Applications of the representative points in statistical simulations

The paper gives a new approach to statistical simulation and resampling by the use of numbertheoretic methods and representative points. Resempling techniques take samples from an approximate population. The bootstrap suggests to use a random sample to form an approximate population. We propose to construct some approximate population distribution by the use of two kinds of representative points, and samples are taken from these approximate distributions. The statistical inference is based on those samples. The statistical inference in this paper involves estimation of mean, variance, skewness, kurtosis, quantile and density of the population distribution. Our results show that the new method can significantly improve the results by the use of Monte Carlo methods.

A sparse algorithm for adaptive pruning least square support vector regression machine based on global representative point ranking

Least square support vector regression(LSSVR)is a method for function approximation,whose solutions are typically non-sparse,which limits its application especially in some occasions of fast prediction.In this paper,a sparse algorithm for adaptive pruning LSSVR algorithm based on global representative point ranking(GRPR-AP-LSSVR)is proposed.At first,the global representative point ranking(GRPR)algorithm is given,and relevant data analysis experiment is implemented which depicts the importance ranking of data points.Furthermore,the pruning strategy of removing two samples in the decremental learning procedure is designed to accelerate the training speed and ensure the sparsity.The removed data points are utilized to test the temporary learning model which ensures the regression accuracy.Finally,the proposed algorithm is verified on artificial datasets and UCI regression datasets,and experimental results indicate that,compared with several benchmark algorithms,the GRPR-AP-LSSVR algorithm has excellent sparsity and prediction speed without impairing the generalization performance.

Schedule refinement for homogeneous multi-core processors in the presence of manufacturing-caused heterogeneity

Multi-core homogeneous processors have been widely used to deal with computation-intensive embedded applications. However, with the continuous down scaling of CMOS technology, within-die variations in the manufacturing process lead to a significant spread in the operating speeds of cores within homogeneous multi-core processors. Task scheduling approaches, which do not consider such heterogeneity caused by within-die variations,can lead to an overly pessimistic result in terms of performance. To realize an optimal performance according to the actual maximum clock frequencies at which cores can run, we present a heterogeneity-aware schedule refining(HASR) scheme by fully exploiting the heterogeneities of homogeneous multi-core processors in embedded domains.We analyze and show how the actual maximum frequencies of cores are used to guide the scheduling. In the scheme,representative chip operating points are selected and the corresponding optimal schedules are generated as candidate schedules. During the booting of each chip, according to the actual maximum clock frequencies of cores, one of the candidate schedules is bound to the chip to maximize the performance. A set of applications are designed to evaluate the proposed scheme. Experimental results show that the proposed scheme can improve the performance by an average value of 22.2%, compared with the baseline schedule based on the worst case timing analysis. Compared with the conventional task scheduling approach based on the actual maximum clock frequencies, the proposed scheme also improves the performance by up to 12%.

Competition mechanism of multiple four-wave mixing in highly nonlinear fiber： spatial instability and satellite characteristics

Competition mechanism in multiple four-wave mixing （MFWM） processes is demonstrated theoretically. Provided considering only two waves injected into a highly nonlinear fiber （HNLF）, there are three modes displaying comprehensive dynamic behaviors, such as fixed points, periodic motion, and chaotic motion. Especially, Mode C of MFWM is emphasized by analyzing its phase-space trajectory to demonstrate nonlinear wave- wave interactions. The study shows that, when the phase- space trajectory approaches or gets through a saddle point, a dramatic power depletion for the injected wave can be realized, with the representative point moving chaotically, but when phase-space trajectories are distributed around a center point, the power for the injected wave is retained almost invariable, with the representative point moving periodically. Finally, the evolvement of satellite wave over an optical fiber is investigated by comparing it with the interference pattern in Young＇s double-slit experiment.