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

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

In-tank fluid sloshing effects during earthquakes: A preliminary computational simulation

Hundreds of underground radioactive waste storage tanks are located at Department of Energy (DOE) sites. At present, no technique for evaluating the pressure loads due to the impact of earthquake generated waves on the side walls and dome of the tanks is known if the wave breaks back on itself. This paper presents the results of two-dimensional Computational Fluid Dynamics (CFD) calculations of the motion of waves in a generic rectangular tank as the result of accelerations recorded during an earthquake. The advantages and limitations of this technique and methods for avoiding the limitations will be discussed

Prosocial response to client-instigated victimization: the roles of forgiveness and workgroup conflict

We investigate forgiveness as a human service employee coping response to client-instigated victimizations and further explore the role of workgroup conflict in 1) facilitating this response, and 2) influencing the relationship between victimization and workplace outcomes. Using the theoretical lens of Conservation of Resources (Hobfoll, 1989), we propose that employees forgive clients – especially in the context of low workgroup conflict. From low to moderate levels of client-instigated victimization, we suggest that victimization and forgiveness are positively related; however, this positive relationship does not prevail when individuals confront egregious levels of victimization (i.e., an inverted-U shape). This curvilinear relationship holds under low but not under high workgroup conflict. Extending this model to workplace outcomes, findings also demonstrate that the indirect effects of victimization on job satisfaction, burnout, and turnover intentions are mediated by forgiveness when workgroup conflict is low. Experiment- and field-based studies provide evidence for the theoretical model

Porting Decision Tree Algorithms to Multicore using FastFlow

The whole computer hardware industry embraced multicores. For these machines, the extreme optimisation of sequential algorithms is no longer sufficient to squeeze the real machine power, which can be only exploited via thread-level parallelism. Decision tree algorithms exhibit natural concurrency that makes them suitable to be parallelised. This paper presents an approach for easy-yet-efficient porting of an implementation of the C4.5 algorithm on multicores. The parallel porting requires minimal changes to the original sequential code, and it is able to exploit up to 7X speedup on an Intel dual-quad core machine.Comment: 18 pages + cove

Kondo effect of non-magnetic impurities and the co-existing charge order in the cuprate superconductors

We present a theory of Kondo effect caused by an induced magnetic moment near non-magnetic impurities such as Zn and Li in the cuprate superconductors. Based on the co-existence of charge order and superconductivity, a natural description of the induced moment and the resulting Kondo effect is obtained in the framework of bond-operator theory of microscopic t-J-V Hamiltonian. The local density of state near impurities is computed in a self-consistent Bogoliubov-de Gennes theory which shows a low-energy peak in the middle of superconducting gap. Our theory also suggests that the charge order can be enhanced near impuries.Comment: 5 pages, 4 figure

General and age-specific fertility rates in non-affective psychosis : population-based analysis of Scottish women

The study was funded by Chief Scientist Office, Scottish Government Health and Social Care Directorate (Grant CZH/4/951), NHS Research Scotland (NHS Research Scotland Career Research Fellowship).Peer reviewedPublisher PD

A model for spin-polarized transport in perovskite manganite bi-crystal grain boundaries

We have studied the temperature dependence of low-field magnetoresistance and current-voltage characteristics of a low-angle bi-crystal grain boundary junction in perovskite manganite La_{2/3}Sr_{1/3}MnO_3 thin film. By gradually trimming the junction we have been able to reveal the non-linear behavior of the latter. With the use of the relation M_{GB} \propto M_{bulk}\sqrt{MR^*} we have extracted the grain boundary magnetization. Further, we demonstrate that the built-in potential barrier of the grain boundary can be modelled by V_{bi}\propto M_{bulk}^2 - M_{GB}^2. Thus our model connects the magnetoresistance with the potential barrier at the grain boundary region. The results indicate that the band-bending at the grain boundary interface has a magnetic origin.Comment: 9 pages, 5 figure

Toroidal mode number estimation of the edge-localized modes using the KSTAR 3-D electron cyclotron emission imaging system

A new and more accurate technique is presented for determining the toroidal mode number n of edge-localized modes (ELMs) using two independent electron cyclotron emission imaging (ECEI) systems in the Korea Superconducting Tokamak Advanced Research (KSTAR) device. The technique involves the measurement of the poloidal spacing between adjacent ELM filaments, and of the pitch angle ?? O of filaments at the plasma outboard midplane. Equilibrium reconstruction verifies that ?? O is nearly constant and thus well-defined at the midplane edge. Estimates of n obtained using two ECEI systems agree well with n measured by the conventional technique employing an array of Mirnov coils.open3