Electrophoretic deposition of gradated oxidation resistant coatings on tantalum-10 tungsten alloy

Material selection and electrophoretic deposition studies of high temperature oxidation resistant coatings on tantalum-10 tungsten allo

Development of oxidation resistant coatings for use above 3500 deg F

Physical property evaluation of oxidation resistant coating materials for high temperature protection of tantalum-base alloy

Letter From the Editor

Welcome to the third issue of The Richmond Journal of Law and Technology\u27s seventh publication term! The 2000-2001 academic year has proved to be one of the most productive and exciting in the Journal\u27s decorated history. Our Editorial Board and staff have done a phenomenal job on the Journal\u27s seventh volume and we are very proud of the issue we have worked to prepare for you today

Efficient Biologically Plausible Adversarial Training

Artificial Neural Networks (ANNs) trained with Backpropagation (BP) show astounding performance and are increasingly often used in performing our daily life tasks. However, ANNs are highly vulnerable to adversarial attacks, which alter inputs with small targeted perturbations that drastically disrupt the models' performance. The most effective method to make ANNs robust against these attacks is adversarial training, in which the training dataset is augmented with exemplary adversarial samples. Unfortunately, this approach has the drawback of increased training complexity since generating adversarial samples is very computationally demanding. In contrast to ANNs, humans are not susceptible to adversarial attacks. Therefore, in this work, we investigate whether biologically-plausible learning algorithms are more robust against adversarial attacks than BP. In particular, we present an extensive comparative analysis of the adversarial robustness of BP and Present the Error to Perturb the Input To modulate Activity (PEPITA), a recently proposed biologically-plausible learning algorithm, on various computer vision tasks. We observe that PEPITA has higher intrinsic adversarial robustness and, with adversarial training, has a more favourable natural-vs-adversarial performance trade-off as, for the same natural accuracies, PEPITA's adversarial accuracies decrease in average by 0.26% and BP's by 8.05%

Quasi-classical Molecular Dynamics Simulations of the Electron Gas: Dynamic properties

Results of quasi-classical molecular dynamics simulations of the quantum electron gas are reported. Quantum effects corresponding to the Pauli and the Heisenberg principle are modeled by an effective momentum-dependent Hamiltonian. The velocity autocorrelation functions and the dynamic structure factors have been computed. A comparison with theoretical predictions was performed.Comment: 8 figure

Equation of state of a strongly magnetized hydrogen plasma

The influence of a constant uniform magnetic field on the thermodynamic properties of a partially ionized hydrogen plasma is studied. Using the method of Green' s function various interaction contributions to the thermodynamic functions are calculated. The equation of state of a quantum magnetized plasma is presented within the framework of a low density expansion up to the order e^4 n^2 and, additionally, including ladder type contributions via the bound states in the case of strong magnetic fields (2.35*10^{5} T << B << 2.35*10^{9} T). We show that for high densities (n=10^{27-30} m^{-3}) and temperatures T=10^5 - 10^6 K typical for the surface of neutron stars nonideality effects as, e.g., Debye screening must be taken into account.Comment: 12 pages, 2 Postscript figures. uses revtex, to appear in Phys. Rev.

Convergence of simple adaptive Galerkin schemes based on h − h/2 error estimators

We discuss several adaptive mesh-refinement strategies based on (h − h/2)-error estimation. This class of adaptivemethods is particularly popular in practise since it is problem independent and requires virtually no implementational overhead. We prove that, under the saturation assumption, these adaptive algorithms are convergent. Our framework applies not only to finite element methods, but also yields a first convergence proof for adaptive boundary element schemes. For a finite element model problem, we extend the proposed adaptive scheme and prove convergence even if the saturation assumption fails to hold in general

Dielectric function of a two-component plasma including collisions

A multiple-moment approach to the dielectric function of a dense non-ideal plasma is treated beyond RPA including collisions in Born approximation. The results are compared with the perturbation expansion of the Kubo formula. Sum rules as well as Ward identities are considered. The relations to optical properties as well as to the dc electrical conductivity are pointed out.Comment: latex, 10 pages, 7 figures in ps forma