82,338 research outputs found
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
Skew -Derivations on Semiprime Rings
For a ring with an automorphism , an -additive mapping
is called a skew
-derivation with respect to if it is always a -derivation
of for each argument. Namely, it is always a -derivation of for
the argument being left once arguments are fixed by elements in
. In this short note, starting from Bre\v{s}ar Theorems, we prove that a
skew -derivation () on a semiprime ring must map into the
center of .Comment: 8 page
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
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