48,569 research outputs found
Implications of Fermi-LAT observations on the origin of IceCube neutrinos
The IceCube (IC) collaboration recently reported the detection of TeV-PeV
extraterrestrial neutrinos whose origin is yet unknown. By the photon-neutrino
connection in and interactions, we use the \fermi-LAT
observations to constrain the origin of the IC detected neutrinos. We find that
Galactic origins, i.e., the diffuse Galactic neutrinos due to cosmic ray (CR)
propagation in the Milky Way, and the neutrinos from the Galactic point
sources, may not produce the IC neutrino flux, thus these neutrinos should be
of extragalactic origin. Moreover, the extragalactic gamma-ray bursts (GRBs)
may not account for the IC neutrino flux, the jets of active galactic nuclei
may not produce the IC neutrino spectrum, but the starburst galaxies (SBGs) may
be promising sources. As suggested by the consistency between the IC detected
neutrino flux and the Waxman-Bahcall bound, GRBs in SBGs may be the sources of
both the ultrahigh energy, eV, CRs and the ~PeV CRs that
produce the IC detected TeV-PeV neutrinos.Comment: JCAP accepted version; 8 pages, 2 figs; discussion on blazar origin
added; conclusion unchange
Integrated Face Analytics Networks through Cross-Dataset Hybrid Training
Face analytics benefits many multimedia applications. It consists of a number
of tasks, such as facial emotion recognition and face parsing, and most
existing approaches generally treat these tasks independently, which limits
their deployment in real scenarios. In this paper we propose an integrated Face
Analytics Network (iFAN), which is able to perform multiple tasks jointly for
face analytics with a novel carefully designed network architecture to fully
facilitate the informative interaction among different tasks. The proposed
integrated network explicitly models the interactions between tasks so that the
correlations between tasks can be fully exploited for performance boost. In
addition, to solve the bottleneck of the absence of datasets with comprehensive
training data for various tasks, we propose a novel cross-dataset hybrid
training strategy. It allows "plug-in and play" of multiple datasets annotated
for different tasks without the requirement of a fully labeled common dataset
for all the tasks. We experimentally show that the proposed iFAN achieves
state-of-the-art performance on multiple face analytics tasks using a single
integrated model. Specifically, iFAN achieves an overall F-score of 91.15% on
the Helen dataset for face parsing, a normalized mean error of 5.81% on the
MTFL dataset for facial landmark localization and an accuracy of 45.73% on the
BNU dataset for emotion recognition with a single model.Comment: 10 page
Further understanding of the non- decays of
We provide details of the study of non- decays into
, where and denote light vector meson and pseudoscalar meson,
respectively. We find that the electromagnetic (EM) interaction plays little
role in these processes, while the strong interaction dominates. The strong
interaction can be separated into two parts, i.e. the short-distance part
probing the wave function at origin and the long-distance part reflecting the
soft gluon exchanged dynamics. The long-distance part is thus described by the
intermediate charmed meson loops. We show that the transition of can be related to such that the parameters in our model
can be constrained by comparing the different parts in to
those in . Our quantitative results confirm the findings of
[Zhang {\it et al.}, Phys. Rev. Lett. 102, 172001 (2009)] that the
OZI-rule-evading long-distance strong interaction via the IML plays an
important role in decays, and could be a key towards a full
understanding of the mysterious non- decay mechanism.Comment: 11 pages, 4 figures, version to appear in Phys. Rev.
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