A Global Model of β−\beta^--Decay Half-Lives Using Neural Networks

Statistical modeling of nuclear data using artificial neural networks (ANNs) and, more recently, support vector machines (SVMs), is providing novel approaches to systematics that are complementary to phenomenological and semi-microscopic theories. We present a global model of $\beta^-$-decay halflives of the class of nuclei that decay 100% by $\beta^-$ mode in their ground states. A fully-connected multilayered feed forward network has been trained using the Levenberg-Marquardt algorithm, Bayesian regularization, and cross-validation. The halflife estimates generated by the model are discussed and compared with the available experimental data, with previous results obtained with neural networks, and with estimates coming from traditional global nuclear models. Predictions of the new neural-network model are given for nuclei far from stability, with particular attention to those involved in r-process nucleosynthesis. This study demonstrates that in the framework of the $\beta^-$-decay problem considered here, global models based on ANNs can at least match the predictive performance of the best conventional global models rooted in nuclear theory. Accordingly, such statistical models can provide a valuable tool for further mapping of the nuclidic chart.Comment: Proceedings of the 16th Panhellenic Symposium of the Hellenic Nuclear Physics Societ

Performance statistics of the FORTRAN 4 /H/ library for the IBM system/360

Test procedures and results for accuracy and timing tests of the basic IBM 360/50 FORTRAN 4 /H/ subroutine library are reported. The testing was undertaken to verify performance capability and as a prelude to providing some replacement routines of improved performance

Oxidation characteristics of Ti-14Al-21Nb ingot alloy

Static oxidation kinetics of Ti14Al21Nb (wt pct) ingot alloy were studied in air over the temperature interval of 649 to 1093 C in a thermogravimetric apparatus. The oxidation products were characterized by x ray diffraction, electron microprobe analysis, energy dispersive x ray analysis, and Auger electron spectroscopy. Cross-sections of the oxidized samples were also examined using light and scanning electron microscopy. The oxidation rate was substantially lower than the conventional alloys of titanium, but the kinetics displayed a complex behavior involving two or more oxidation rates depending on the temperature and duration of exposure. The primary oxide formed was TiO2, but this oxide was doped with Nb. Small amounts of Al2O3 and TiN were also present in the scale. Diffusion of oxygen into the alloy was observed and the diffusivity seemed to be dependent on the microstructure of the metal. A model was presented to explain the oxidation behavior of the alloy in terms of the reduction in the oxygen diffusivity in the oxide caused by the modification of the defect structure of TiO2 by Nb ions

Statistical Global Modeling of Beta-Decay Halflives Systematics Using Multilayer Feedforward Neural Networks and Support Vector Machines

In this work, the beta-decay halflives problem is dealt as a nonlinear optimization problem, which is resolved in the statistical framework of Machine Learning (LM). Continuing past similar approaches, we have constructed sophisticated Artificial Neural Networks (ANNs) and Support Vector Regression Machines (SVMs) for each class with even-odd character in Z and N to global model the systematics of nuclei that decay 100% by the beta-minus-mode in their ground states. The arising large-scale lifetime calculations generated by both types of machines are discussed and compared with each other, with the available experimental data, with previous results obtained with neural networks, as well as with estimates coming from traditional global nuclear models. Particular attention is paid on the estimates for exotic and halo nuclei and we focus to those nuclides that are involved in the r-process nucleosynthesis. It is found that statistical models based on LM can at least match or even surpass the predictive performance of the best conventional models of beta-decay systematics and can complement the latter.Comment: 8 pages, 1 fiqure, Proceedings of the 17th HNPS Symposiu

Index tracking with utility enhanced weighting

Passive index investing involves investing in a fund that replicates a market index. Enhanced indexation uses the returns of an index as a reference point and aims at outperforming this index. The motivation behind enhanced indexing is that the indices and portfolios available to academics and practitioners for asset pricing and benchmarking are generally inefficient and, thus, susceptible to enhancement. In this paper we propose a novel technique based on the concept of cumulative utility area ratios and the Analytic Hierarchy Process (AHP) to construct enhanced indices from the DJIA and S&P500. Four main conclusions are forthcoming. First, the technique, called the utility enhanced tracking technique (UETT), is computationally parsimonious and applicable for all return distributions. Second, if desired, cardinality constraints are simple and computationally parsimonious. Third, the technique requires only infrequent rebalancing, monthly at the most. Finally, the UETT portfolios generate consistently higher out-of-sample utility profiles and after-cost returns for the fully enhanced portfolios as well as for the enhanced portfolios adjusted for cardinality constraints. These results are robust to varying market conditions and a range of utility functions

Theory of Feshbach molecule formation in a dilute gas during a magnetic field ramp

Starting with coupled atom-molecule Boltzmann equations, we develop a simplified model to understand molecule formation observed in recent experiments. Our theory predicts several key features: (1) the effective adiabatic rate constant is proportional to density; (2) in an adiabatic ramp, the dependence of molecular fraction on magnetic field resembles an error function whose width and centroid are related to the temperature; (3) the molecular production efficiency is a universal function of the initial phase space density, the specific form of which we derive for a classical gas. Our predictions show qualitative agreement with the data from [Hodby et al, Phys. Rev. Lett. {\bf{94}}, 120402 (2005)] without the use of adjustable parameters

A critical analysis of the tools to evaluate neuropsychiatric lupus.

Neuropsychiatric symptoms occur commonly in patients with systemic lupus erythematosus, but they are not always due to active disease. It is crucial to identify cases that are due to active systemic lupus erythematosus so that appropriate treatment can be instituted. There is no single serological or imaging test that distinguishes active neuropsychiatric systemic lupus erythematosus from neuropsychiatric manifestations caused by other factors such as infection. Most patients with neuropsychiatric systemic lupus erythematosus have generalised features of disease activity. Raised anti-dsDNA and low C3 complement levels are often seen, but are not an invariable guide. The presence of antiphospholipid antibodies is more suggestive of thrombotic than inflammatory causation. A number of other autoantibody tests have been proposed as biomarkers for neuropsychiatric systemic lupus erythematosus, but results in clinical studies have been inconsistent and none has so far entered routine clinical practice. Cerebrospinal fluid features and magnetic resonance imaging appearances are non-specific in neuropsychiatric systemic lupus erythematosus, but are useful in excluding other causes of neuropsychiatric symptoms. Newer magnetic resonance imaging sequences show promise for distinguishing new neuropsychiatric systemic lupus erythematosus activity from previous damage and recent research suggests these may correlate with changes in cognitive function in patients with systemic lupus erythematosus. However, formal cognitive testing is seldom carried out in the acute setting

Detection of Single Ion Spectra by Coulomb Crystal Heating

The coupled motion of ions in a radiofrequency trap has been used to connect the frequency- dependent laser-induced heating of a sympathetically cooled spectroscopy ion with changes in the fluorescence of a laser-cooled control ion. This technique, sympathetic heating spectroscopy, is demonstrated using two isotopes of calcium. In the experiment, a few scattered photons from the spectroscopy ion are transformed into a large deviation from the steady-state fluorescence of the control ion. This allows us to detect an optical transition where the number of scattered photons is below our fluorescence detection limit. Possible applications of the technique to molecular ion spectroscopy are briefly discussed.Comment: 7 Pages,10 Figure