20,133 research outputs found
FAST polarization mapping of the SNR VRO 42.05.01
We have obtained the polarization data cube of the VRO 42.05.01 supernova
remnant at 1240 MHz using the Five-hundred-meter Aperture Spherical radio
Telescope (FAST). Three-dimensional Faraday Synthesis is applied to the FAST
data to derive the Faraday depth spectrum. The peak Faraday depth map shows a
large area of enhanced foreground RM of ~60 rad m-2 extending along the
remnant's "wing" section, which coincides with a large-scale HI shell at -20
km/s. The two depolarization patches within the "wing" region with RM of 97 rad
m-2 and 55 rad m-2 coincide with two HI structures in the HI shell. Faraday
screen model fitting on the Canadian Galactic Plane Survey (CGPS) 1420 MHz
full-scale polarization data reveals a distance of 0.7-0.8d_{SNR} in front of
the SNR with enhanced regular magnetic field there. The highly piled-up
magnetic field indicates that the HI shell at -20 km/s could originate from an
old evolved SNR.Comment: 9 pages, 8 figures, accepted by Ap
Repulsion Loss: Detecting Pedestrians in a Crowd
Detecting individual pedestrians in a crowd remains a challenging problem
since the pedestrians often gather together and occlude each other in
real-world scenarios. In this paper, we first explore how a state-of-the-art
pedestrian detector is harmed by crowd occlusion via experimentation, providing
insights into the crowd occlusion problem. Then, we propose a novel bounding
box regression loss specifically designed for crowd scenes, termed repulsion
loss. This loss is driven by two motivations: the attraction by target, and the
repulsion by other surrounding objects. The repulsion term prevents the
proposal from shifting to surrounding objects thus leading to more crowd-robust
localization. Our detector trained by repulsion loss outperforms all the
state-of-the-art methods with a significant improvement in occlusion cases.Comment: Accepted to IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) 201
Complete NLO Operators in the Higgs Effective Field Theory
We enumerate the complete and independent sets of operators at the
next-to-leading order (NLO) in the Higgs effective field theory (HEFT), based
on the Young tensor technique on the Lorentz, gauge and flavor structures. The
operator-amplitude correspondence tells a type of operators forms the on-shell
amplitude basis, and for operators involving in Nambu-Goldstone bosons, the
amplitude basis is further reduced to the subspace satisfying the Adler's zero
condition in the soft momentum limit. Different from dynamical field, the
spurion should not enter into the Lorentz sector, instead it only plays a role
of forming the invariant together with other dynamical fields. With
these new treatments, for the first time we could obtain the 237 (8595)
operators for one (three) generation fermions, 295 (11307) with right-handed
neutrinos, and find there were 6 (9) terms of operators missing and many
redundant operators can be removed in the effective theory without (with)
right-handed neutrinos.Comment: 63 pages, 2 tables, revised version: operators in 4-component
notation, correct typo for countin
MegDet: A Large Mini-Batch Object Detector
The improvements in recent CNN-based object detection works, from R-CNN [11],
Fast/Faster R-CNN [10, 31] to recent Mask R-CNN [14] and RetinaNet [24], mainly
come from new network, new framework, or novel loss design. But mini-batch
size, a key factor in the training, has not been well studied. In this paper,
we propose a Large MiniBatch Object Detector (MegDet) to enable the training
with much larger mini-batch size than before (e.g. from 16 to 256), so that we
can effectively utilize multiple GPUs (up to 128 in our experiments) to
significantly shorten the training time. Technically, we suggest a learning
rate policy and Cross-GPU Batch Normalization, which together allow us to
successfully train a large mini-batch detector in much less time (e.g., from 33
hours to 4 hours), and achieve even better accuracy. The MegDet is the backbone
of our submission (mmAP 52.5%) to COCO 2017 Challenge, where we won the 1st
place of Detection task
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