17,600 research outputs found
Propagation of singularities for weak KAM solutions and barrier functions
This paper studies the structure of the singular set (points of
nondifferentiability) of viscosity solutions to Hamilton-Jacobi equations
associated with general mechanical systems on the n-torus. First, using the
level set method, we characterize the propagation of singularities along
generalized characteristics. Then, we obtain a local propagation result for
singularities of weak KAM solutions in the supercritical case. Finally, we
apply such a result to study the propagation of singularities for barrier
functions
Automatic Translating Between Ancient Chinese and Contemporary Chinese with Limited Aligned Corpora
The Chinese language has evolved a lot during the long-term development.
Therefore, native speakers now have trouble in reading sentences written in
ancient Chinese. In this paper, we propose to build an end-to-end neural model
to automatically translate between ancient and contemporary Chinese. However,
the existing ancient-contemporary Chinese parallel corpora are not aligned at
the sentence level and sentence-aligned corpora are limited, which makes it
difficult to train the model. To build the sentence level parallel training
data for the model, we propose an unsupervised algorithm that constructs
sentence-aligned ancient-contemporary pairs by using the fact that the aligned
sentence pair shares many of the tokens. Based on the aligned corpus, we
propose an end-to-end neural model with copying mechanism and local attention
to translate between ancient and contemporary Chinese. Experiments show that
the proposed unsupervised algorithm achieves 99.4% F1 score for sentence
alignment, and the translation model achieves 26.95 BLEU from ancient to
contemporary, and 36.34 BLEU from contemporary to ancient.Comment: Acceptted by NLPCC 201
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
Although the performance of person Re-Identification (ReID) has been
significantly boosted, many challenging issues in real scenarios have not been
fully investigated, e.g., the complex scenes and lighting variations, viewpoint
and pose changes, and the large number of identities in a camera network. To
facilitate the research towards conquering those issues, this paper contributes
a new dataset called MSMT17 with many important features, e.g., 1) the raw
videos are taken by an 15-camera network deployed in both indoor and outdoor
scenes, 2) the videos cover a long period of time and present complex lighting
variations, and 3) it contains currently the largest number of annotated
identities, i.e., 4,101 identities and 126,441 bounding boxes. We also observe
that, domain gap commonly exists between datasets, which essentially causes
severe performance drop when training and testing on different datasets. This
results in that available training data cannot be effectively leveraged for new
testing domains. To relieve the expensive costs of annotating new training
samples, we propose a Person Transfer Generative Adversarial Network (PTGAN) to
bridge the domain gap. Comprehensive experiments show that the domain gap could
be substantially narrowed-down by the PTGAN.Comment: 10 pages, 9 figures; accepted in CVPR 201
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