25,133 research outputs found
decay in scalar and vector leptoquark scenarios
It has been shown that the anomalies observed in and decays can be
resolved by adding a single scalar or vector leptoquark to the Standard Model,
while constraints from other precision measurements in the flavour sector can
be satisfied without fine-tuning. To further explore these two interesting
scenarios, in this paper, we study their effects in the semi-leptonic
decay. Using the best-fit solutions for
the operator coefficients allowed by the current data of mesonic decays, we
find that (i) the two scenarios give similar amounts of enhancements to the
branching fraction and the
ratio , (ii) the
two best-fit solutions in each of these two scenarios are also
indistinguishable from each other, (iii) both scenarios give nearly the same
predictions as those of the Standard Model for the longitudinal polarizations
of and as well as the lepton-side forward-backward
asymmetry. With future measurements of these observables in
decay at the LHCb, the two leptoquark
scenarios could be further tested, and even differentiated from the other NP
explanations for the anomalies. We also discuss the
feasibility for the measurements of these observables at the LHC and the future
colliders.Comment: 29 pages, 4 tables and 2 figures; More references and the feasibility
for the measurements of the observables in these decays at the LHC and the
future colliders added, final version published in the journa
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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