4,078 research outputs found
Densely tracking sequences of 3D face scans
3D face dense tracking aims to find dense inter-frame correspondences in a
sequence of 3D face scans and constitutes a powerful tool for many face
analysis tasks, e.g., 3D dynamic facial expression analysis. The majority of
the existing methods just fit a 3D face surface or model to a 3D target surface
without considering temporal information between frames. In this paper, we
propose a novel method for densely tracking sequences of 3D face scans, which
ex- tends the non-rigid ICP algorithm by adding a novel specific criterion for
temporal information. A novel fitting framework is presented for automatically
tracking a full sequence of 3D face scans. The results of experiments carried
out on the BU4D-FE database are promising, showing that the proposed algorithm
outperforms state-of-the-art algorithms for 3D face dense tracking.Comment: 8 page
Super-Tonks-Girardeau gas of spin-1/2 interacting fermions
Fermi gases confined in tight one-dimensional waveguides form two-particle
bound states of atoms in the presence of a strongly attractive interaction.
Based on the exact solution of the one-dimensional spin-1/2 interacting Fermi
gas, we demonstrate that a stable excited state with no pairing between
attractive fermionic atoms can be realized by a sudden switch of interaction
from strongly repulsive regime to the strongly attractive regime. Such a state
is an exact fermionic analog of the experimentally observed
super-Tonks-Girardeau state of bosonic Cesium atoms [Science 325, 1224 (2009)]
and should be possible to be observed by the experiment. The frequency of
lowest breathing mode of the fermionic super-Tonks-Girardeau gas is calculated
as a function of the interaction strength, which could be used as a detectable
signature for the experimental observation.Comment: 4.1 pages, 5 figures, version accepted for publication in Phys. Rev.
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Trajectory Characters of Rogue Waves
We present a simple representation for arbitrary-order rogue wave solution
and study on the trajectories of them explicitly. We find that the global
trajectories on temporal-spatial distribution all look like "X" shape for rogue
waves. Short-time prediction on rogue wave can be done through measuring the
information contained in the initial perturbation twice.Comment: Research paper, 6 pages, 6 figure
Modulational instability and homoclinic orbit solutions in vector nonlinear Schr\"odinger equation
Modulational instability has been used to explain the formation of breather
and rogue waves qualitatively. In this paper, we show modulational instability
can be used to explain the structure of them in a quantitative way. We develop
a method to derive general forms for Akhmediev breather and rogue wave
solutions in a -component nonlinear Schr\"odinger equations. The existence
condition for each pattern is clarified clearly. Moreover, the general
multi-high-order rogue wave solutions and multi-Akhmediev breather solutions
for -component nonlinear Schr\"odinger equations are constructed. The
results further deepen our understanding on the quantitative relations between
modulational instability and homoclinic orbits solutions.Comment: 30 page
Quantitative Relation between Modulational Instability and Several Well-known Nonlinear Excitations
We study on the relations between modulational instability and several
well-known nonlinear excitations in a nonlinear fiber, such as bright soliton,
nonlinear continuous wave, Akhmediev breather, Peregrine rogue wave, and
Kuznetsov-Ma breather. We present a quantitative correspondence between them
based on the dominant frequency and propagation constant of each perturbation
on a continuous wave background. Especially, we find rogue wave comes from
modulational instability under the "resonance" perturbation with continuous
wave background. These results will deepen our understanding on rogue wave
excitation and could be helpful for controllable nonlinear wave excitations in
nonlinear fiber and other nonlinear systems.Comment: 5 pages, 1 figur
Automatic video scene segmentation based on spatial-temporal clues and rhythm
With ever increasing computing power and data storage capacity, the potential
for large digital video libraries is growing rapidly.However, the massive use
of video for the moment is limited by its opaque characteristics. Indeed, a
user who has to handle and retrieve sequentially needs too much time in order
to find out segments of interest within a video. Therefore, providing an
environment both convenient and efficient for video storing and retrieval,
especially for content-based searching as this exists in traditional textbased
database systems, has been the focus of recent and important efforts of a large
research community
In this paper, we propose a new automatic video scene segmentation method
that explores two main video features; these are spatial-temporal relationship
and rhythm of shots. The experimental evidence we obtained from a 80
minutevideo showed that our prototype provides very high accuracy for video
segmentation.Comment: 25 pages, 12 figure
Optimal Transport for Deep Joint Transfer Learning
Training a Deep Neural Network (DNN) from scratch requires a large amount of
labeled data. For a classification task where only small amount of training
data is available, a common solution is to perform fine-tuning on a DNN which
is pre-trained with related source data. This consecutive training process is
time consuming and does not consider explicitly the relatedness between
different source and target tasks.
In this paper, we propose a novel method to jointly fine-tune a Deep Neural
Network with source data and target data. By adding an Optimal Transport loss
(OT loss) between source and target classifier predictions as a constraint on
the source classifier, the proposed Joint Transfer Learning Network (JTLN) can
effectively learn useful knowledge for target classification from source data.
Furthermore, by using different kind of metric as cost matrix for the OT loss,
JTLN can incorporate different prior knowledge about the relatedness between
target categories and source categories.
We carried out experiments with JTLN based on Alexnet on image classification
datasets and the results verify the effectiveness of the proposed JTLN in
comparison with standard consecutive fine-tuning. This Joint Transfer Learning
with OT loss is general and can also be applied to other kind of Neural
Networks
A Markov Chain-Based Numerical Method for Calculating Network Degree Distributions
This paper establishes a relation between scale-free networks and Markov
chains, and proposes a computation framework for degree distributions of
scale-free networks. We first find that, under the BA model, the degree
evolution of individual nodes in a scale-free network follows some
non-homogeneous Markov chains. Exploring the special structure of these Markov
chains, we are able to develop an efficient algorithm to compute the degree
distribution numerically. The complexity of our algorithm is O(t^2), where
is the number of time steps for adding new nodes. We use three examples to
demonstrate the computation procedure and compare the results with those from
the existing methods.Comment: 11 pages, 3 figures, and 5 table
Strongly interacting Bose-Fermi mixtures in one dimension
We study one-dimensional strongly interacting Bose-Fermi mixtures by both the
exact Bethe-ansatz method and variational perturbation theory within the
degenerate ground state subspace of the system in the infinitely repulsive
limit. Based on the exact solution of the one-dimensional Bose-Fermi gas with
equal boson-boson and boson-fermion interaction strengths, we demonstrate that
the ground state energy is degenerate for different Bose-Fermi configurations
and the degeneracy is lifted when the interaction deviates the infinitely
interacting limit. We then show that the ground properties in the strongly
interacting regime can be well characterized by using the variational
perturbation method within the degenerate ground state subspace, which can be
applied to deal with more general cases with anisotropic interactions and in
external traps. Our results indicate that the total ground-state density
profile in the strongly repulsive regime behaves like the polarized
noninteracting fermions, whereas the density distributions of bosons and
fermions display different properties for different Bose-Fermi configurations
and are sensitive to the anisotropy of interactions.Comment: 11 pages, 3 figures, Version published in "Focus on Strongly
Interacting Quantum Gases in One Dimension" (NJP, IOP) dedicated to Marvin D.
Girardeau (1930-2015
Jacquard: A Large Scale Dataset for Robotic Grasp Detection
Grasping skill is a major ability that a wide number of real-life
applications require for robotisation. State-of-the-art robotic grasping
methods perform prediction of object grasp locations based on deep neural
networks. However, such networks require huge amount of labeled data for
training making this approach often impracticable in robotics. In this paper,
we propose a method to generate a large scale synthetic dataset with ground
truth, which we refer to as the Jacquard grasping dataset. Jacquard is built on
a subset of ShapeNet, a large CAD models dataset, and contains both RGB-D
images and annotations of successful grasping positions based on grasp attempts
performed in a simulated environment. We carried out experiments using an
off-the-shelf CNN, with three different evaluation metrics, including real
grasping robot trials. The results show that Jacquard enables much better
generalization skills than a human labeled dataset thanks to its diversity of
objects and grasping positions. For the purpose of reproducible research in
robotics, we are releasing along with the Jacquard dataset a web interface for
researchers to evaluate the successfulness of their grasping position
detections using our dataset
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