Bisimulation is an important notion for the verification of distributed systems. Timed bisimulation is its natural extension to real time systems. Timed bisimulation is known to be decidable for timed automata using t...
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ISBN:
(纸本)3540626166
Bisimulation is an important notion for the verification of distributed systems. Timed bisimulation is its natural extension to real time systems. Timed bisimulation is known to be decidable for timed automata using the so-called region technique. We present a new, top down approach to timed bisimulation which applies the zone technique from the theory of hybrid systems. In contrast to the original decision algorithm, our method has a better space complexity and is scaling invariant: altering the time scale does not effect the space complexity.
In this extended abstract, we outline a concurrent implementation of a data structure called skip graphs. This data structure, proposed by Aspnes and Shah, has similar functionality to binary search trees, and has als...
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We present O(n log m) algorithms for a new class of problems termed self-normalised distance with don't cares. The input is a pattern p of length m and text t of length n > m. The elements of these strings are ...
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ISBN:
(纸本)9783540734369
We present O(n log m) algorithms for a new class of problems termed self-normalised distance with don't cares. The input is a pattern p of length m and text t of length n > m. The elements of these strings are either integers or wild card symbols. In the shift version, the problem is to compute min(alpha) Sigma(m-1)(j-0)(alpha + P-j - t(i+j))(2) for all i, where wild cards do not contribute to the sum. In the shift-scale version, the objective is to compute min(alpha,beta) Sigma(m-1)(j=0)(alpha + beta p(j) - t(i+j))(2) for all i, similarly. We show that the algorithms have the additional benefit of providing simple O(n log m) solutions for the problems of exact matching with don't cares, exact shift matching with don't cares and exact shift-scale matching with don't cares.
Perhaps the applied nature of information retrieval research goes some way to explain the community's rich history of evaluating machine learning models holistically, understanding that efficacy matters but so doe...
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ISBN:
(纸本)9781450387323
Perhaps the applied nature of information retrieval research goes some way to explain the community's rich history of evaluating machine learning models holistically, understanding that efficacy matters but so does the computational cost incurred to achieve it. This is evidenced, for example, by more than a decade of research on efficient training and inference of large decision forest models in learning-to-rank. As the community adopts even more complex, neural network-based models in a wide range of applications, questions on efficiency have once again become relevant. We propose this workshop as a forum for a critical discussion of efficiency in the era of neural information retrieval, to encourage debate on the current state and future directions of research in this space, and to promote more sustainable research by identifying best practices in the development and evaluation of neural models for information retrieval.
We analyze the complexity of graph reachability queries on ST-graphs, defined as directed acyclic graphs (DAGs) obtained by merging the suffix tree of a given string and its suffix links. Using a simplified reachabili...
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ISBN:
(纸本)8001035336
We analyze the complexity of graph reachability queries on ST-graphs, defined as directed acyclic graphs (DAGs) obtained by merging the suffix tree of a given string and its suffix links. Using a simplified reachability labeling algorithm presented by Agrawal et al. (1989), we show that for a random string of length n, its ST-graph can be preprocessed in O(n log n) expected time and space to answer reachability queries in O(log n) time. Furthermore, we present a series of strings that require circle minus(n root n) time and space to answer reachability queries in O(log n) time for the same algorithm. Exhaustive computational calculations for strings of length n <= 33 have revealed that the same strings are also the worst case instances of the algorithm. We therefore conjecture that reachability queries can be answered in O(log n) time with a worst case time and space preprocessing complexity of circle minus(n root n).
We investigate the testability properties of Boolean circuits derived from (Reduced Ordered) Binary Decision Diagrams. It is shown that BDD-cirucits (or at least) BDD-like circuits are easily testable with respect to ...
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Reconstructing 3D objects by gathering information from multiple spatial viewpoints is a fundamental problem in a variety of applications ranging from heritage reconstruction to industrial image processing. A central ...
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ISBN:
(纸本)9789897582257
Reconstructing 3D objects by gathering information from multiple spatial viewpoints is a fundamental problem in a variety of applications ranging from heritage reconstruction to industrial image processing. A central issue is known as the "point set registration or matching" problem. where the two sets being considered are to be rigidly aligned. This is a complex problem with a huge search space that suffers from high computational costs or requires expensive and bulky hardware to be added to the scanning system. To address these issues, a hybrid hardware-software approach was presented in (Pribanic et al., 2016) allowing for fast software registration by using commonly available (smartphone) sensors. In this paper we present hierarchical techniques to improve the performance of this algorithm. Additionally, we compare the performance of our algorithm against other approaches. Experimental results using real data show how the algorithm presented greatly improves the time of the previous algorithm and perform best over all studied algorithms.
For a given ordered graph (G, <), we consider the smallest (strongly) chordal graph G' containing G with < as a (strongly) perfect elimination ordering. We call (G, <) a compact representation of G'. ...
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ISBN:
(纸本)3540626166
For a given ordered graph (G, <), we consider the smallest (strongly) chordal graph G' containing G with < as a (strongly) perfect elimination ordering. We call (G, <) a compact representation of G'. We show that the computation of a depth-first search tree and a breadth-first search tree can be done in polylogarithmic time with a linear processor number with respect to the size of the compact representation in parallel. We consider also the problems to find a maximum clique and to develop a data structure extension that allows an adjacency query in polylogarithmic time.
Inferring cluster structure in microarray datasets is a fundamental task for the so-called -omic sciences. It is also a fundamental question in Statistics, data Analysis and Classification, in particular with regard t...
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Inferring cluster structure in microarray datasets is a fundamental task for the so-called -omic sciences. It is also a fundamental question in Statistics, data Analysis and Classification, in particular with regard to the prediction of the number of clusters in a dataset, usually established via internal validation measures. Despite the wealth of internal measures available in the literature, new ones have been recently proposed, some of them specifically for microarray data. In this dissertation, a study of internal validation measures is given, paying particular atten- tion to the stability based ones. Indeed, this class of measures is particularly prominent and promising in order to have a reliable estimate of the correct number of clusters in a dataset. For this kind of measures, a new general algorithmic paradigm is proposed here that highlights the richness of measures in this class and accounts for the ones already available in the literature. Moreover, some of the most representative data-driven validation measures are also considered. Extensive experiments on twelve benchmark microarray datasets are performed, using both Hierarchical and K-means clustering algorithms, in order to assess both the intrinsic ability of a measure to predict the correct number of clusters in a dataset and its merit relative to the other measures. Particular attention is given both to precision and speed. The main result is a hierarchy of internal validation measures in terms of precision and speed, highlighting some of their merits and limitations not reported before in the literature. This hierarchy shows that the faster the measure, the less accurate it is. In order to reduce the time performance gap between the fastest and the most precise measures, the technique of designing fast approxima- tion algorithms is systematically applied. The end result is a speed-up of many of the measures studied here that brings the gap between the fastest and the most precise within one order
We present one more proof of the fact that the class of proper interval graphs is precisely the class of unit interval graphs. The proof leads to a new and efficient O (n) time and space algorithm that transforms a pr...
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