Perfection of materials technology for producing improved Gunn-effect devices

Chemical vapor deposition system for improved Gunn effect devices using arsenic chloride 3 metho

How model sets can be determined by their two-point and three-point correlations

We show that real model sets with real internal spaces are determined, up to translation and changes of density zero by their two- and three-point correlations. We also show that there exist pairs of real (even one dimensional) aperiodic model sets with internal spaces that are products of real spaces and finite cyclic groups whose two- and three-point correlations are identical but which are not related by either translation or inversion of their windows. All these examples are pure point diffractive. Placed in the context of ergodic uniformly discrete point processes, the result is that real point processes of model sets based on real internal windows are determined by their second and third moments.Comment: 19 page

Deferring the learning for better generalization in radial basis neural networks

Proceeding of: International Conference Artificial Neural Networks — ICANN 2001. Vienna, Austria, August 21–25, 2001The level of generalization of neural networks is heavily dependent on the quality of the training data. That is, some of the training patterns can be redundant or irrelevant. It has been shown that with careful dynamic selection of training patterns, better generalization performance may be obtained. Nevertheless, generalization is carried out independently of the novel patterns to be approximated. In this paper, we present a learning method that automatically selects the most appropriate training patterns to the new sample to be predicted. The proposed method has been applied to Radial Basis Neural Networks, whose generalization capability is usually very poor. The learning strategy slows down the response of the network in the generalisation phase. However, this does not introduces a significance limitation in the application of the method because of the fast training of Radial Basis Neural Networks

Calculation of Transactinide Homolog Isotope Production Reactions Possible with the Center for Accelerator Mass Spectrometry (CAMS) at Lawrence Livermore National Laboratory

The LLNL heavy element group has been investigating the chemical properties of the heaviest elements over the past several years. The properties of the transactinides (elements with Z > 103) are often unknown due to their low production rates and short half-lives, which require lengthy cyclotron irradiations in order to make enough atoms for statistically significant evaluations of their chemistry. In addition, automated chemical methods are often required to perform consistent and rapid chemical separations on the order of minutes for the duration of the experiment, which can last from weeks to months. Separation methods can include extraction chromatography, liquid-liquid extraction, or gas-phase chromatography. Before a lengthy transactinide experiment can be performed at an accelerator, a large amount of preparatory work must be done both to ensure the successful application of the chosen chemical system to the transactinide chemistry problem being addressed, and to evaluate the behavior of the lighter elemental homologs in the same chemical system. Since transactinide chemistry is literally performed on one single atom, its chemical properties cannot be determined from bulk chemical matrices, but instead must be inferred from the behavior of the lighter elements that occur in its chemical group and in those of its neighboring elements. By first studying the lighter group homologs in a particular chemical system, when the same system is applied to the transactinide element under investigation, its decay properties can be directly compared to those of the homologues, thereby allowing an inference of its own chemistry. The Center for Accelerator Mass Spectrometry (CAMS) at Lawrence Livermore National Laboratory (LLNL) includes a 1 MV Tandem accelerator, capable of accelerating light ions such as protons to energies of roughly 15 MeV. By using the CAMS beamline, tracers of transactinide homolog elements can be produced both for development of chemical systems and for evaluation of homolog chemical properties. CAMS also offers an environment for testing these systems 'online' by incorporating automated chemical systems into the beamline so that tracers can be created, transported, and chemically separated all on the shorter timescales required for transactinide experiments. Even though CAMS is limited in the types and energies of ions they can accelerate, there are still a wide variety of reactions that can be performed there with commercially available target materials. The half-lives of these isotopes vary over a range that could be used for both online chemistry (where shorter half-lives are required) and benchtop tracers studies (where longer lived isotopes are preferred). In this document, they present a summary of tracer production reactions that could be performed at CAMS, specifically for online, automated chemical studies. They are from chemical groups four through seven, 13, and 14, which would be appropriate for studies of elements 104-107, 113, and 114. Reactions were selected that had (a) commercially available target material, (b) half-lives long enough for transport from a target chamber to an automated chemistry system, and (c) cross-sections at CAMS available projectile energies that were large enough to produce enough atoms to result in a statistically relevant signal after losses for transport and chemistry were considered. In addition, the resulting product atoms had to decay with an observable gamma-ray using standard Ge gamma-ray detectors. The table includes calculations performed for both metal targets and their corresponding oxides

Lazy training of radial basis neural networks

Proceeding of: 16th International Conference on Artificial Neural Networks, ICANN 2006. Athens, Greece, September 10-14, 2006Usually, training data are not evenly distributed in the input space. This makes non-local methods, like Neural Networks, not very accurate in those cases. On the other hand, local methods have the problem of how to know which are the best examples for each test pattern. In this work, we present a way of performing a trade off between local and non-local methods. On one hand a Radial Basis Neural Network is used like learning algorithm, on the other hand a selection of the training patterns is used for each query. Moreover, the RBNN initialization algorithm has been modified in a deterministic way to eliminate any initial condition influence. Finally, the new method has been validated in two time series domains, an artificial and a real world one.This article has been financed by the Spanish founded research MEC project OPLINK::UC3M, Ref: TIN2005-08818-C04-0

Measurements of near forward scattered laser light in a large ICF plasma

We describe an instrument which measures the angular spread and spectrum of near forward scattered laser light from a probe beam in a long scalelength laser-plasma. The instrument consists of a combination of time integrating and time resolving detectors which measure the scattered light amplitude over four orders of magnitude for a range of angles. These measurements allow us to study the beam spray resulting from various laser and plasma conditions and determine the density fluctuations associated with this beam spray