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 $pp$ and $p\gamma$ 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, $>10^{19}$eV, CRs and the $1-100$~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-DDˉD\bar D decays of ψ(3770)\psi(3770)

We provide details of the study of $\psi(3770)$ non-$D\bar D$ decays into $VP$, where $V$ and $P$ 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 $\psi(3770)\to VP$ can be related to $\psi(3686)\to VP$ such that the parameters in our model can be constrained by comparing the different parts in $\psi(3770)\to VP$ to those in $\psi(3686)\to VP$. 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 $\psi(3770)$ decays, and could be a key towards a full understanding of the mysterious $\psi(3770)$ non-$D\bar{D}$ decay mechanism.Comment: 11 pages, 4 figures, version to appear in Phys. Rev.