I am enclosing a copy of the revised decommissioning plan for the University of Illinois Advanced TRIGA reactor facility. The revised plan is submitted in its entirety. I am also enclosing the Final Status Survey plan per our discussion. If there are any questions please do not hesitate to contact me. I declare under penalty of perjury that the foregoing is true and correct. Executed on August 20, 2007. Sincerely,

The proliferation of topic hierarchies for text documents has resulted in a need for tools that automatically classify new documents within such hierarchies. Existing classification schemes which ignore the hierarchical structure and treat the topics as separate classes are often inadequate in text

Automatic query expansion has long been suggested as a technique for dealing with the fundamental issue of word mismatch in information retrieval. A number of approaches to expansion have been studied and, more recently, attention has focused on techniques that analyze the corpus to discover word

This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. This is important because in many text classification problems obtaining training labels is expensive, while large

Document clustering has not been well received as an information retrieval tool. Objections to its use fall into two main categories: first, that clustering is too slow for large corpora (with running time often quadratic in the number of documents); and second, that clustering does not appreciably

Modelling extensions for polymorphic signals, final library elements Prepared by Issued by

In this contribution we present analysis and simulation results on controlling TCP rates by buffer allocation. When segment loss is low, TCP throughput depends primarily on the TCP window size, and the round trip time of the connection. As a result, it is possible to control TCP rates with FIFO queuing.

FLUKA calculations for the beam dump system of the LHC: Energy deposition in the dump core and particle spectra in the beam loss monitors

Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized

Data integration is the problem of combining data residing at different sources, and providing the user with a unified view of these data. The problem of designing data integration systems is important in current real world applications, and is characterized by a number of issues