39392055
Maximum genus and chromatic number of graphs
Let T be a spanning tree of a connected graph G. Denote by @x(G,T) the number of components in [email protected]?E(T) with odd number of edges. The value min"[email protected](G,T) is known as the Betti deficiency of G, denoted by @x(G), where the minimum is taken over all spanning trees T of G. It is known (N.H. Xuong, J. Combin. Theory 26 (1979) 217-225) that the maximum genus of a graph is mainly determined by its Betti deficiency @x(G). Let G be a k-edge-connected graph (k=<3) whose complementary graph has the chromatic number m. In this paper we prove that the Betti deficiency @x(G) is bounded by a function f"k(m) on m, and the bound is the best possible. Thus by Xuong's maximum genus theorem we obtain some new results on the lower bounds of the maximum genus of graphs.
8485722
The maximum genus of graphs with diameter three
Abstract This paper shows that if G is a simple graph with diameter three then G is up-embeddable unless G is either a Δ2-graph (Fig. 1) or a Δ3-graph (Fig. 2) with ξ(G) = 2, i.e., the maximum genus γM(G) = (β(G) − 2)/2.
2832131
Applying Data Mining to Pseudo-Relevance Feedback for High Performance Text Retrieval
In this paper, we investigate the use of data mining, in particular the text classification and co-training techniques, to identify more relevant passages based on a small set of labeled passages obtained from the blind feedback of a retrieval system. The data mining results are used to expand query terms and to re-estimate some of the parameters used in a probabilistic weighting function. We evaluate the data mining based feedback method on the TREC HARD data set. The results show that data mining can be successfully applied to improve the text retrieval performance. We report our experimental findings in detail.
6825460
DynamicWorkflow Management Using Performance Data
An approach to dynamic workflow management and optimisation using performance data is presented. We discuss strategies for choosing an optimal service (based on user specified criteria) from several semantically equivalent Web Services. Such an approach involves finding "similar" services, by first pruning the set of discovered services based on service metadata, and subsequently selecting an optimal service based on data recorded during prior executions of a service and/or current machine loads. We describe the current implementation of the system, and demonstrate this by a BLAST (used in BioInformatics for Protein-alignment) example by using the Ganglia monitoring tool to get performance data.
764405
Quantization based on statistical moments for signal detection: design and analysis
This paper addresses a signal detection problem based on a class of quantizers. The known moments of the underlying noise distribution are used to index a set of quantization points that have been predetermined under an assumed noise model. The fact that lower order moments are easy to obtain and that they are required in the implementation and analysis of most threshold detectors makes this approach quite appealing. The performance of the resulting quantizers is shown to be relatively insensitive to variations in the underlying noise distribution and to small deviations of the presumed moments. The detector's performance in the finite-sample-size case is investigated, and the distribution resulting in the highest false alarm rate is described for both symmetric and asymmetric noise cases. By setting the test threshold according to the worst distribution, a lower bound on the detector's performance is guaranteed. >
5977047
Decision tree evolution using limited number of labeled data items from drifting data streams
Most previously proposed mining methods on data streams make an unrealistic assumption that "labelled" data stream is readily available and can be mined at anytime. However, in most real-world problems, labelled data streams are rarely immediately available. Due to this reason, models are reconstructed only when labelled data become available periodically. This passive stream mining model has several drawbacks. We propose a concept of demand-driven active data mining. In active mining, the loss of the model is either continuously guessed without using any true class labels or estimated, whenever necessary, from a small number of instances whose actual class labels are verified by paying an affordable cost. When the estimated loss is more than a tolerable threshold, the model evolves by using a small number of instances with verified true class labels. Previous work on active mining concentrates on error guess and estimation. In this paper, we discuss several approaches on decision tree evolution.
39326914
Parameterized subscriptions in publish/subscribe systems
Traditional publish/subscribe systems commonly deal with static subscriptions, whose event filtering criteria stay fixed once defined. Although systems with static subscriptions are simpler to implement, there are cases where the subscription criterion involves state that changes frequently over time. Rather than having the user re-submit his/her subscription repeatedly, we propose parameterized subscriptions as a systematic solution for adaptive subscriptions. Parameterized subscriptions depend on one or more parameters, which are state variables stored and maintained automatically by the publish/subscribe servers. In this paper, we modify traditional publish/subscribe protocols in order to deal with parameterized subscriptions. We then look at certain optimizations that improve the efficiency by controlling where and how much state is allocated in the system. Finally, we present a simple evaluation framework to illustrate the fundamental operating differences between several schemes.