Batch normalization (batch norm) is often used in an attempt to stabilize and accelerate training in deep neural networks. In many cases it indeed decreases the number of parameter updates required to achieve low training error. However, it also reduces robustness to small adversarial input perturbations and noise by double-digit percentages, as we show on five standard datasets. Furthermore, substituting weight decay for batch norm is sufficient to nullify the relationship between adversarial vulnerability and the input dimension. Our work is consistent with a mean-field analysis that found that batch norm causes exploding gradients.
Abstract.Deuterium retention in plasma-sprayed tungsten (PSW) was investigated by means of the thermodesorption technique. . The observed retention in PSW is four times higher than in polycrystalline tungsten (PCW). An additional high-temperature peak was found in the thermodesorption spectra of PSW (at 1050 K). The nature of the high-temperature peak and the mechanism of higher retention in PSW are discussed.
A concept of the divertor and the technology for organizing the edge plasma in a fusion neutron source based on a spherical tokamak (FNS ST) are described. The experimental data on the characteristics of the peripheral plasma in modern tokamaks are extrapolated to the FNS ST conditions with the help of semi analytical models. The effects depending on the magnetic configuration and on the geometry and mate rials of the divertor and the first wall elements are considered. Possible designs of the FNS ST divertor and the first wall are described. Using an original model, it is shown that the maximum density of the heat flux at the divertor plates in a double null magnetic configuration does not exceed 5-6 MW/m 2 , which complies with modern engineering capabilities. Methods for further improvement of the FNS ST divertor concept are analyzed.
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