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Titlebook: LATIN 2004: Theoretical Informatics; 6th Latin American S Martín Farach-Colton Conference proceedings 2004 Springer-Verlag Berlin Heidelber

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樓主: gingerly
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發(fā)表于 2025-3-25 04:14:18 | 只看該作者
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發(fā)表于 2025-3-25 10:07:08 | 只看該作者
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發(fā)表于 2025-3-25 12:05:09 | 只看該作者
Collective Tree Exploration that, in order to get overhead sublinear in the number of robots, some communication is necessary. Indeed, we prove that if robots cannot communicate at all, then every distributed exploration algorithm works in time Ω (.) larger than optimal exploration time with full knowledge, for some trees.
24#
發(fā)表于 2025-3-25 18:02:46 | 只看該作者
Conference proceedings 2004is was 80% more than the previous record for the number of submissions. We feel lucky to have been able to build on the solid foundation provided by the increasingly successful previous LATINs. And we are very grateful for the tireless work of Pablo Mart′ ?nez L′ opez, the Local Arrangements Chair.
25#
發(fā)表于 2025-3-25 21:11:20 | 只看該作者
0302-9743 vided by the increasingly successful previous LATINs. And we are very grateful for the tireless work of Pablo Mart′ ?nez L′ opez, the Local Arrangements Chair. 978-3-540-21258-4978-3-540-24698-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
26#
發(fā)表于 2025-3-26 00:18:57 | 只看該作者
Yoshiharu Kohayakawacurrent climate of rising debt and negative savings, the Handbook is timely and instructive reading for educators, researchers, and policymakers who wish to develop or evaluate financial education programs, des978-1-4419-2604-3978-0-387-75734-6
27#
發(fā)表于 2025-3-26 06:59:20 | 只看該作者
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發(fā)表于 2025-3-26 09:38:36 | 只看該作者
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發(fā)表于 2025-3-26 16:03:52 | 只看該作者
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發(fā)表于 2025-3-26 17:19:42 | 只看該作者
Sublinear Methods for Detecting Periodic Trends in Data Streamsvering representative trends in data streams in the form of periodicities. Our algorithms involve sampling ?. positions. and thus they scan not the entire data stream but merely a sublinear sample thereof. Alternately, our algorithms may be thought of as working on streaming inputs where each data i
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