Exploration of Large Digital Sky Surveys

We review some of the scientific opportunities and technical challenges posed by the exploration of the large digital sky surveys, in the context of a Virtual Observatory (VO). The VO paradigm will profoundly change the way observational astronomy is done. Clustering analysis techniques can be used to discover samples of rare, unusual, or even previously unknown types of astronomical objects and phenomena. Exploration of the previously poorly probed portions of the observable parameter space are especially promising. We illustrate some of the possible types of studies with examples drawn from DPOSS; much more complex and interesting applications are forthcoming. Development of the new tools needed for an efficient exploration of these vast data sets requires a synergy between astronomy and information sciences, with great potential returns for both fields.Comment: To appear in: Mining the Sky, eds. A. Banday et al., ESO Astrophysics Symposia, Berlin: Springer Verlag, in press (2001). Latex file, 18 pages, 6 encapsulated postscript figures, style files include

Novel modeling of task versus rest brain state predictability using a dynamic time warping spectrum: comparisons and contrasts with other standard measures of brain dynamics

Dynamic time warping, or DTW, is a powerful and domain-general sequence alignment method for computing a similarity measure. Such dynamic programming-based techniques like DTW are now the backbone and driver of most bioinformatics methods and discoveries. In neuroscience it has had far less use, though this has begun to change. We wanted to explore new ways of applying DTW, not simply as a measure with which to cluster or compare similarity between features but in a conceptually different way. We have used DTW to provide a more interpretable spectral description of the data, compared to standard approaches such as the Fourier and related transforms. The DTW approach and standard discrete Fourier transform (DFT) are assessed against benchmark measures of neural dynamics. These include EEG microstates, EEG avalanches, and the sum squared error (SSE) from a multilayer perceptron (MLP) prediction of the EEG time series, and simultaneously acquired FMRI BOLD signal. We explored the relationships between these variables of interest in an EEG-FMRI dataset acquired during a standard cognitive task, which allowed us to explore how DTW differentially performs in different task settings. We found that despite strong correlations between DTW and DFT-spectra, DTW was a better predictor for almost every measure of brain dynamics. Using these DTW measures, we show that predictability is almost always higher in task than in rest states, which is consistent to other theoretical and empirical findings, providing additional evidence for the utility of the DTW approach