databases, its performance degrades rapidly with the level of attribute noise in training instances. Therefore, we extended it with a significance test to distinguish noisy instances. This extended algorithm's performance degrades gracefully with increasing noise levels and compares favorably with a

by invoking congestion control and avoidance algorithms, resulting in degraded end-to-end performance in wireless and lossy systems. In this paper, we compare several schemes designed to improve the performance of TCP in such networks. We classify these schemes into three broad categories: end

requirements of GLS grow slowly with the size of the network. Furthermore, GLS tolerates node failures well: each failure has only a limited effect and query performance degrades gracefully as nodes fail and restart. The query performance of GLS is also relatively insensitive to node speeds. Simple geographic

Abstract—We analyze performance degradation phenomena due to software aging on a real supercomputer deployed at the Federico II University of Naples, by considering a dataset of ten months of operational usage. We adopted a statistical approach for identifying when and where the supercomputer

fraction of such performance degrading events. This paper analyzes the dynamic instruction stream leading up to these performance degrading instructions to identify the operations necessary to execute them early. The backward slice (the subset of the program that relates to the instruction

. Surprisingly, learning phrases longer than three words and learning phrases from high-accuracy wordlevel alignment models does not have a strong impact on performance. Learning only syntactically motivated phrases degrades the performance of our systems. 1

-Bayes slightly outperformed C4.5 on average. We also show that in some cases, the performance of the C4.5 induction algorithm signi cantly improved if features were discretized in advance � in our experiments, the performance never signi cantly degraded, an interesting phenomenon considering the fact that C4

and run-time mechanisms that support mobility, and techniques for implementing mobility that do not degrade the performance of local operations. Performance measurements of the current implementation are included.

human performance. (i) The learning follows a power law. (;i) The more words the network learns, the better it is at generalizing and correctly pronouncing new words, (iii) The performance of the network degrades very slowly as connections in the network are damaged: no single link or processing unit

of applications, often being significantly faster (in terms of computation time) than competing methods. Although the performance of GP methods tends to degrade as the regularization term is de-emphasized, we show how they can be embedded in a continuation scheme to recover their efficient practical performance.