287,167 research outputs found
Faster computation of the Tate pairing
This paper proposes new explicit formulas for the doubling and addition step
in Miller's algorithm to compute the Tate pairing. For Edwards curves the
formulas come from a new way of seeing the arithmetic. We state the first
geometric interpretation of the group law on Edwards curves by presenting the
functions which arise in the addition and doubling. Computing the coefficients
of the functions and the sum or double of the points is faster than with all
previously proposed formulas for pairings on Edwards curves. They are even
competitive with all published formulas for pairing computation on Weierstrass
curves. We also speed up pairing computation on Weierstrass curves in Jacobian
coordinates. Finally, we present several examples of pairing-friendly Edwards
curves.Comment: 15 pages, 2 figures. Final version accepted for publication in
Journal of Number Theor
Quantum cryptography: key distribution and beyond
Uniquely among the sciences, quantum cryptography has driven both
foundational research as well as practical real-life applications. We review
the progress of quantum cryptography in the last decade, covering quantum key
distribution and other applications.Comment: It's a review on quantum cryptography and it is not restricted to QK
Appearance-and-Relation Networks for Video Classification
Spatiotemporal feature learning in videos is a fundamental problem in
computer vision. This paper presents a new architecture, termed as
Appearance-and-Relation Network (ARTNet), to learn video representation in an
end-to-end manner. ARTNets are constructed by stacking multiple generic
building blocks, called as SMART, whose goal is to simultaneously model
appearance and relation from RGB input in a separate and explicit manner.
Specifically, SMART blocks decouple the spatiotemporal learning module into an
appearance branch for spatial modeling and a relation branch for temporal
modeling. The appearance branch is implemented based on the linear combination
of pixels or filter responses in each frame, while the relation branch is
designed based on the multiplicative interactions between pixels or filter
responses across multiple frames. We perform experiments on three action
recognition benchmarks: Kinetics, UCF101, and HMDB51, demonstrating that SMART
blocks obtain an evident improvement over 3D convolutions for spatiotemporal
feature learning. Under the same training setting, ARTNets achieve superior
performance on these three datasets to the existing state-of-the-art methods.Comment: CVPR18 camera-ready version. Code & models available at
https://github.com/wanglimin/ARTNe
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