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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)
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)
The spatio-temporal relationship is an essential aspect of road traffic prediction. The fundamental observation is that the traffic condition at a link is affected by the immediate past traffic conditions of some number of its neighboring links. A time lag function defines how traffic flows are related in the temporal dimension. In parallel, the spatial(More)
This paper presents a statistical approach to predict the public bus arrival time based on traffic information management system. It considers a number of factors affecting bus travel time, such as departure time, work day, current bus location, number of links, number of intersections, passenger demand at each stop and traffic status of the urban network,(More)
Did 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)
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)
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)