© 2013 ETRI Journal, Volume 35, Number 6, December 2013 http://dx.doi.org/10.4218/etrij.13.2013.0040 This paper describes the scalable extension of High Efficiency Video Coding (HEVC) to provide flexible highquality digital video content services. The proposed scalable codec is designed on multi-loop decoding architecture to support inter-layer sample prediction and inter-layer motion parameter...

Inferences need to be drawn in biological systems using experimental multivariate data. The number of samples collected in many such experiments is small, and the data are noisy. We present and study the performance of a robust optimization (RO) model for such situations. We adapt this model to generate a minimum and a maximum estimation of analyte concentration for a given sample, producing a ...

This paper presents a method for freely drawing a graphical password. The new method achieves better security than conventional textual passwords and other graphical password schemes. With this method it is also easier for a user to remember the password. The basic idea of the new method is to use a number of the user's representative sample drawings to predict the user's future drawing predict...

Top differentially expressed gene lists are often inconsistent between studies and it has been suggested that small sample sizes contribute to lack of reproducibility and poor prediction accuracy in discriminative models. We considered sex differences (69♂, 65 ♀) in 134 human skeletal muscle biopsies using DNA microarray. The full dataset and subsamples (n = 10 (5 ♂, 5 ♀) to n = 120 (60 ♂, 60 ♀...

This paper introduces a new approach to prediction by bringing together two different nonparametric ideas: distribution free inference and nonparametric smoothing. Specifically, we consider the problem of constructing nonparametric tolerance/prediction sets. We start from the general conformal prediction approach and we use a kernel density estimator as a measure of agreement between a sample p...

The stability of the inverse of the optimum forward prediction error filter obtained when the input data is nonstationary is investigated. Due to this nonstationary character, the resulting system (which is obtained assuming optimality on a sample-by-sample basis) is time-varying. It turns out that an extension of the Levinson recursion still provides a means to orderupdate the prediction error...

Correspondence K. Krishnamoorthy, Department of Mathematics, University of Louisiana at Lafayette, Lafayette, LA 70504, U.S.A. Email: [email protected] The problems of finding confidence limits for the mean and an upper percentile, and upper prediction limits for the mean of a future sample from a gamma distribution are considered. Simple methods based on cube root transformation and fiduci...

We take a model selection approach to the question of whether forward interest rates are useful in predicting future spot rates, using a variety of out-of-sample forecast-based model selection criteria: forecast mean squared error, forecast direction accuracy, and forecast-based trading system profitability. We also examine the usefulness of a class of novel prediction models called "artificial...

This paper studies an out-of-sample statistic for time-series prediction that is analogous to the widely used R in-sample statistic. We propose and study methods to estimate the variance of this out-of-sample statistic. We suggest that the out-of-sample statistic is more robust to distributional and asymptotic assumptions behind many tests for insample statistics. Furthermore we argue that it m...

Model-based prediction is dependent on many choices ranging from the sample collection and prediction endpoint to the choice of algorithm and its parameters. Here we studied the effects of such choices, exemplified by predicting sensitivity (as IC50) of cancer cell lines towards a variety of compounds. For this, we used three independent sample collections and applied several machine learning a...