6,445 research outputs found
Double-Edge Factor Graphs: Definition, Properties, and Examples
Some of the most interesting quantities associated with a factor graph are
its marginals and its partition sum. For factor graphs \emph{without cycles}
and moderate message update complexities, the sum-product algorithm (SPA) can
be used to efficiently compute these quantities exactly. Moreover, for various
classes of factor graphs \emph{with cycles}, the SPA has been successfully
applied to efficiently compute good approximations to these quantities. Note
that in the case of factor graphs with cycles, the local functions are usually
non-negative real-valued functions. In this paper we introduce a class of
factor graphs, called double-edge factor graphs (DE-FGs), which allow local
functions to be complex-valued and only require them, in some suitable sense,
to be positive semi-definite. We discuss various properties of the SPA when
running it on DE-FGs and we show promising numerical results for various
example DE-FGs, some of which have connections to quantum information
processing.Comment: Submitte
Estimating the Information Rate of a Channel with Classical Input and Output and a Quantum State (Extended Version)
We consider the problem of transmitting classical information over a
time-invariant channel with memory. A popular class of time-invariant channels
with memory are finite-state-machine channels, where a \emph{classical} state
evolves over time and governs the relationship between the classical input and
the classical output of the channel. For such channels, various techniques have
been developed for estimating and bounding the information rate. In this paper
we consider a class of time-invariant channels where a \emph{quantum} state
evolves over time and governs the relationship between the classical input and
the classical output of the channel. We propose algorithms for estimating and
bounding the information rate of such channels. In particular, we discuss
suitable graphical models for doing the relevant computations.Comment: This is an extended version of a paper that appears in Proc. 2017
IEEE International Symposium on Information Theory, Aachen, Germany, June
201
Bounding and Estimating the Classical Information Rate of Quantum Channels with Memory
We consider the scenario of classical communication over a finite-dimensional
quantum channel with memory using a separable-state input ensemble and local
output measurements. We propose algorithms for estimating the information rate
of such communication setups, along with algorithms for bounding the
information rate based on so-called auxiliary channels. Some of the algorithms
are extensions of their counterparts for (classical) finite-state-machine
channels. Notably, we discuss suitable graphical models for doing the relevant
computations. Moreover, the auxiliary channels are learned in a data-driven
approach; i.e., only input/output sequences of the true channel are needed, but
not the channel model of the true channel.Comment: This work has been submitted to the IEEE Transactions on Information
Theory for possible publication. Copyright may be transferred without notice,
after which this version may no longer be accessibl
Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection
Multispectral pedestrian detection has received extensive attention in recent
years as a promising solution to facilitate robust human target detection for
around-the-clock applications (e.g. security surveillance and autonomous
driving). In this paper, we demonstrate illumination information encoded in
multispectral images can be utilized to significantly boost performance of
pedestrian detection. A novel illumination-aware weighting mechanism is present
to accurately depict illumination condition of a scene. Such illumination
information is incorporated into two-stream deep convolutional neural networks
to learn multispectral human-related features under different illumination
conditions (daytime and nighttime). Moreover, we utilized illumination
information together with multispectral data to generate more accurate semantic
segmentation which are used to boost pedestrian detection accuracy. Putting all
of the pieces together, we present a powerful framework for multispectral
pedestrian detection based on multi-task learning of illumination-aware
pedestrian detection and semantic segmentation. Our proposed method is trained
end-to-end using a well-designed multi-task loss function and outperforms
state-of-the-art approaches on KAIST multispectral pedestrian dataset
Coherent quantum transport in disordered systems I: The influence of dephasing on the transport properties and absorption spectra on one-dimensional systems
Excitonic transport in static disordered one dimensional systems is studied
in the presence of thermal fluctuations that are described by the
Haken-Strobl-Reineker model. For short times, non-diffusive behavior is
observed that can be characterized as the free-particle dynamics in the
Anderson localized system. Over longer time scales, the environment-induced
dephasing is sufficient to overcome the Anderson localization caused by the
disorder and allow for transport to occur which is always seen to be diffusive.
In the limiting regimes of weak and strong dephasing quantum master equations
are developed, and their respective scaling relations imply the existence of a
maximum in the diffusion constant as a function of the dephasing rate that is
confirmed numerically. In the weak dephasing regime, it is demonstrated that
the diffusion constant is proportional to the square of the localization length
which leads to a significant enhancement of the transport rate over the
classical prediction. Finally, the influence of noise and disorder on the
absorption spectrum is presented and its relationship to the transport
properties is discussed.Comment: 23 pages, 7 figure
Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection
Effective fusion of complementary information captured by multi-modal sensors
(visible and infrared cameras) enables robust pedestrian detection under
various surveillance situations (e.g. daytime and nighttime). In this paper, we
present a novel box-level segmentation supervised learning framework for
accurate and real-time multispectral pedestrian detection by incorporating
features extracted in visible and infrared channels. Specifically, our method
takes pairs of aligned visible and infrared images with easily obtained
bounding box annotations as input and estimates accurate prediction maps to
highlight the existence of pedestrians. It offers two major advantages over the
existing anchor box based multispectral detection methods. Firstly, it
overcomes the hyperparameter setting problem occurred during the training phase
of anchor box based detectors and can obtain more accurate detection results,
especially for small and occluded pedestrian instances. Secondly, it is capable
of generating accurate detection results using small-size input images, leading
to improvement of computational efficiency for real-time autonomous driving
applications. Experimental results on KAIST multispectral dataset show that our
proposed method outperforms state-of-the-art approaches in terms of both
accuracy and speed
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