Conductivity of Silicon Inversion Layers: comparison with and without in-plane magnetic field

A detailed comparison is presented of the temperature dependence of the conductivity of dilute, strongly interacting electrons in two-dimensional silicon inversion layers in the metallic regime in the presence and in the absence of a magnetic field. We show explicitly and quantitatively that a magnetic field applied parallel to the plane of the electrons reduces the slope of the conductivity versus temperature curves to near zero over a broad range of electron densities extending from $n_c$ to deep in the metallic regime where the high field conductivity is on the order of $10 e^2/h$. The strong suppression (or "quenching") of the metallic behavior by a magnetic field sets an important constraint on theory.Comment: 4 pages, 4 figure

Juncture stress fields in multicellular shell structures. Volume IV - Stresses and deformations of fixed-edge segmental spherical shells Final report

Equations for thin elastic spherical shells and digital program for analysis of stresses and deformation of fixed edge segmental spherical shells - solution by finite difference techniqu

Sequential RBF function estimator: memory regression network

The newal-network training algorithm can be divided into 2 categories: (I) Batch mode and (2) Sequential mode. In this paper, a novel online RBF network called "Memory Regression Network (MRN)" is proposed. Different from the previous approaches [2, 11], MRN involves two types of memories: Experience and Neuron, which handle short and long term memories respectively. By simulating human's learning behavior, a given function can be estimated without memorizing the whole training set. Two sets of function estimation experiments are examined in order to illustrate the performance of the proposed algorithm. The results show that MRN can effectively approximate the given function within a reasonable time and acceptable mean square error. © 2004 IEEE.published_or_final_versio