All previously known efficient maximum-flow algorithms work by finding augmenting paths, either one path at a time (as in the original Ford and Fulkerson algorithm) or all shortest-length augmenting paths at once (using the layered network approach of Dinic). An alternative method based

are highly cross-correlated, and principal components analysis implies that they are mostly driven by a single common factor. An important implication of this finding is that if any explanatory variables have been omitted, they are likely not firm-specific. We therefore re-run the regression, but 1 this time

3-10 times better performance in comparison with a typical path-based method, GraphGrep. The gIndex approach not only provides an elegant solution to the graph indexing problem, but also demonstrates how database indexing and query processing can benefit from data mining, especially frequent pattern

and technical concerns, inherent limits of k-space speed have almost been reached. An entirely different approach to sub-wavelength resolution in MRI is based on the fact that with a receiver placed near the object the contribution of a signal source to the induced voltage varies appreciably with its relative

and the document content change over time. A filtering system should be able to adapt to such concept changes. This paper proposes a new method to recognize and handle concept changes with support vector machines. The method maintains a window on the training data. The key idea is to automatically adjust

in the .50 and .60 range may be regarded as problematic by some researchers. We feel that there is no need for concern on this issue, however, because internal consistency that is based on the alpha coefficient may not be an adequate reliability estimate for a scale made up of a small number of items. Indeed

In this paper, we present a new approach for the visualization of time-series data based on spirals. Different to classical bar charts and line graphs, the spiral is suited to visualize large data sets and supports much better the identification of periodic structures in the data. Moreover

this look like?'). Finally, these analogies activate associated representations that translate into predictions Each of these key components -associations, analogies and predictions -has been the focus of rich and active research for a long time. By connecting these concepts in one unifying principle

summary based on a query. In this paper, we investigate a new approach that tackles this problem and propose a new solution using document graphs. This is our first time to participate in Document Understanding Conferences.

, demand robust knowledge bases capable of supporting flexible, nuanced reasoning. Populating such knowledge bases is highly time-consuming, making it necessary to develop techniques for deconstructing natural language texts into commonsense concepts. In this work, we propose an approach for effective