16,484 research outputs found
Renormalization group improved predictions for production at hadron colliders
We study the factorization and resummation of the production
at hadron colliders. The cross section in the threshold limit can be factorized
into a convolution of hard and soft functions and parton distribution functions
with the soft-collinear effective theory. We calculate the next-to-leading
order soft function for the associated production of the heavy quark pair and
colorless particle, and we perform the resummation calculation with the
next-to-next-to-leading logarithms accuracy. Our results show that the
resummation effects reduce the dependence of the cross section on the scales
significantly and increase the total cross section by compared with
NLO QCD results.Comment: 23 pages, 7 figures and 2 tables; final version in PR
Threshold resummation for the production of a color sextet (antitriplet) scalar at the LHC
We investigate threshold resummation effects in the production of a color
sextet (antitriplet) scalar at next-to-next-to-leading logarithmic (NNLL) order
at the LHC in the frame of soft-collinear effective theory. We show the total
cross section and the rapidity distribution with NLO+NNLL accuracy, and we
compare them with the NLO results. Besides, we use recent dijet data at the LHC
to give the constraints on the couplings between the colored scalars and
quarks.Comment: 21 pages,9 figures,3 tables; Version published in EPJ
FoveaBox: Beyond Anchor-based Object Detector
We present FoveaBox, an accurate, flexible, and completely anchor-free
framework for object detection. While almost all state-of-the-art object
detectors utilize predefined anchors to enumerate possible locations, scales
and aspect ratios for the search of the objects, their performance and
generalization ability are also limited to the design of anchors. Instead,
FoveaBox directly learns the object existing possibility and the bounding box
coordinates without anchor reference. This is achieved by: (a) predicting
category-sensitive semantic maps for the object existing possibility, and (b)
producing category-agnostic bounding box for each position that potentially
contains an object. The scales of target boxes are naturally associated with
feature pyramid representations. In FoveaBox, an instance is assigned to
adjacent feature levels to make the model more accurate.We demonstrate its
effectiveness on standard benchmarks and report extensive experimental
analysis. Without bells and whistles, FoveaBox achieves state-of-the-art single
model performance on the standard COCO and Pascal VOC object detection
benchmark. More importantly, FoveaBox avoids all computation and
hyper-parameters related to anchor boxes, which are often sensitive to the
final detection performance. We believe the simple and effective approach will
serve as a solid baseline and help ease future research for object detection.
The code has been made publicly available at
https://github.com/taokong/FoveaBox .Comment: IEEE Transactions on Image Processing, code at:
https://github.com/taokong/FoveaBo
Quantum state transmission via a spin ladder as a robust data bus
We explore the physical mechanism to coherently transfer the quantum
information of spin by connecting two spins to an isotropic antiferromagnetic
spin ladder system as data bus. Due to a large spin gap existing in such a
perfect medium, the effective Hamiltonian of the two connected spins can be
archived as that of Heisenberg type, which possesses a ground state with
maximal entanglement. We show that the effective coupling strength is inversely
proportional to the distance of the two spins and thus the quantum information
can be transferred between the two spins separated by a longer distance, i.e.
the characteristic time of quantum state transferring linearly depends on the
distance.Comment: 7 pages, 5 figures, 1 tabl
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