19,669 research outputs found
Investigating Properties of a Family of Quantum Renyi Divergences
Audenaert and Datta recently introduced a two-parameter family of relative
R\'{e}nyi entropies, known as the --relative R\'{e}nyi entropies.
The definition of the --relative R\'{e}nyi entropy unifies all
previously proposed definitions of the quantum R\'{e}nyi divergence of order
under a common framework. Here we will prove that the
--relative R\'{e}nyi entropies are a proper generalization of the
quantum relative entropy by computing the limit of the - divergence
as approaches one and is an arbitrary function of . We
also show that certain operationally relevant families of R\'enyi divergences
are differentiable at . Finally, our analysis reveals that the
derivative at evaluates to half the relative entropy variance, a
quantity that has attained operational significance in second-order quantum
hypothesis testing.Comment: 15 pages, v2: journal versio
Light Dark Matter: Models and Constraints
We study the direct detection prospects for a representative set of
simplified models of sub-GeV dark matter (DM), accounting for existing
terrestrial, astrophysical and cosmological constraints. We focus on dark
matter lighter than an MeV, where these constraints are most stringent, and
find three scenarios with accessible direct detection cross sections: (i) DM
interacting via an ultralight kinetically mixed dark photon, (ii) a DM
sub-component interacting with nucleons or electrons through a light scalar or
vector mediator, and (iii) DM coupled with nucleons via a mediator heavier than
~ 100 keV.Comment: 44 pages, 13 figures, reference added and minor updates to some of
the constraints, conclusions unchange
LHC limits on axion-like particles from heavy-ion collisions
In these proceedings we use recent LHC heavy-ion data to set a limit on
axion-like particles coupling to electromagnetism with mass in the range 10-100
GeV. We recast ATLAS data as per the strategy proposed in 1607.06083, and find
results in-line with the projections given there.Comment: 4 pages, 3 figures, conference proceeding for PHOTON201
Deep-LK for Efficient Adaptive Object Tracking
In this paper we present a new approach for efficient regression based object
tracking which we refer to as Deep- LK. Our approach is closely related to the
Generic Object Tracking Using Regression Networks (GOTURN) framework of Held et
al. We make the following contributions. First, we demonstrate that there is a
theoretical relationship between siamese regression networks like GOTURN and
the classical Inverse-Compositional Lucas & Kanade (IC-LK) algorithm. Further,
we demonstrate that unlike GOTURN IC-LK adapts its regressor to the appearance
of the currently tracked frame. We argue that this missing property in GOTURN
can be attributed to its poor performance on unseen objects and/or viewpoints.
Second, we propose a novel framework for object tracking - which we refer to as
Deep-LK - that is inspired by the IC-LK framework. Finally, we show impressive
results demonstrating that Deep-LK substantially outperforms GOTURN.
Additionally, we demonstrate comparable tracking performance to current state
of the art deep-trackers whilst being an order of magnitude (i.e. 100 FPS)
computationally efficient
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