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Titlebook: Structural Information and Communication Complexity; 22nd International C Christian Scheideler Conference proceedings 2015 Springer Interna

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樓主: clannish
51#
發(fā)表于 2025-3-30 08:15:15 | 只看該作者
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發(fā)表于 2025-3-30 12:53:37 | 只看該作者
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發(fā)表于 2025-3-30 17:22:15 | 只看該作者
Annalisa D’Andrea,Mattia D’Emidio,Daniele Frigioni,Stefano Leucci,Guido Proiettiwriters such as D. H. Lawrence. In this area of phanopoeiac "focus," the sketching of particulars by which a motif is recognizably significant, David Cope has made, by the beginning of his third decade, the largest body of such work that I know of among poets of his own generation. Allen Ginsberg Ta
54#
發(fā)表于 2025-3-31 00:37:32 | 只看該作者
Davide Bilò,Feliciano Colella,Luciano Gualà,Stefano Leucci,Guido Proietti romantic writers such as D. H. Lawrence. In this area of phanopoeiac "focus," the sketching of particulars by which a motif is recognizably significant, David Cope has made, by the beginning of his third decade, the largest body of such work that I know of among poets of his own generation. Allen Ginsberg Ta978-0-89603-049-7978-1-4612-5631-1
55#
發(fā)表于 2025-3-31 01:14:57 | 只看該作者
Marc Bury romantic writers such as D. H. Lawrence. In this area of phanopoeiac "focus," the sketching of particulars by which a motif is recognizably significant, David Cope has made, by the beginning of his third decade, the largest body of such work that I know of among poets of his own generation. Allen Ginsberg Ta978-0-89603-049-7978-1-4612-5631-1
56#
發(fā)表于 2025-3-31 08:25:19 | 只看該作者
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發(fā)表于 2025-3-31 09:48:52 | 只看該作者
58#
發(fā)表于 2025-3-31 13:48:40 | 只看該作者
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發(fā)表于 2025-3-31 18:27:01 | 只看該作者
Online Admission Control and Embedding of Service Chains, and randomized online algorithms. We also explore lower bounds for offline approximation algorithms, and prove that the offline problem is APX-hard for unit capacities and small???≥?3, and even Poly-APX-hard in general, when there is no bound on??. These approximation lower bounds may be of indepen
60#
發(fā)表于 2025-4-1 00:04:17 | 只看該作者
Optimizing Spread of Influence in Social Networks via Partial Incentives,of the networks, and we quantify how this affects (i.e., reduces) the total costs of starting an influence diffusion process that influence the whole network. For the two above problems we provide both hardness results and algorithms. We also experimentally validate our algorithms via extensive simu
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