Titlebook: Algorithmic Aspects in Information and Management; 5th International Co Andrew V. Goldberg,Yunhong Zhou Conference proceedings 2009 Springe

tangle

moment of one-dimensional data streams..Prior work [10] presented a nearly space-optimal algorithm for estimating .. for .?∈?[0,2] and .?∈?[0,1]. Here, we complement that work by presenting a nearly space-optimal algorithm for estimating .. for .?∈?[0,1] and .?∈?[0,2].

拋物線

QUAIL

Power Indices in Spanning Connectivity Games,to compute the Banzhaf indices. This answers (positively) an open question of whether computing Shapley-Shubik indices for a simple game represented by the set of minimal winning coalitions is NP-hard.

呼吸

Three Results on Frequency Assignment in Linear Cellular Networks,etitive ratio .. Second, in the general online case, where the requests arrive and depart over time, we improve the current lower bound on the (asymptotic) competitive ratio to .. Third, we prove that the offline version of this problem is .-complete.

啞巴

發(fā)起

5 Miscellaneous Compounds, CnH(2n-6), both shown to be APX-hard previously. We show in this paper that MFR has a polynomial time .(log.)-factor approximation. We also consider Maximum Fragment Identification (MFI), which is the complementary version of MFR; and Maximum SNP Identification (MSI), which is the complementary version of MSR

dialect

臭了生氣

Densities of Aromatic Hydrocarbonsetitive ratio .. Second, in the general online case, where the requests arrive and depart over time, we improve the current lower bound on the (asymptotic) competitive ratio to .. Third, we prove that the offline version of this problem is .-complete.

acetylcholine

Densities of Aromatic Hydrocarbonsefore we give some general methods to construct equiseparable pairs and compare the methods in the case of Wiener index. More specifically, we show that terminal Wiener index is degenerative to some extent.

Arctic

Algorithmic Challenge in Online Advertising,eval, statistical modeling, machine learning, classification, optimization, and microeconomics. The central challenge of computational advertising is to find the “best match” between a given user in a given context and a suitable advertisement. The context could be a user entering a query in a searc