60,366 research outputs found
Integrated optical devices based on broadband epsilon-near-zero meta-atoms
We verify the feasibility of the proposed theoretical strategy for designing
the broadband near-zero permittivity (ENZ) metamaterial at optical frequency
range with numerical simulations. In addition, the designed broadband ENZ stack
are used as meta-atoms to build functional nanophotonic devices with
extraordinary properties, including an ultranarrow electromagnetic energy
tunneling channel and an ENZ concave focusing lens.Comment: 3 pages, 3 figure
Euclidean Dynamical Symmetry in Nuclear Shape Phase Transitions
The Euclidean dynamical symmetry hidden in the critical region of nuclear
shape phase transitions is revealed by a novel algebraic F(5) description. With
a nonlinear projection, it is shown that the dynamics in the critical region of
the spherical--axial deformed and the spherical-- soft shape phase
transitions can indeed be manifested by this description, which thus provides a
unified symmetry--based interpretation of the critical phenomena in the region.Comment: 5 pages, 2 figures, 2 table
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
We present a compact but effective CNN model for optical flow, called
PWC-Net. PWC-Net has been designed according to simple and well-established
principles: pyramidal processing, warping, and the use of a cost volume. Cast
in a learnable feature pyramid, PWC-Net uses the cur- rent optical flow
estimate to warp the CNN features of the second image. It then uses the warped
features and features of the first image to construct a cost volume, which is
processed by a CNN to estimate the optical flow. PWC-Net is 17 times smaller in
size and easier to train than the recent FlowNet2 model. Moreover, it
outperforms all published optical flow methods on the MPI Sintel final pass and
KITTI 2015 benchmarks, running at about 35 fps on Sintel resolution (1024x436)
images. Our models are available on https://github.com/NVlabs/PWC-Net.Comment: CVPR 2018 camera ready version (with github link to Caffe and PyTorch
code
Collaborative Inference of Coexisting Information Diffusions
Recently, \textit{diffusion history inference} has become an emerging
research topic due to its great benefits for various applications, whose
purpose is to reconstruct the missing histories of information diffusion traces
according to incomplete observations. The existing methods, however, often
focus only on single information diffusion trace, while in a real-world social
network, there often coexist multiple information diffusions over the same
network. In this paper, we propose a novel approach called Collaborative
Inference Model (CIM) for the problem of the inference of coexisting
information diffusions. By exploiting the synergism between the coexisting
information diffusions, CIM holistically models multiple information diffusions
as a sparse 4th-order tensor called Coexisting Diffusions Tensor (CDT) without
any prior assumption of diffusion models, and collaboratively infers the
histories of the coexisting information diffusions via a low-rank approximation
of CDT with a fusion of heterogeneous constraints generated from additional
data sources. To improve the efficiency, we further propose an optimal
algorithm called Time Window based Parallel Decomposition Algorithm (TWPDA),
which can speed up the inference without compromise on the accuracy by
utilizing the temporal locality of information diffusions. The extensive
experiments conducted on real world datasets and synthetic datasets verify the
effectiveness and efficiency of CIM and TWPDA
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