Search results for "decomposition"
showing 10 items of 766 documents
Potential effects of transgenic cotton on soil ecosystem processes in Vietnam.
2008
This chapter concentrates on the potential effects of transgenic cotton on the soil ecosystem of three major cotton growing areas in Vietnam: the coastal lowlands region, the central highlands (eastern and western Truong Son Mountain Range) and the south-eastern region. Soils in these three regions are very different, so it will be necessary to assess the effects of transgenic cotton on typical soils from all three regions. The soils in the south-eastern region are Luvisols, Andosols and Acrisols. In the central highlands, the soils are mainly Luvisols, Rhodic Ferrasols and Haplic Acrisols. The soils in the coastal lowlands region are mainly delta soils, consolidated occasionally by grey li…
New Types of Jacobian-Free Approximate Riemann Solvers for Hyperbolic Systems
2017
We present recent advances in PVM (Polynomial Viscosity Matrix) methods based on internal approximations to the absolute value function. These solvers only require a bound on the maximum wave speed, so no spectral decomposition is needed. Moreover, they can be written in Jacobian-free form, in which only evaluations of the physical flux are used. This is particularly interesting when considering systems with complex Jacobians, as the relativistic magnetohydrodynamics (RMHD) equations. The proposed solvers have also been extended to the case of approximate DOT (Dumbser-Osher-Toro) methods, which can be regarded as simple and efficient approximations to the classical Osher-Solomon method. Som…
Exploiting ongoing EEG with multilinear partial least squares during free-listening to music
2016
During real-world experiences, determining the stimulus-relevant brain activity is excitingly attractive and is very challenging, particularly in electroencephalography. Here, spectrograms of ongoing electroencephalogram (EEG) of one participant constructed a third-order tensor with three factors of time, frequency and space; and the stimulus data consisting of acoustical features derived from the naturalistic and continuous music formulated a matrix with two factors of time and the number of features. Thus, the multilinear partial least squares (PLS) conforming to the canonical polyadic (CP) model was performed on the tensor and the matrix for decomposing the ongoing EEG. Consequently, we …
Higher-order Nonnegative CANDECOMP/PARAFAC Tensor Decomposition Using Proximal Algorithm
2019
Tensor decomposition is a powerful tool for analyzing multiway data. Nowadays, with the fast development of multisensor technology, more and more data appear in higherorder (order > 4) and nonnegative form. However, the decomposition of higher-order nonnegative tensor suffers from poor convergence and low speed. In this study, we propose a new nonnegative CANDECOM/PARAFAC (NCP) model using proximal algorithm. The block principal pivoting method in alternating nonnegative least squares (ANLS) framework is employed to minimize the objective function. Our method can guarantee the convergence and accelerate the computation. The results of experiments on both synthetic and real data demonstrate …
Deriving electrophysiological brain network connectivity via tensor component analysis during freely listening to music
2020
Recent studies show that the dynamics of electrophysiological functional connectivity is attracting more and more interest since it is considered as a better representation of functional brain networks than static network analysis. It is believed that the dynamic electrophysiological brain networks with specific frequency modes, transiently form and dissolve to support ongoing cognitive function during continuous task performance. Here, we propose a novel method based on tensor component analysis (TCA), to characterize the spatial, temporal, and spectral signatures of dynamic electrophysiological brain networks in electroencephalography (EEG) data recorded during free music-listening. A thr…
The Tucker tensor decomposition for data analysis: capabilities and advantages
2022
Tensors are powerful multi-dimensional mathematical objects, that easily embed various data models such as relational, graph, time series, etc. Furthermore, tensor decomposition operators are of great utility to reveal hidden patterns and complex relationships in data. In this article, we propose to study the analytical capabilities of the Tucker decomposition, as well as the differences brought by its major algorithms. We demonstrate these differences through practical examples on several datasets having a ground truth. It is a preliminary work to add the Tucker decomposition to the Tensor Data Model, a model aiming to make tensors data-centric, and to optimize operators in order to enable…
Multi-domain Features of the Non-phase-locked Component of Interest Extracted from ERP Data by Tensor Decomposition
2020
The waveform in the time domain, spectrum in the frequency domain, and topography in the space domain of component(s) of interest are the fundamental indices in neuroscience research. Despite the application of time–frequency analysis (TFA) to extract the temporal and spectral characteristics of non-phase-locked component (NPLC) of interest simultaneously, the statistical results are not always expectedly satisfying, in that the spatial information is not considered. Complex Morlet wavelet transform is widely applied to TFA of event-related-potential (ERP) data, and mother wavelet (which should be firstly defined by center frequency and bandwidth (CFBW) before using the method to TFA of ERP…
Sparse nonnegative tensor decomposition using proximal algorithm and inexact block coordinate descent scheme
2021
Nonnegative tensor decomposition is a versatile tool for multiway data analysis, by which the extracted components are nonnegative and usually sparse. Nevertheless, the sparsity is only a side effect and cannot be explicitly controlled without additional regularization. In this paper, we investigated the nonnegative CANDECOMP/PARAFAC (NCP) decomposition with the sparse regularization item using l1-norm (sparse NCP). When high sparsity is imposed, the factor matrices will contain more zero components and will not be of full column rank. Thus, the sparse NCP is prone to rank deficiency, and the algorithms of sparse NCP may not converge. In this paper, we proposed a novel model of sparse NCP w…
Thermal and thermo-oxidative stability and kinetics of decomposition of PHBV/sisal composites
2017
The decomposition behaviours of composites made of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) and sisal were assessed in terms of thermal stability and decomposition kinetics, under inert and oxidative conditions, by means of multi-rate linear non-isothermal thermogravimetric experiments. A statistical design of experiments was applied to study the influence of the addition of sisal (0-10-20-30%wt), the presence coupling agent (Yes/No) and the applied conditions of work (inert or oxidative). An improvement of the thermal and thermo-oxidative stability of PHBV with the addition of sisal was observed for all cases. An accurate methodology based on iso-conversional methods was applied…
Structural distortions in homoleptic (RE)4A (E = O, S, Se; A = C, Si, Ge, Sn): Implications for the CVD of tin sulfides
2001
The structures of Sn(SBut)4 and Sn(SCy)4 have been determined and adopt S4 and D2 conformations respectively; the anion [(PhS)Sn3]−, as its Ph4P+ salt, has a structure approaching Cs symmetry. In all three compounds, there are large variations in the ∠S–Sn–S within the same molecule, which have been rationalised in terms of the C–S–Sn–S–C conformations. For Sn(SR)4, the ∠S–Sn–S increases as the conformations change from trans, trans to trans, gauche and gauche, gauche, as the number of eclipsed lone pairs decreases and this rationale is shown to be applicable to a variety of A(ER)4 (A = C, Si, Ge, Sn; E = O, S, Se) and related [Mo(SR)4, Ga(SR)4−] systems. AM1 calculations have been used to …