direction of the term structure of interest rates. As a consequence, immunization against all possible shift directions leads necessarily to an exact matching strategy. Mathematics Subject Classification: 62P05, 91B82, 91G30

In the exact matching problem we are given a graph G, some of whose edges are coloredr ed, and a positive integer k. The goal is to determine if G has a perfect matching, exactly k edges of which are red. More generally if the matching number of G is m = m(G), the goal is to find a matching with m

We believe there is a niche for exact sub-image searching in image databases. When only a sub-image is searched for, traditional image searching approaches fail because the sub-image features usually represent only a small fraction of the global image features. Said differently: it is difficult

Abstract—In this paper, we consider the problem of determining whether sequences X and Y, generated i.i.d. according to PX × PY, are equal given access only to the pair (Y,T(X)), where T(X) is a rate-R compressed version of X. In general, the rate R may not be sufficiently large to reliably determine whether X = Y. We precisely characterize this reliability – i.e., the exponential rate at which an error is made – as a function of R. Interestingly, the exponent turns out to be related to the Bhattacharyya distance between the distributions PX and PY. In addition, the scheme achieving this exponent is universal, i.e. does not depend on PX,PY. I.

applications than existing matching methods. In CEM, users temporarily coarsen their data, exact match on these coarsened data, then run their analysis on the uncoarsened, matched data. CEM bounds the degree of model dependence and causal effect estimation error by ex ante user choice, is montonic imbalance

in them, which is one of the crucial steps in many bioinformatics pipelines, is a time consuming task. Maximal exact matches have been considered important to detect and evaluate the similarity. Most of the existing tools that are designed and developed to find the maximal matches are based on advanced

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of the reconstructed result. To improve the pointcloud obtained from other algorithms, and break the limit laid by the regular matching, we propose our refinement method using exact matching. The exact matching enables more accurate matching windows for the points, by taking the normal vector into consideration

exact matching: topics exact matching search pattern P in text T (P,T strings)

molecules. Then exact matching with the query molecule is performed through fine filtering on the remaining molecules in order to find the exact match for the query molecule from the pruned database using more expensive graph isomorphism algorithm. In this way search time can be reduced significantly. I.