The string Barcoding (SBC) problem, introduced by Rash and Gusfield (RECOMB, 2002), consists in finding a minimum set of substrings that can be used to distinguish between all members of a set of given strings. In a c...
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The string Barcoding (SBC) problem, introduced by Rash and Gusfield (RECOMB, 2002), consists in finding a minimum set of substrings that can be used to distinguish between all members of a set of given strings. In a computational biology context, the given strings represent a set of known viruses, while the substrings can be used as probes for an hybridization experiment via microarray. Eventually, one aims at the classification of new strings ( unknown viruses) through the result of the hybridization experiment. In this paper we show that SBC is as hard to approximate as Set Cover. Furthermore, we show that the constrained version of SBC ( with probes of bounded length) is also hard to approximate. These negative results are tight.
Learning Classifier Systems (LCS) traditionally use a binary string rule representation with wildcards added to allow for generalizations over the problem encoding. We have presented a neural network-based representat...
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ISBN:
(纸本)0780393635
Learning Classifier Systems (LCS) traditionally use a binary string rule representation with wildcards added to allow for generalizations over the problem encoding. We have presented a neural network-based representation to aid their use in complex problem domains. Here each rule's condition and action are represented by a small neural network, evolved through the actions of the genetic algorithm. In this paper we present results from the use of backpropagation to provide local search in conjunction with the global search of the genetic algorithm within XCS creating a Memetic neural LCS. Significant decreases in the time taken to reach optimal behaviour are obtained from the incorporation of this local learning algorithm.
A random intrinsic chip ID method generates a pair of 4Kb binary strings using retention fails in 32nm SOI embedded DRAM. Hardware results show ID overlap distance mean=0.58 and σ=0.76 and demonstrate 100% authentica...
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Background: The center string (or closest string) problem is a classic computer science problem with important applications in computational biology. Given k input strings and a distance threshold d, we search for a s...
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Background: The center string (or closest string) problem is a classic computer science problem with important applications in computational biology. Given k input strings and a distance threshold d, we search for a string within Hamming distance at most d to each input string. This problem is NP complete. Results: In this paper, we focus on exact methods for the problem that are also swift in application. We first introduce data reduction techniques that allow us to infer that certain instances have no solution, or that a center string must satisfy certain conditions. We describe how to use this information to speed up two previously published search tree algorithms. Then, we describe a novel iterative search strategy that is effecient in practice, where some of our reduction techniques can also be applied. Finally, we present results of an evaluation study for two different data sets from a biological application. Conclusions: We find that the running time for computing the optimal center string is dominated by the subroutine calls for d = d(opt) - 1 and d = d(opt). Our data reduction is very effective for both, either rejecting unsolvable instances or solving trivial positions. We find that this speeds up computations considerably.
We study the restless bandit associated with an extremely simple scalar Kalman filter model in discrete time. Under certain assumptions, we prove that the problem is indexable in the sense that the Whittle index is a ...
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ISBN:
(纸本)9781510825024
We study the restless bandit associated with an extremely simple scalar Kalman filter model in discrete time. Under certain assumptions, we prove that the problem is indexable in the sense that the Whittle index is a non-decreasing function of the relevant belief state. In spite of the long history of this problem, this appears to be the first such proof. We use results about Schur-convexity and mechanical words, which are particular binary strings intimately related to palindromes.
binary descriptors have recently emerged as low-complexity alternatives to state-of-the-art descriptors such as SIFT. The descriptor is represented by means of a binary string, in which each bit is the result of the p...
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ISBN:
(纸本)9781479923427
binary descriptors have recently emerged as low-complexity alternatives to state-of-the-art descriptors such as SIFT. The descriptor is represented by means of a binary string, in which each bit is the result of the pair-wise comparison of smoothed pixel values properly selected in a patch around each keypoint. Previous works have focused on the construction of the descriptor neglecting the opportunity of performing lossless compression. In this paper, we propose two contributions. First, design an entropy coding scheme that seeks the internal ordering of the descriptor that minimizes the number of bits necessary to represent it. Second, we compare different selection strategies that can be adopted to identify which pair-wise comparisons to use when building the descriptor. Unlike previous works, we evaluate the discriminative power of descriptors as a function of rate, in order to investigate the trade-offs in a bandwidth constrained scenario.
We consider NCA labeling schemes: given a rooted tree T, label the nodes of T with binary strings such that, given the labels of any two nodes, one can determine, by looking only at the labels, the label of their near...
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ISBN:
(纸本)9781611973389
We consider NCA labeling schemes: given a rooted tree T, label the nodes of T with binary strings such that, given the labels of any two nodes, one can determine, by looking only at the labels, the label of their nearest common ancestor. For trees with n nodes we present upper and lower bounds establishing that labels of size (2±ε) log n, ε< 1 are both sufficient and necessary. Alstrup, Bille, and Rauhe (SIDMA'05) showed thatancestor and NCA labeling schemes have labels of size log n + Ω(log log n). Our lower bound increases this to log n+Ω(log n) for NCA labeling schemes. Since Fraigniaud and Korman (STOC'10) established that labels in ancestor labeling schemes have size log n+Θ(log log n), our new lower bound separates ancestor and NCA labeling schemes. Our upper bound improves the 10 log n upper bound by Alstrup, Gavoille, Kaplan and Rauhe (TOCS'04), and our theoretical result even outperforms some recent experimental studies by Fischer (ESA'09) where variants of the same NCA labeling scheme are shown to all have labels of size approximately8 log n.
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