13,785 research outputs found
Could the 21-cm absorption be explained by the dark matter suggested by Be transitions?
The stronger than expected 21-cm absorption was observed by EDGES recently,
and another anomaly of Be transitions would be signatures of new
interactions. These two issues may be related to each other, e.g., pseudoscalar
mediated fermionic millicharged dark matter (DM), and the 21-cm absorption
could be induced by photon mediated scattering between MeV millicharged DM and
hydrogen. This will be explored in this paper. For fermionic millicharged DM
with masses in a range of , the
p-wave annihilation would be dominant during DM
freeze-out. The s-wave annihilation is tolerant by constraints from CMB and the 21-cm absorption. The
millicharged DM can evade constraints from direct detection experiments. The
process of with the invisible decay could be employed to search for the millicharged DM, and future high
intensity sources, such as NA62, will do the job.Comment: 6 pages, 2 figures, the accepted version, EPJ
Zero-Shot Visual Recognition using Semantics-Preserving Adversarial Embedding Networks
We propose a novel framework called Semantics-Preserving Adversarial
Embedding Network (SP-AEN) for zero-shot visual recognition (ZSL), where test
images and their classes are both unseen during training. SP-AEN aims to tackle
the inherent problem --- semantic loss --- in the prevailing family of
embedding-based ZSL, where some semantics would be discarded during training if
they are non-discriminative for training classes, but could become critical for
recognizing test classes. Specifically, SP-AEN prevents the semantic loss by
introducing an independent visual-to-semantic space embedder which disentangles
the semantic space into two subspaces for the two arguably conflicting
objectives: classification and reconstruction. Through adversarial learning of
the two subspaces, SP-AEN can transfer the semantics from the reconstructive
subspace to the discriminative one, accomplishing the improved zero-shot
recognition of unseen classes. Comparing with prior works, SP-AEN can not only
improve classification but also generate photo-realistic images, demonstrating
the effectiveness of semantic preservation. On four popular benchmarks: CUB,
AWA, SUN and aPY, SP-AEN considerably outperforms other state-of-the-art
methods by an absolute performance difference of 12.2\%, 9.3\%, 4.0\%, and
3.6\% in terms of harmonic mean value
Data-driven approach for modeling Reynolds stress tensor with invariance preservation
The present study represents a data-driven turbulent model with Galilean
invariance preservation based on machine learning algorithm. The fully
connected neural network (FCNN) and tensor basis neural network (TBNN) [Ling et
al. (2016)] are established. The models are trained based on five kinds of flow
cases with Reynolds Averaged Navier-Stokes (RANS) and high-fidelity data. The
mappings between two invariant sets, mean strain rate tensor and mean rotation
rate tensor as well as additional consideration of invariants of turbulent
kinetic energy gradients, and the Reynolds stress anisotropy tensor are
trained. The prediction of the Reynolds stress anisotropy tensor is treated as
user's defined RANS turbulent model with a modified turbulent kinetic energy
transport equation. The results show that both FCNN and TBNN models can provide
more accurate predictions of the anisotropy tensor and turbulent state in
square duct flow and periodic flow cases compared to the RANS model. The
machine learning based turbulent model with turbulent kinetic energy gradient
related invariants can improve the prediction precision compared with only mean
strain rate tensor and mean rotation rate tensor based models. The TBNN model
is able to predict a better flow velocity profile compared with FCNN model due
to a prior physical knowledge.Comment: 23 page
Asymmetry of Left Versus Right Lateral Face in Face Recognition
Prior research has found that the left side of the face is emotionally more expressive than the right side [1]. This was demonstrated in a study where the right and the left halves of a face image were combined with their mirror-reversed duplicates to make composite images. When observers were asked which composite face appeared more emotional, they selected the left-left over the right-right composite more often
- …