Compactness properties of operator multipliers

We continue the study of multidimensional operator multipliers initiated in~cite{jtt}. We introduce the notion of the symbol of an operator multiplier. We characterise completely compact operator multipliers in terms of their symbol as well as in terms of approximation by finite rank multipliers. We give sufficient conditions for the sets of compact and completely compact multipliers to coincide and characterise the cases where an operator multiplier in the minimal tensor product of two C*-algebras is automatically compact. We give a description of multilinear modular completely compact completely bounded maps defined on the direct product of finitely many copies of the C*-algebra of compact operators in terms of tensor products, generalising results of Saar

Polymer conditioning of alum sludge and discrepancies between estimates of the optimum dosage

The paper outlines the effects of polymer conditioning on alum sludge properties, such as floc size, density, fractal dimension (DF) and rheological properties. Experimental results demonstrate that polymer conditioning of alum sludge leads to: larger floc size with a plateau reached in higher doses; higher densities associated with higher doses; increased degree of compactness; and an initial decrease followed by an increase of supernatant viscosity with continued increase in polymer dose. The secondary focus of this paper dwells on a comparison of the estimates of optimum dose using different criteria that emanate from established dewatering tests such as CST, SRF, liquid phase viscosity and modified SRF as well as a simple settlement test in terms of CML30. Alum sludge was derived from a water works treating coloured, low-turbidity raw waters.

A two-stage algorithm for identification of nonlinear dynamic systems

This paper investigates the two-stage stepwise identification for a class of nonlinear dynamic systems that can be described by linear-in-the-parameters models, and the model has to be built from a very large pool of basis functions or model terms. The main objective is to improve the compactness of the model that is obtained by the forward stepwise methods, while retaining the computational efficiency. The proposed algorithm first generates an initial model using a forward stepwise procedure. The significance of each selected term is then reviewed at the second stage and all insignificant ones are replaced, resulting in an optimised compact model with significantly improved performance. The main contribution of this paper is that these two stages are performed within a well-defined regression context, leading to significantly reduced computational complexity. The efficiency of the algorithm is confirmed by the computational complexity analysis, and its effectiveness is demonstrated by the simulation results.

Efficient modelling technique for fractal electromagnetic band-gap arrays

An efficient modelling technique is proposed for the analysis of a fractal-element electromagnetic band-gap array. The modelling is based on a method of moments modal analysis in conjunction with an interpolation scheme, which significantly accelerates the computations. The plane-wave and the surface-wave responses of the structure have been studied by means of transmission coefficients and dispersion diagrams. The multiband properties and the compactness of the proposed structure are presented. The technique is general and can be applied to arbitrary-shaped element geometries.

Fast automatic two-stage nonlinear model identification based on the extreme learning machine

It is convenient and effective to solve nonlinear problems with a model that has a linear-in-the-parameters (LITP) structure. However, the nonlinear parameters (e.g. the width of Gaussian function) of each model term needs to be pre-determined either from expert experience or through exhaustive search. An alternative approach is to optimize them by a gradient-based technique (e.g. Newton’s method). Unfortunately, all of these methods still need a lot of computations. Recently, the extreme learning machine (ELM) has shown its advantages in terms of fast learning from data, but the sparsity of the constructed model cannot be guaranteed. This paper proposes a novel algorithm for automatic construction of a nonlinear system model based on the extreme learning machine. This is achieved by effectively integrating the ELM and leave-one-out (LOO) cross validation with our two-stage stepwise construction procedure [1]. The main objective is to improve the compactness and generalization capability of the model constructed by the ELM method. Numerical analysis shows that the proposed algorithm only involves about half of the computation of orthogonal least squares (OLS) based method. Simulation examples are included to confirm the efficacy and superiority of the proposed technique.

Optical quantum memory for polarization qubits with V-type three-level atoms

We investigate an optical quantum memory scheme with V-type three-level atoms based on the controlled reversible inhomogeneous broadening (CRIB) technique. We theoretically show the possibility to store and retrieve a weak light pulse interacting with the two optical transitions of the system. This scheme implements a quantum memory for a polarization qubit - a single photon in an arbitrary polarization state - without the need of two spatially separated two-level media, thus offering the advantage of experimental compactness overcoming the limitations due to mismatching and unequal efficiencies that can arise in spatially separated memories. The effects of a relative phase change between the atomic levels, as well as of phase noise due to, for example, the presence of spurious electric and magnetic fields are analyzed.

Propositional and Relational Bayesian Networks Associated with Imprecise and Qualitative Probabilistic Assessments

This paper investigates a representation language with flexibility inspired by probabilistic logic and compactness inspired by relational Bayesian networks. The goal is to handle propositional and first-order constructs together with precise, imprecise, indeterminate and qualitative probabilistic assessments. The paper shows how this can be achieved through the theory of credal networks. New exact and approximate inference algorithms based on multilinear programming and iterated/loopy propagation of interval probabilities are presented; their superior performance, compared to existing ones, is shown empirically.

Two-Stage Orthogonal Least Squares Methods for Neural Network Construction

A number of neural networks can be formulated as the linear-in-the-parameters models. Training such networks can be transformed to a model selection problem where a compact model is selected from all the candidates using subset selection algorithms. Forward selection methods are popular fast subset selection approaches. However, they may only produce suboptimal models and can be trapped into a local minimum. More recently, a two-stage fast recursive algorithm (TSFRA) combining forward selection and backward model refinement has been proposed to improve the compactness and generalization performance of the model. This paper proposes unified two-stage orthogonal least squares methods instead of the fast recursive-based methods. In contrast to the TSFRA, this paper derives a new simplified relationship between the forward and the backward stages to avoid repetitive computations using the inherent orthogonal properties of the least squares methods. Furthermore, a new term exchanging scheme for backward model refinement is introduced to reduce computational demand. Finally, given the error reduction ratio criterion, effective and efficient forward and backward subset selection procedures are proposed. Extensive examples are presented to demonstrate the improved model compactness constructed by the proposed technique in comparison with some popular methods.

Forward and backward least angle regression for nonlinear system identification

A forward and backward least angle regression (LAR) algorithm is proposed to construct the nonlinear autoregressive model with exogenous inputs (NARX) that is widely used to describe a large class of nonlinear dynamic systems. The main objective of this paper is to improve model sparsity and generalization performance of the original forward LAR algorithm. This is achieved by introducing a replacement scheme using an additional backward LAR stage. The backward stage replaces insignificant model terms selected by forward LAR with more significant ones, leading to an improved model in terms of the model compactness and performance. A numerical example to construct four types of NARX models, namely polynomials, radial basis function (RBF) networks, neuro fuzzy and wavelet networks, is presented to illustrate the effectiveness of the proposed technique in comparison with some popular methods.

Recent advances in laser-driven neutron sources

Due to the limited number and high cost of large-scale neutron facilities, there has been a growing interest in compact accelerator-driven sources. In this context, several potential schemes of laser-driven neutron sources are being intensively studied employing laser-accelerated electron and ion beams. In addition to the potential of delivering neutron beams with high brilliance, directionality and ultra-short burst duration, a laser-driven neutron source would offer further advantages in terms of cost-effectiveness, compactness and radiation confinement by closed-coupled experiments. Some of the recent advances in this field are discussed,
showing improvements in the directionality and flux of the laser-driven neutron beams.