24,631 research outputs found
Recovering information of tunneling spectrum from Weakly Isolated Horizon
In this paper we investigate the properties of tunneling spectrum from weakly
isolated horizon(WIH). We find that there are correlations among Hawking
radiations from weakly isolated horizon, the information can be carried out in
terms of correlations between sequential emissions, and the radiation is an
entropy conservation process. We generalize Refs.[11-13]' results to a more
general spacetime. Through revisiting the calculation of tunneling spectrum of
weakly isolated horizon, we find that Ref.[12]'s requirement that radiating
particles have the same angular momenta of unit mass as that of black hole is
not needed, and the energy and angular momenta of emitting particles are very
arbitrary which should be restricted only by keeping the cosmic censorship of
black hole
Interaction-aware Factorization Machines for Recommender Systems
Factorization Machine (FM) is a widely used supervised learning approach by
effectively modeling of feature interactions. Despite the successful
application of FM and its many deep learning variants, treating every feature
interaction fairly may degrade the performance. For example, the interactions
of a useless feature may introduce noises; the importance of a feature may also
differ when interacting with different features. In this work, we propose a
novel model named \emph{Interaction-aware Factorization Machine} (IFM) by
introducing Interaction-Aware Mechanism (IAM), which comprises the
\emph{feature aspect} and the \emph{field aspect}, to learn flexible
interactions on two levels. The feature aspect learns feature interaction
importance via an attention network while the field aspect learns the feature
interaction effect as a parametric similarity of the feature interaction vector
and the corresponding field interaction prototype. IFM introduces more
structured control and learns feature interaction importance in a stratified
manner, which allows for more leverage in tweaking the interactions on both
feature-wise and field-wise levels. Besides, we give a more generalized
architecture and propose Interaction-aware Neural Network (INN) and DeepIFM to
capture higher-order interactions. To further improve both the performance and
efficiency of IFM, a sampling scheme is developed to select interactions based
on the field aspect importance. The experimental results from two well-known
datasets show the superiority of the proposed models over the state-of-the-art
methods
Reliable Energy-Efficient Routing Algorithm for Vehicle-Assisted Wireless Ad-Hoc Networks
We investigate the design of the optimal routing path in a moving vehicles
involved the internet of things (IoT). In our model, jammers exist that may
interfere with the information exchange between wireless nodes, leading to
worsened quality of service (QoS) in communications. In addition, the transmit
power of each battery-equipped node is constrained to save energy. We propose a
three-step optimal routing path algorithm for reliable and energy-efficient
communications. Moreover, results show that with the assistance of moving
vehicles, the total energy consumed can be reduced to a large extend. We also
study the impact on the optimal routing path design and energy consumption
which is caused by path loss, maximum transmit power constrain, QoS
requirement, etc.Comment: 6 pages, 5 figures, rejected by IEEE Globecom 2017,resubmit to IEEE
WCNC 201
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