Ultra-fast spin dynamics in the 2s2p^2 configuration of C^+ studied by time-dependent R-matrix theory

We employ time-dependent R-matrix theory to study ultra-fast dynamics in the doublet 2s2p(2) configuration of C+ for a total magnetic quantum number M = 1. In contrast to the dynamics observed for M = 0, ultra-fast dynamics for M = 1 is governed by spin dynamics in which the 2s electron acts as a flag rather than a spectator electron. Under the assumption that m(S) = 1/2, m(2s) = 1/2 allows spin dynamics involving the two 2p electrons, whereas m(2s) = -1/2 prevents spin dynamics of the two 2p electrons. For a pump-probe pulse scheme with (h) over bar omega(pump) = 10.9 eV and (h) over bar omega(probe) = 16.3 eV and both pulses six cycles long, little sign of spin dynamics is observed in the total ionization probability. Signs of spin dynamics can be observed, however, in the ejected-electron momentum distributions. We demonstrate that the ejected-electron momentum distributions can be used for unaligned targets to separate the contributions of initial M = 0 and M = 1 levels. This would, in principle, allow unaligned target ions to be used to obtain information on the different dynamics in the 2s2p(2) configuration for the M = 0 and M = 1 levels from a single experime

Reduction of sample matrix effects - The analysis of benzimidazole residues in serum by immunobiosensor

Regulatory authorities, the food industry and the consumer demand reliable determination of chemical contaminants present in foods. A relatively new analytical technique that addresses this need is an immunobiosensor based on surface plasmon resonance (SPR) measurements. Although a range of tests have been developed to measure residues in milk, meat, animal bile and honey, a considerable problem has been encountered with both serum and plasma samples. The high degree of non-specific binding of some sample components can lead to loss of assay robustness, increased rates of false positives and general loss of assay sensitivity. In this paper we describe a straightforward precipitation technique to remove interfering substances from serum samples to be analysed for veterinary anthelmintics by SPR. This technique enabled development of an assay to detect a wide range of benzimidazole residues in serum samples by immunobiosensor. The limit of quantification was below 5 ng/ml and coefficients of variation were about 2%.

A New Gradient Descent Approach for Local Learning of Fuzzy Neural Models

The majority of reported learning methods for Takagi-Sugeno-Kang fuzzy neural models to date mainly focus on the improvement of their accuracy. However, one of the key design requirements in building an interpretable fuzzy model is that each obtained rule consequent must match well with the system local behaviour when all the rules are aggregated to produce the overall system output. This is one of the distinctive characteristics from black-box models such as neural networks. Therefore, how to find a desirable set of fuzzy partitions and, hence, to identify the corresponding consequent models which can be directly explained in terms of system behaviour presents a critical step in fuzzy neural modelling. In this paper, a new learning approach considering both nonlinear parameters in the rule premises and linear parameters in the rule consequents is proposed. Unlike the conventional two-stage optimization procedure widely practised in the field where the two sets of parameters are optimized separately, the consequent parameters are transformed into a dependent set on the premise parameters, thereby enabling the introduction of a new integrated gradient descent learning approach. A new Jacobian matrix is thus proposed and efficiently computed to achieve a more accurate approximation of the cost function by using the second-order Levenberg-Marquardt optimization method. Several other interpretability issues about the fuzzy neural model are also discussed and integrated into this new learning approach. Numerical examples are presented to illustrate the resultant structure of the fuzzy neural models and the effectiveness of the proposed new algorithm, and compared with the results from some well-known methods.

RELATIONSHIP BETWEEN WORD AND BIT LEVEL SYSTOLIC ARRAYS AS APPLIED TO MATRIX multiplied by MATRIX MULTIPLICATIONS.

The mapping of matrix multiplied by matrix multiplication onto both word and bit level systolic arrays has been investigated. It has been found that well defined word and bit level data flow constraints must be satisfied within such circuits. An efficient and highly regular bit level array has been generated by exploiting the basic compatibilities in data flow symmetries at each level of the problem. A description of the circuit which emerges is given and some details relating to its practical implementation are discussed.

Microstrcutural evolution of SiC fibre embedded AA6061 matrix induced by ultrasonic consolidation

Ultrasonic consolidation (UC) uses high frequency (20-40KHz) mechanical vibrations to produce a solid-state metallurgical bond (weld) between metal foils. UC as a novel layered manufacturing technique is used in this research to embed reinforcing members such as silicon carbide fibers into the aluminium alloy 6061's matrices. It is known that UC induce volume and surface effect in the material it is acting on. Both effects are employed in embedding active/passive elements in the metal matrix. Whilst the process and the two effects are used and identified at macro level, what is happening at micro level is unknown and hardly studied. In this research we are investigating the phenomena occurring in the microstructure of the parts during UC process to obtain better understanding about how and why the process works. In this research, high-resolution electron backscatter diffraction is used to study the effects of the UC process on the evolution of microstructure in AA6061 with and without fibre elements. The inverse pole figures (IPF), pole figures (PF) and the correlated misorientation angle distribution of the mentioned samples are obtained. The characteristics of the crystallographic orientation, the grain structure and the grain boundary are analysed to find the effect of ultrasonic vibration and embedding fibre on the microstructure and texture of the bond. The ultrasonic vibration will lead to exceptional refinement of grains to a micron level along the bond area and affect the crystallographic orientation. Additional plastic flow occurs around the fibre which leads to the fibre embedding. © 2008 Materials Research Society.

Electron scattering by atoms and ions using the Breit-Pauli Hamiltonian:An R-matrix approach

The R-matrix method describing the scattering of low-energy electrons by complex atoms and ions is extended to include terms of the Breit-Pauli Hamiltonian. An application is made to the astrophysically important 1s 2s S-1s 2s2p P transition in Fe XXIII, where in the most accurate calculations carried out all terms of the 1s 2s, 1s2s2p and 1s2p configurations are included in the expansion describing the collision. This gives up to 28 coupled channels for each total angular momentum and parity which are solved on a CRAY-1. The collision strengths are increased by more than a factor of two from their non-relativistic values at all energies considered.

Harmonic generation by noble-gas atoms in the near-IR regime using ab initio time-dependent R-matrix theory

We demonstrate the capability of ab initio time-dependent R-matrix theory to obtain accurate harmonic generation spectra of noble-gas atoms at near-IR wavelengths between 1200 and 1800 nm and peak intensities up to 1.8 × 10^(14) W/cm^(2). To accommodate the excursion length of the ejected electron, we use an angular-momentum expansion up to Lmax=279. The harmonic spectra show evidence of atomic structure through the presence of a Cooper minimum in harmonic generation for Kr, and of multielectron interaction through the giant resonance for Xe. The theoretical spectra agree well with those obtained experimentally.

Turbo-smt: Accelerating coupled sparse matrix-tensor factorizations by 200x

How can we correlate the neural activity in the human brain as it responds to typed words, with properties of these terms (like ‘edible’, ‘fits in hand’)? In short, we want to find latent variables, that jointly explain both the brain activity, as well as the behavioral responses. This is one of many settings of the Coupled Matrix-Tensor Factorization (CMTF) problem.

Can we accelerate any CMTF solver, so that it runs within a few minutes instead of tens of hours to a day, while maintaining good accuracy? We introduce Turbo-SMT, a meta-method capable of doing exactly that: it boosts the performance of any CMTF algorithm, by up to 200x, along with an up to 65 fold increase in sparsity, with comparable accuracy to the baseline.

We apply Turbo-SMT to BrainQ, a dataset consisting of a (nouns, brain voxels, human subjects) tensor and a (nouns, properties) matrix, with coupling along the nouns dimension. Turbo-SMT is able to find meaningful latent variables, as well as to predict brain activity with competitive accuracy.