Skew NN-Derivations on Semiprime Rings

For a ring $R$ with an automorphism $\sigma$, an $n$-additive mapping $\Delta:R\times R\times... \times R \rightarrow R$ is called a skew $n$-derivation with respect to $\sigma$ if it is always a $\sigma$-derivation of $R$ for each argument. Namely, it is always a $\sigma$-derivation of $R$ for the argument being left once $n-1$ arguments are fixed by $n-1$ elements in $R$. In this short note, starting from Bre\v{s}ar Theorems, we prove that a skew $n$-derivation ($n\geq 3$) on a semiprime ring $R$ must map into the center of $R$.Comment: 8 page

Electron correlation and spin-orbit coupling effects in US3 and USe3

A systematic density functional theory (DFT)+U study is conducted to investigate the electron correlation and spin-orbit coupling (SOC) effects in US3 and USe3. Our calculations reveal that inclusion of the U term is essential to get energy band gaps for them, indicating the strong correlation effects for uranium 5f electrons. Taking consideration of the SOC effect results in small reduction on the electronic band gaps of US3 and USe3, but largely changes the energy band shapes around the Fermi energy. As a result, US3 has a direct band gap while USe3 has an indirect one. Our calculations predict that both US3 and USe3 are antiferromagnetic insulators, in agreement with corresponding experimental results. Based on our DFT+U calculations, we systematically present the ground-state electronic, mechanical, and Raman properties for US3 and USe3.Comment: 6 pages, 6 figure

Cross-Domain Image Retrieval with Attention Modeling

With the proliferation of e-commerce websites and the ubiquitousness of smart phones, cross-domain image retrieval using images taken by smart phones as queries to search products on e-commerce websites is emerging as a popular application. One challenge of this task is to locate the attention of both the query and database images. In particular, database images, e.g. of fashion products, on e-commerce websites are typically displayed with other accessories, and the images taken by users contain noisy background and large variations in orientation and lighting. Consequently, their attention is difficult to locate. In this paper, we exploit the rich tag information available on the e-commerce websites to locate the attention of database images. For query images, we use each candidate image in the database as the context to locate the query attention. Novel deep convolutional neural network architectures, namely TagYNet and CtxYNet, are proposed to learn the attention weights and then extract effective representations of the images. Experimental results on public datasets confirm that our approaches have significant improvement over the existing methods in terms of the retrieval accuracy and efficiency.Comment: 8 pages with an extra reference pag