In this paper we focus on the microwave bistatic scattering process, with the aim of deriving an expression for the interferometric complex field auto-correlation function from a static platform. We start from the Fresnel integral and derive the auto-correlation function in the Fraunhofer and Modified Fraunhofer regime. The autocorrelation function at short times can be expressed as a Gaussian ...

To evaluate coherent structures in the dimension of time and scale, a new procedure based on the multiresolution analysis and multiresolution auto-correlation analysis is developed in this paper. By analyzing uand v-components of fluctuating velocity, the coherent structure and its scales can be identified when larger amplitude fluctuation and stronger auto-correlation are appeared at same wave...

Mathematical morphology (MM) is a theory for nonlinear image and signal processing that was originally based on complete lattices and is usually still conducted in this framework. Later, MM was extended from complete lattices to complete inf-semilattices (cisls) using reference functions. Recently an auto-associative memory model based on a cisl was introduced by Sussner and Medeiros who conduc...

This paper presents a simple and effective approach to improve dependency parsing by using subtrees from auto-parsed data. First, we use a baseline parser to parse large-scale unannotated data. Then we extract subtrees from dependency parse trees in the auto-parsed data. Finally, we construct new subtree-based features for parsing algorithms. To demonstrate the effectiveness of our proposed app...

Spectral tuning is a sound processing method based on the phase vocoder. In this method, most or all partials are tuned to a fixed set of frequency values corresponding to a desired scale. If the input sound is a monophonic melody, the result is a kind of auto-tuning, providing greater flexibility in tuning schemas than with commercial auto-tuning programs. When applied to polyphonic or textura...

The enormous and growing complexity of today's high-end systems has increased the already significant challenges of obtaining high performance on today's equally complex scientific applications. Application scientists are faced with a daunting challenge in tuning their codes to exploit performance-enhancing architectural features. The Performance Engineering Research Institute (PERI) is working...

In the last decades, named entity recognition has been extensively studied with various supervised learning approaches depend on massive labeled data. In this paper, we focus on person name recognition in judgment documents. Owing to the lack of human-annotated data, we propose a joint learning approach, namely Aux-LSTM, to use a large scale of auto-annotated data to help human-annotated data (...

Starting from the traditional definition of auto-correlation function we can introduce a new quantity, based on the wavelet formalism, called local wavelet auto-correlation function which contains more information about the correlation properties of a given signal on different scales. The application of this formalism to the temporal variations of solar activity, from the monthly time series of...

We apply auto-tuning to a hybrid MPI-pthreads lattice Boltzmann computation running on the Cray XT4 at National Energy Research Scientific Computing Center (NERSC). Previous work showed that multicorespecific auto-tuning can improve the performance of lattice Boltzmann magnetohydrodynamics (LBMHD) by a factor of 4× when running on dualand quad-core Opteron dual-socket SMPs. We extend these stud...