2,001 research outputs found
Uncertainty Relation for a Quantum Open System
We derive the uncertainty relation for a quantum open system comprised of a
Brownian particle interacting with a bath of quantum oscillators at finite
temperature. We examine how the quantum and thermal fluctuations of the
environment contribute to the uncertainty in the canonical variables of the
system. We show that upon contact with the bath (assumed ohmic in this paper)
the system evolves from a quantum-dominated state to a thermal-dominated state
in a time which is the same as the decoherence time in similar models in the
discussion of quantum to classical transition. This offers some insight into
the physical mechanisms involved in the environment-induced decoherence
process. We obtain closed analytic expressions for this generalized uncertainty
relation under the conditions of high temperature and weak damping separately.
We also consider under these conditions an arbitrarily-squeezed initial state
and show how the squeeze parameter enters in the generalized uncertainty
relation. Using these results we examine the transition of the system from a
quantum pure state to a nonequilibrium quantum statistical state and to an
equilibrium quantum statistical state. The three stages are marked by the
decoherence time and the relaxation time respectively. With these observations
we explicate the physical conditions when the two basic postulates of quantum
statistical mechanics become valid. We also comment on the inappropriateness in
the usage of the word classicality in many decoherence studies of quantum to
classical transition.Comment: 36 pages,Tex,umdpp93-162,(submitted to Phys. Rev. A
SiamVGG: Visual Tracking using Deeper Siamese Networks
Recently, we have seen a rapid development of Deep Neural Network (DNN) based
visual tracking solutions. Some trackers combine the DNN-based solutions with
Discriminative Correlation Filters (DCF) to extract semantic features and
successfully deliver the state-of-the-art tracking accuracy. However, these
solutions are highly compute-intensive, which require long processing time,
resulting unsecured real-time performance. To deliver both high accuracy and
reliable real-time performance, we propose a novel tracker called SiamVGG. It
combines a Convolutional Neural Network (CNN) backbone and a cross-correlation
operator, and takes advantage of the features from exemplary images for more
accurate object tracking.
The architecture of SiamVGG is customized from VGG-16, with the parameters
shared by both exemplary images and desired input video frames.
We demonstrate the proposed SiamVGG on OTB-2013/50/100 and VOT 2015/2016/2017
datasets with the state-of-the-art accuracy while maintaining a decent
real-time performance of 50 FPS running on a GTX 1080Ti. Our design can achieve
2% higher Expected Average Overlap (EAO) compared to the ECO and C-COT in
VOT2017 Challenge
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