Comment on 'processing and microstructure of single grain uranium-doped Y-Ba-Cu-O superconductor'

We comment on the paper by N Hari Babu et al. (2002 Supercond. Sci. Technol. 15 104-10) and point out misinterpretations of the chemical composition of U-bearing deposits observed in Y123. The observed small deposits are those of new compounds which do not contain Cu, rather than refined Y211 plus U, as stated by the authors. We further note that extensive literature, not quoted, is in disagreement by nearly an order of magnitude concerning the values of Pt and U doping at which the optimum value of Jc is obtained. Other related information, presently in the literature, which may be helpful to those working with this high temperature superconducting chemical system, is presented.

Beyond dataset bias: Multi-task unaligned shared knowledge transfer

Many visual datasets are traditionally used to analyze the performance of different learning techniques. The evaluation is usually done within each dataset, therefore it is questionable if such results are a reliable indicator of true generalization ability. We propose here an algorithm to exploit the existing data resources when learning on a new multiclass problem. Our main idea is to identify an image representation that decomposes orthogonally into two subspaces: a part specific to each dataset, and a part generic to, and therefore shared between, all the considered source sets. This allows us to use the generic representation as un-biased reference knowledge for a novel classification task. By casting the method in the multi-view setting, we also make it possible to use different features for different databases. We call the algorithm MUST, Multitask Unaligned Shared knowledge Transfer. Through extensive experiments on five public datasets, we show that MUST consistently improves the cross-datasets generalization performance. © 2013 Springer-Verlag.