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We investigate asymptotic properties of partial sums and sample covariances for linear processes whose innovations are dependent. Central limit theorems and invariance principles are established under fairly mild conditions. Our results go beyond earlier ones by allowing a quite wide class of innovations which includes many important non-linear time series(More)
efficiency opportunities in data centers H. F. Hamann T. G. van Kessel M. Iyengar J.-Y. Chung W. Hirt M. A. Schappert A. Claassen J. M. Cook W. Min Y. Amemiya V. López J. A. Lacey M. O’Boyle The combination of rapidly increasing energy use of data centers (DCs), which is triggered by dramatic increases in IT (information technology) demands, and increases(More)
D id you know your brain continuously emits electric waves, even while you sleep? Based on a sample of wave measurements, physicians specializing in sleep medicine can use statistical tools to classify your sleep pattern as normal or problematic. Brain-computer interfaces (BCIs) now being developed can classify a disabled person' s thinking based on wave(More)
The P300 brain-computer interface (BCI) using electroencephalogram (EEG) signals can allow amyotrophic lateral sclerosis (ALS) patients to instruct computers to perform tasks. To strengthen the P300 response and increase classification accuracy, we proposed an experimental design where characters are intensified according to orthogonal Latin square pairs.(More)
Sleep staging is the pattern recognition task of classifying sleep recordings into sleep stages. This task is one of the most important steps in sleep analysis. It is crucial for the diagnosis and treatment of various sleep disorders, and also relates closely to brain-machine interfaces. We report an automatic, online sleep stager using electroencephalogram(More)
Dimensionality reduction techniques are widespread in pattern recognition research. Principal component analysis, as one of the most popular methods used, is optimal when the data points reside on a linear subspace. Nevertheless, it may fail to preserve the local structure if the data reside on some nonlinear manifold, which is indisputably important in(More)
Recently, several manifold learning algorithms have been proposed, such as ISOMAP (Tenenbaum et al., 2000), Locally Linear Embedding (Roweis & Saul, 2000), Laplacian Eigenmap (Belkin & Niyogi, 2001), Locality Preserving Projection (LPP) (He & Niyogi, 2003), etc. All of them aim at discovering the meaningful low dimensional structure of the data space.(More)
We studied the age distribution of duplicate genes in each of four eukaryotic genomes: human, Arabidopsis thaliana, Caenorhabditis elegans, and Drosophila melanogaster. The four distributions differ greatly from each other, contrary to the previous proposal of a universal L-shaped distribution in all eukaryotic genomes studied. Indeed, only the distribution(More)