10,430 research outputs found
Multiparty quantum secret sharing with pure entangled states and decoy photons
We present a scheme for multiparty quantum secret sharing of a private key
with pure entangled states and decoy photons. The boss, say Alice uses the
decoy photons, which are randomly in one of the four nonorthogonal
single-photon states, to prevent a potentially dishonest agent from
eavesdropping freely. This scheme requires the parties of communication to have
neither an ideal single-photon quantum source nor a maximally entangled one,
which makes this scheme more convenient than others in a practical application.
Moreover, it has the advantage of having high intrinsic efficiency for qubits
and exchanging less classical information in principle.Comment: 5 pages, no figure
PixelLink: Detecting Scene Text via Instance Segmentation
Most state-of-the-art scene text detection algorithms are deep learning based
methods that depend on bounding box regression and perform at least two kinds
of predictions: text/non-text classification and location regression.
Regression plays a key role in the acquisition of bounding boxes in these
methods, but it is not indispensable because text/non-text prediction can also
be considered as a kind of semantic segmentation that contains full location
information in itself. However, text instances in scene images often lie very
close to each other, making them very difficult to separate via semantic
segmentation. Therefore, instance segmentation is needed to address this
problem. In this paper, PixelLink, a novel scene text detection algorithm based
on instance segmentation, is proposed. Text instances are first segmented out
by linking pixels within the same instance together. Text bounding boxes are
then extracted directly from the segmentation result without location
regression. Experiments show that, compared with regression-based methods,
PixelLink can achieve better or comparable performance on several benchmarks,
while requiring many fewer training iterations and less training data.Comment: AAAI-201
PixelLink: Detecting Scene Text via Instance Segmentation
Most state-of-the-art scene text detection algorithms are deep learning based
methods that depend on bounding box regression and perform at least two kinds
of predictions: text/non-text classification and location regression.
Regression plays a key role in the acquisition of bounding boxes in these
methods, but it is not indispensable because text/non-text prediction can also
be considered as a kind of semantic segmentation that contains full location
information in itself. However, text instances in scene images often lie very
close to each other, making them very difficult to separate via semantic
segmentation. Therefore, instance segmentation is needed to address this
problem. In this paper, PixelLink, a novel scene text detection algorithm based
on instance segmentation, is proposed. Text instances are first segmented out
by linking pixels within the same instance together. Text bounding boxes are
then extracted directly from the segmentation result without location
regression. Experiments show that, compared with regression-based methods,
PixelLink can achieve better or comparable performance on several benchmarks,
while requiring many fewer training iterations and less training data.Comment: AAAI-201
The effects of overtaking strategy in the Nagel-Schreckenberg model
Based on the Nagel-Schreckenberg (NS) model with periodic boundary
conditions, we proposed the NSOS model by adding the overtaking strategy (OS).
In our model, overtaking vehicles are randomly selected with probability at
each time step, and the successful overtaking is determined by their
velocities. We observed that (i) traffic jams still occur in the NSOS model;
(ii) OS increases the traffic flow in the regime where the densities exceed the
maximum flow density. We also studied the phase transition (from free flow
phase to jammed phase) of the NSOS model by analyzing the overtaking success
rate, order parameter, relaxation time and correlation function, respectively.
It was shown that the NSOS model differs from the NS model mainly in the jammed
regime, and the influence of OS on the transition density is dominated by the
braking probability Comment: 9 pages, 20 figures, to be published in The European Physical Journal
B (EPJB
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