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. Let

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 $N$-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 $N$-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 $t$ 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