm to the repetition-free longest common subsequence problem show that the algorithm generally outperforms competing approaches from the literature. Moreover, they show that the algorithm is competitive with CPLEX for small and medium size problem instances, whereas it outperforms CPLEX for larger problem instances.Peer ReviewedPostprint (author's final draft
Topological transition in disordered planar matching: combinatorial arcs expansion
In this paper, we investigate analytically the properties of the disordered
Bernoulli model of planar matching. This model is characterized by a
topological phase transition, yielding complete planar matching solutions only
above a critical density threshold. We develop a combinatorial procedure of
arcs expansion that explicitly takes into account the contribution of short
arcs, and allows to obtain an accurate analytical estimation of the critical
value by reducing the global constrained problem to a set of local ones. As an
application to a toy representation of the RNA secondary structures, we suggest
generalized models that incorporate a one-to-one correspondence between the
contact matrix and the RNA-type sequence, thus giving sense to the notion of
effective non-integer alphabets.Comment: 28 pages, 6 figures, published versio
Protein design in a lattice model of hydrophobic and polar amino acids
A general strategy is described for finding which amino acid sequences have
native states in a desired conformation (inverse design). The approach is used
to design sequences of 48 hydrophobic and polar aminoacids on three-dimensional
lattice structures. Previous studies employing a sequence-space Monte-Carlo
technique resulted in the successful design of one sequence in ten attempts.
The present work also entails the exploration of conformations that compete
significantly with the target structure for being its ground state. The design
procedure is successful in all the ten cases.Comment: RevTeX, 12 pages, 1 figur
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Metaheuristic approaches for the quartet method of hierarchical clustering
Given a set of objects and their pairwise distances, we wish to determine a visual representation of the data. We use the quartet paradigm to compute a hierarchy of clusters of the objects. The method is based on an NP-hard graph optimization problem called the Minimum Quartet Tree Cost problem. This paper presents and compares several metaheuristic approaches to approximate the optimal hierarchy. The performance of the algorithms is tested through extensive computational experiments and it is shown that the Reduced Variable Neighbourhood Search metaheuristic is the most effective approach to the problem, obtaining high quality solutions in short computational running times
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