7,029 research outputs found
The new interaction suggested by the anomalous Be transition sets a rigorous constraint on the mass range of dark matter
The WIMPs are considered one of the favorable dark matter (DM) candidates,
but as the upper bounds on the interactions between DM and standard model (SM)
particles obtained by the upgraded facilities of DM direct detections get lower
and lower. Researchers turn their attentions to search for less massive DM
candidates, i.e. light dark matter of MeV scale. The recently measured
anomalous transition in Be suggests that there exists a vectorial boson
which may mediate the interaction between DM and SM particles. Based on this
scenario, we combine the relevant cosmological data to constrain the mass range
of DM, and have found that there exists a model parameter space where the
requirements are satisfied, a range of 16.7
MeV for scalar DM, and 16.7 MeV for vectorial
DM is demanded. Then a possibility of directly detecting such light DM
particles via the DM-electron scattering is briefly studied in this framework.Comment: 13 Pages, 7 figures, references added, version accepted by journa
Study on radiative decays of and into by means of LFQM
The observed resonance peak around 2.86 GeV has been carefully reexamined by
the LHCb collaboration and it is found that under the peak there reside two
states and which are considered as
and with slightly different masses and
total widths. Thus, the earlier assumption that the resonance
was a state should not be right. We suggest to measure the partial widths
of radiative decays of and to confirm their
quantum numbers. We would consider as or a pure
state, or their mixture and respectively calculate the corresponding
branching ratios as well as those of . The future precise
measurement would provide us information to help identifying the structures of
those resonances .Comment: 8 pages, 4 figures, 1 tabl
Thoracic Disease Identification and Localization with Limited Supervision
Accurate identification and localization of abnormalities from radiology
images play an integral part in clinical diagnosis and treatment planning.
Building a highly accurate prediction model for these tasks usually requires a
large number of images manually annotated with labels and finding sites of
abnormalities. In reality, however, such annotated data are expensive to
acquire, especially the ones with location annotations. We need methods that
can work well with only a small amount of location annotations. To address this
challenge, we present a unified approach that simultaneously performs disease
identification and localization through the same underlying model for all
images. We demonstrate that our approach can effectively leverage both class
information as well as limited location annotation, and significantly
outperforms the comparative reference baseline in both classification and
localization tasks.Comment: Conference on Computer Vision and Pattern Recognition 2018 (CVPR
2018). V1: CVPR submission; V2: +supplementary; V3: CVPR camera-ready; V4:
correction, update reference baseline results according to their latest post;
V5: minor correction; V6: Identification results using NIH data splits and
various image model
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