A sprinkling of stardust

Laboratory Studies of Stardust

Sprinkling Topics for Weakly Supervised Text Classification

Supervised text classification algorithms require a large number of documents labeled by humans, that involve a laborintensive and time consuming process. In this paper, we propose a weakly supervised algorithm in which supervision comes in the form of labeling of Latent Dirichlet Allocation (LDA) topics. We then use this weak supervision to “sprinkle” artificial words to the training documents...

Stardust in meteorites.

Primitive meteorites, interplanetary dust particles, and comets contain dust grains that formed around stars that lived their lives before the solar system formed. These remarkable objects have been intensively studied since their discovery a little over twenty years ago and they provide samples of other stars that can be studied in the laboratory in exquisite detail with modern analytical tool...

Supervised Latent Semantic Indexing Using Adaptive Sprinkling

Latent Semantic Indexing (LSI) has been shown to be effective in recovering from synonymy and polysemy in text retrieval applications. However, since LSI ignores class labels of training documents, LSI generated representations are not as effective in classification tasks. To address this limitation, a process called ‘sprinkling’ is presented. Sprinkling is a simple extension of LSI based on au...

Stardust Wild 2 Dust Measurements

McDonnell, A. J. Tuzzolino, T. E. Economou, B. C. Clark, Z. Sekanina, P. Tsou, D. E. Brownlee, PSSRI, The Open University, Walton Hall, Milton Keynes, MK7 6AA, UK, [email protected], Laboratory for Astrophysics and Space Research, Enrico Fermi Institute, University of Chicago, 933 East 56 St, Chicago Ill 60637, USA, Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grov...