268 research outputs found
Solving Set Cover with Pairs Problem using Quantum Annealing
Here we consider using quantum annealing to solve Set Cover with Pairs (SCP), an NP-hard combinatorial optimization problem that plays an important role in networking, computational biology, and biochemistry. We show an explicit construction of Ising Hamiltonians whose ground states encode the solution of SCP instances. We numerically simulate the time-dependent Schrödinger equation in order to test the performance of quantum annealing for random instances and compare with that of simulated annealing. We also discuss explicit embedding strategies for realizing our Hamiltonian construction on the D-wave type restricted Ising Hamiltonian based on Chimera graphs. Our embedding on the Chimera graph preserves the structure of the original SCP instance and in particular, the embedding for general complete bipartite graphs and logical disjunctions may be of broader use than that the specific problem we deal with
CNN-based Real-time Dense Face Reconstruction with Inverse-rendered Photo-realistic Face Images
With the powerfulness of convolution neural networks (CNN), CNN based face
reconstruction has recently shown promising performance in reconstructing
detailed face shape from 2D face images. The success of CNN-based methods
relies on a large number of labeled data. The state-of-the-art synthesizes such
data using a coarse morphable face model, which however has difficulty to
generate detailed photo-realistic images of faces (with wrinkles). This paper
presents a novel face data generation method. Specifically, we render a large
number of photo-realistic face images with different attributes based on
inverse rendering. Furthermore, we construct a fine-detailed face image dataset
by transferring different scales of details from one image to another. We also
construct a large number of video-type adjacent frame pairs by simulating the
distribution of real video data. With these nicely constructed datasets, we
propose a coarse-to-fine learning framework consisting of three convolutional
networks. The networks are trained for real-time detailed 3D face
reconstruction from monocular video as well as from a single image. Extensive
experimental results demonstrate that our framework can produce high-quality
reconstruction but with much less computation time compared to the
state-of-the-art. Moreover, our method is robust to pose, expression and
lighting due to the diversity of data.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine
Intelligence, 201
Algorithmic Regularization in Model-free Overparametrized Asymmetric Matrix Factorization
We study the asymmetric matrix factorization problem under a natural
nonconvex formulation with arbitrary overparametrization. The model-free
setting is considered, with minimal assumption on the rank or singular values
of the observed matrix, where the global optima provably overfit. We show that
vanilla gradient descent with small random initialization sequentially recovers
the principal components of the observed matrix. Consequently, when equipped
with proper early stopping, gradient descent produces the best low-rank
approximation of the observed matrix without explicit regularization. We
provide a sharp characterization of the relationship between the approximation
error, iteration complexity, initialization size and stepsize. Our complexity
bound is almost dimension-free and depends logarithmically on the approximation
error, with significantly more lenient requirements on the stepsize and
initialization compared to prior work. Our theoretical results provide accurate
prediction for the behavior gradient descent, showing good agreement with
numerical experiments.Comment: 30 pages, 7 figure
SoccerDB: A Large-Scale Database for Comprehensive Video Understanding
Soccer videos can serve as a perfect research object for video understanding
because soccer games are played under well-defined rules while complex and
intriguing enough for researchers to study. In this paper, we propose a new
soccer video database named SoccerDB, comprising 171,191 video segments from
346 high-quality soccer games. The database contains 702,096 bounding boxes,
37,709 essential event labels with time boundary and 17,115 highlight
annotations for object detection, action recognition, temporal action
localization, and highlight detection tasks. To our knowledge, it is the
largest database for comprehensive sports video understanding on various
aspects. We further survey a collection of strong baselines on SoccerDB, which
have demonstrated state-of-the-art performances on independent tasks. Our
evaluation suggests that we can benefit significantly when jointly considering
the inner correlations among those tasks. We believe the release of SoccerDB
will tremendously advance researches around comprehensive video understanding.
{\itshape Our dataset and code published on
https://github.com/newsdata/SoccerDB.}Comment: accepted by MM2020 sports worksho
Topological dissipative Kerr soliton combs in a valley photonic crystal resonator
Topological phases have become an enabling role in exploiting new
applications of nonlinear optics in recent years. Here we theoretically propose
a valley photonic crystal resonator emulating topologically protected
dissipative Kerr soliton combs. It is shown that topological resonator modes
can be observed in the resonator. Moreover, we also simulate the dynamic
evolution of the topological resonator with the injection of a continuous-wave
pump laser. We find that the topological optical frequency combs evolve from
Turing rolls to chaotic states, and eventually into single soliton states. More
importantly, such dissipative Kerr soliton combs generated in the resonator are
inborn topologically protected, showing robustness against sharp bends and
structural disorders. Our design supporting topologically protected dissipative
Kerr soliton combs could be implemented experimentally in on-chip
nanofabricated photonic devices.Comment: 16 pages, 12 figure
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