187,089 research outputs found
Taurasi, un nuovo aspetto dell’Eneolitico in Campania
No full texta cura di P. Talamo, Ministero per i Beni e le Attività Culturali – Comune di Taurasi, Salern
Eforo di Cuma nella storia della storiografia (Atti dell'Incontro Internazionale di Studi, Fisciano-Salerno, 10-12 dicembre 2008, volume primo, a cura di P. de Fidio e C. Talamo con la collaborazione di L. Vecchio
No full textEforo di Cuma nella storia della storiografia (Atti dell'Incontro Internazionale di Studi, Fisciano-Salerno, 10-12 dicembre 2008, volume secondo, a cura di P. de Fidio e C. Talamo con la collaborazione di L. Vecchio
No full textTalamo (Clara). La Lidia arcaica
No full textDuhoux Yves. Talamo (Clara). La Lidia arcaica. In: Revue belge de philologie et d'histoire, tome 60, fasc. 1, 1982. Antiquité — Oudheid. p. 217
Probabilistic model for interactive decision-making
No full textA probabilistic reasoning model is defined where the decision maker (d.m.) is engaged in a sequential information-gathering process facing the trade-off between the reliability of the achieved solution and the associated observation cost. The d.m. is directly involved in the proposed flexible control strategy, which is based on information-theoretic principles. The devised strategy works on a Bayesian belief network that allows the efficient representation and manipulation of the knowledge base relevant to the problem domain. It is shown that this strategy guarantees a constant factor approximate solution with respect to the optimum of the decision problem. Some application examples are also discussed. (C) 1999 Elsevier Science B.V. All rights reserved
A data structure for lattice representation
No full textAbstractIn this paper, we present an implicit data structure for the representation of a partial lattice L = (s, N), which allows to test the partial order relation among two given elements in constant time. The data structure proposed has an overall O(n√n)-space complexity, where n is the size of ground set N, which we will prove to be optimal in the worst case. Hence, we derive an overall O(n√n) -space∗time bound for the relation testing problem thus beating the O(n2) bottle-neck representing the present complexity.The overall pre-processing time is O(n2)
Efficient data structure for lattice operations
No full textIn this paper, we consider the representation and management of an element set on which a lattice partial order relation is defined. In particular, let n be the element set size. We present an O(n root n)-space implicit data structure for performing the following set of basic operations: 1. Test the presence of an order relation between two given elements, in constant time. 2. Find a path between two elements whenever one exists, in O(l) steps, where l is the path length. 3. Compute the successors and/or predecessors set of a given element, in O(h) steps, where h is the size of the returned set. 4. Given two elements, find all elements between them, in time O(k log d), where k is the size of the returned set and d is the maximum in-degree or out-degree in the transitive reduction of the order relation. 5. Given two elements, find the least common ancestor and/or the greatest common successor in O(root n)-time. 6. Given k elements, find the least common ancestor and/or the greatest common successor in O(root n + k log n)time. (Unless stated otherwise, all logarithms are to the base 2.) The preprocessing time is O(n(2)). Focusing on the first operation, representing the building-box for all the others, we derive an overall O(n root n)-space x time bound which beats the order n(2) bottleneck representing the present complexity for this problem. Moreover, we will show that the complexity bounds for the first three operations are optimal with respect to the worst case. Additionally, a stronger result can be derived. In particular, it is possible to represent a lattice in space O(n root t), where t is the minimum number of disjoint chains which partition the element set
Representing graphs implicitly using almost optimal space
No full textHow to represent a graph in memory is a fundamental data-structuring problem. In the usual representations, a graph is stored by representing explicitly all vertices and all edges. The names (labels) assigned to vertices are used only to encode the edges and reveal nothing about the structure of the graph itself and hence are a "waste" of space. In this context, we present a general framework for labeling any graph so that adjacency between any two given vertices can be tested in constant time. The labeling scheme assigns to each vertex x a O(delta (x) log(2) n) bit label, where n is the number of vertices and delta (x) is x's degree. The adjacency test can be performed in seven steps and the scheme can be computed in polynomial time. The proposed graph encoding positively demonstrates its superiority over the usual representations, i.e. adjacency matrix and adjacency list representations, which require O(n log n) bit label per vertex and constant time adjacency test, and O(delta (x)log n) bit label per vertex and O(log delta (x)) steps to test adjacency, respectively. Additionally, the labeling scheme is implicit, that is: no pointers are used. (C) 2001 Elsevier Science B.V. All rights reserved
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