Marching towards decolonisation: notes and reflections

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Time-stepping error bounds for fractional diffusion problems with non-smooth initial data

We apply the piecewise constant, discontinuous Galerkin method to discretize a fractional diffusion equation with respect to time. Using Laplace transform techniques, we show that the method is first order accurate at the \$n\$th time level \$t_n\$, but the error bound includes a factor \$t_n^{-1}\$ if we assume no smoothness of the initial data. We also show that for smoother initial data the growth in the error bound as \$t_n\$ decreases is milder, and in some cases absent altogether. Our error bounds generalize known results for the classical heat equation and are illustrated for a model problem.Comment: 22 pages, 5 figure

Formation of nanocrystalline aluminum magnesium alloys by mechanical alloying

The effect of the nominal Mg content and the milling time on the microstructure and the hardness of mechanically alloyed Al (Mg) solid solutions is studied. The crystallite size distribution and the dislocation structure are determined by X-ray diffraction peak profile analysis and the hardness is obtained from depth sensing indentation test. Magnesium gradually goes into solid solution during ball milling and after about 3 h almost complete solid solution state is attained up to the nominal Mg content of the alloys. With increasing milling time the dislocation density, the hardness and the Mg content in solid solution are increasing, whereas the crystallite size is decreasing. A similar tendency of these parameters is observed at a particular duration of ball milling with increasing of the nominal Mg content. At the same time for long milling period the dislocation density slightly decreases together with a slight reduction of the hardness

A new Backdoor Attack in CNNs by training set corruption without label poisoning

Backdoor attacks against CNNs represent a new threat against deep learning systems, due to the possibility of corrupting the training set so to induce an incorrect behaviour at test time. To avoid that the trainer recognises the presence of the corrupted samples, the corruption of the training set must be as stealthy as possible. Previous works have focused on the stealthiness of the perturbation injected into the training samples, however they all assume that the labels of the corrupted samples are also poisoned. This greatly reduces the stealthiness of the attack, since samples whose content does not agree with the label can be identified by visual inspection of the training set or by running a pre-classification step. In this paper we present a new backdoor attack without label poisoning Since the attack works by corrupting only samples of the target class, it has the additional advantage that it does not need to identify beforehand the class of the samples to be attacked at test time. Results obtained on the MNIST digits recognition task and the traffic signs classification task show that backdoor attacks without label poisoning are indeed possible, thus raising a new alarm regarding the use of deep learning in security-critical applications