Homogeneity Pursuit in Single Index Models based Panel Data Analysis

Panel data analysis is an important topic in statistics and econometrics. Traditionally, in panel data analysis, all individuals are assumed to share the same unknown parameters, e.g. the same coefficients of covariates when the linear models are used, and the differences between the individuals are accounted for by cluster effects. This kind of modelling only makes sense if our main interest is on the global trend, this is because it would not be able to tell us anything about the individual attributes which are sometimes very important. In this paper, we proposed a modelling based on the single index models embedded with homogeneity for panel data analysis, which builds the individual attributes in the model and is parsimonious at the same time. We develop a data driven approach to identify the structure of homogeneity, and estimate the unknown parameters and functions based on the identified structure. Asymptotic properties of the resulting estimators are established. Intensive simulation studies conducted in this paper also show the resulting estimators work very well when sample size is finite. Finally, the proposed modelling is applied to a public financial dataset and a UK climate dataset, the results reveal some interesting findings.Comment: 46 pages, 2 figure

Towards Good Practices for Deep 3D Hand Pose Estimation

3D hand pose estimation from single depth image is an important and challenging problem for human-computer interaction. Recently deep convolutional networks (ConvNet) with sophisticated design have been employed to address it, but the improvement over traditional random forest based methods is not so apparent. To exploit the good practice and promote the performance for hand pose estimation, we propose a tree-structured Region Ensemble Network (REN) for directly 3D coordinate regression. It first partitions the last convolution outputs of ConvNet into several grid regions. The results from separate fully-connected (FC) regressors on each regions are then integrated by another FC layer to perform the estimation. By exploitation of several training strategies including data augmentation and smooth $L_1$ loss, proposed REN can significantly improve the performance of ConvNet to localize hand joints. The experimental results demonstrate that our approach achieves the best performance among state-of-the-art algorithms on three public hand pose datasets. We also experiment our methods on fingertip detection and human pose datasets and obtain state-of-the-art accuracy.Comment: Extended version of arXiv:1702.0244

Pose Guided Structured Region Ensemble Network for Cascaded Hand Pose Estimation

Hand pose estimation from a single depth image is an essential topic in computer vision and human computer interaction. Despite recent advancements in this area promoted by convolutional neural network, accurate hand pose estimation is still a challenging problem. In this paper we propose a Pose guided structured Region Ensemble Network (Pose-REN) to boost the performance of hand pose estimation. The proposed method extracts regions from the feature maps of convolutional neural network under the guide of an initially estimated pose, generating more optimal and representative features for hand pose estimation. The extracted feature regions are then integrated hierarchically according to the topology of hand joints by employing tree-structured fully connections. A refined estimation of hand pose is directly regressed by the proposed network and the final hand pose is obtained by utilizing an iterative cascaded method. Comprehensive experiments on public hand pose datasets demonstrate that our proposed method outperforms state-of-the-art algorithms.Comment: Accepted by Neurocomputin

Motion Feature Augmented Recurrent Neural Network for Skeleton-based Dynamic Hand Gesture Recognition

Dynamic hand gesture recognition has attracted increasing interests because of its importance for human computer interaction. In this paper, we propose a new motion feature augmented recurrent neural network for skeleton-based dynamic hand gesture recognition. Finger motion features are extracted to describe finger movements and global motion features are utilized to represent the global movement of hand skeleton. These motion features are then fed into a bidirectional recurrent neural network (RNN) along with the skeleton sequence, which can augment the motion features for RNN and improve the classification performance. Experiments demonstrate that our proposed method is effective and outperforms start-of-the-art methods.Comment: Accepted by ICIP 201

Exploring the convective core of the hybrid δ\delta Scuti-γ\gamma Doradus star CoRoT 100866999 with asteroseismology

We computed a grid of theoretical models to fit the $\delta$ Scuti frequencies of CoRoT 100866999 detected earlier from the CoRoT timeserials. The pulsating primary star is determined to be a main sequence star with a rotation period of $4.1^{+0.6}_{-0.5}$ days, rotating slower than the orbital motion. The fundamental parameters of the primary star are determined to be $M$ = $1.71^{+0.13}_{-0.04}$ $M_{\odot}$, $Z=0.012^{+0.004}_{-0.000}$, $f_{\rm ov}$ = $0.02^{+0.00}_{-0.02}$, $T_{\rm eff}$ = $8024^{+249}_{-297}$ K, $L$ = $11.898^{+2.156}_{-1.847}$ $L_{\odot}$, $\log g$ = $4.166^{+0.013}_{-0.002}$, $R$ = $1.787^{+0.040}_{-0.016}$ $R_{\odot}$, and $X_{\rm c}$ = 0.488$^{+0.011}_{-0.020}$, matching well those obtained from the eclipsing light curve analysis. Based on the model fittings, $p_1$ and $p_5$ are suggested to be two dipole modes, and $p_3$, $p_4$, $p_6$, and $p_7$ to be four quadrupole modes. In particular, $p_4$ and $p_7$ are identified as two components of one quintuplet. Based on the best-fitting model, we find that $p_1$ is a g mode and the other nonradial modes have pronounced mixed characters, which give strong constraints on the convective core. Finally, the relative size of the convective core of CoRoT 100866999 is determined to $R_{\rm conv}/R$ = $0.0931^{+0.0003}_{-0.0013}$.Comment: 7 figures and 6 tables. accepted by Ap

Look More Than Once: An Accurate Detector for Text of Arbitrary Shapes

Previous scene text detection methods have progressed substantially over the past years. However, limited by the receptive field of CNNs and the simple representations like rectangle bounding box or quadrangle adopted to describe text, previous methods may fall short when dealing with more challenging text instances, such as extremely long text and arbitrarily shaped text. To address these two problems, we present a novel text detector namely LOMO, which localizes the text progressively for multiple times (or in other word, LOok More than Once). LOMO consists of a direct regressor (DR), an iterative refinement module (IRM) and a shape expression module (SEM). At first, text proposals in the form of quadrangle are generated by DR branch. Next, IRM progressively perceives the entire long text by iterative refinement based on the extracted feature blocks of preliminary proposals. Finally, a SEM is introduced to reconstruct more precise representation of irregular text by considering the geometry properties of text instance, including text region, text center line and border offsets. The state-of-the-art results on several public benchmarks including ICDAR2017-RCTW, SCUT-CTW1500, Total-Text, ICDAR2015 and ICDAR17-MLT confirm the striking robustness and effectiveness of LOMO.Comment: Accepted by CVPR1

Bayesian Nonparametric Dictionary Learning for Compressed Sensing MRI

We develop a Bayesian nonparametric model for reconstructing magnetic resonance images (MRI) from highly undersampled k-space data. We perform dictionary learning as part of the image reconstruction process. To this end, we use the beta process as a nonparametric dictionary learning prior for representing an image patch as a sparse combination of dictionary elements. The size of the dictionary and the patch-specific sparsity pattern are inferred from the data, in addition to other dictionary learning variables. Dictionary learning is performed directly on the compressed image, and so is tailored to the MRI being considered. In addition, we investigate a total variation penalty term in combination with the dictionary learning model, and show how the denoising property of dictionary learning removes dependence on regularization parameters in the noisy setting. We derive a stochastic optimization algorithm based on Markov Chain Monte Carlo (MCMC) for the Bayesian model, and use the alternating direction method of multipliers (ADMM) for efficiently performing total variation minimization. We present empirical results on several MRI, which show that the proposed regularization framework can improve reconstruction accuracy over other methods

CYCLADES: Conflict-free Asynchronous Machine Learning

We present CYCLADES, a general framework for parallelizing stochastic optimization algorithms in a shared memory setting. CYCLADES is asynchronous during shared model updates, and requires no memory locking mechanisms, similar to HOGWILD!-type algorithms. Unlike HOGWILD!, CYCLADES introduces no conflicts during the parallel execution, and offers a black-box analysis for provable speedups across a large family of algorithms. Due to its inherent conflict-free nature and cache locality, our multi-core implementation of CYCLADES consistently outperforms HOGWILD!-type algorithms on sufficiently sparse datasets, leading to up to 40% speedup gains compared to the HOGWILD! implementation of SGD, and up to 5x gains over asynchronous implementations of variance reduction algorithms

HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens

Neural Architecture Search (NAS) refers to automatically design the architecture. We propose an hourglass-inspired approach (HourNAS) for this problem that is motivated by the fact that the effects of the architecture often proceed from the vital few blocks. Acting like the narrow neck of an hourglass, vital blocks in the guaranteed path from the input to the output of a deep neural network restrict the information flow and influence the network accuracy. The other blocks occupy the major volume of the network and determine the overall network complexity, corresponding to the bulbs of an hourglass. To achieve an extremely fast NAS while preserving the high accuracy, we propose to identify the vital blocks and make them the priority in the architecture search. The search space of those non-vital blocks is further shrunk to only cover the candidates that are affordable under the computational resource constraints. Experimental results on the ImageNet show that only using 3 hours (0.1 days) with one GPU, our HourNAS can search an architecture that achieves a 77.0% Top-1 accuracy, which outperforms the state-of-the-art methods

Coverless Video Steganography based on Maximum DC Coefficients

Coverless steganography has been a great interest in recent years, since it is a technology that can absolutely resist the detection of steganalysis by not modifying the carriers. However, most existing coverless steganography algorithms select images as carriers, and few studies are reported on coverless video steganography. In fact, video is a securer and more informative carrier. In this paper, a novel coverless video steganography algorithm based on maximum Direct Current (DC) coefficients is proposed. Firstly, a Gaussian distribution model of DC coefficients considering video coding process is built, which indicates that the distribution of changes for maximum DC coefficients in a block is more stable than the adjacent DC coefficients. Then, a novel hash sequence generation method based on the maximum DC coefficients is proposed. After that, the video index structure is established to speed up the efficiency of searching videos. In the process of information hiding, the secret information is converted into binary segments, and the video whose hash sequence equals to secret information segment is selected as the carrier according to the video index structure. Finally, all of the selected videos and auxiliary information are sent to the receiver. Especially, the subjective security of video carriers, the cost of auxiliary information and the robustness to video compression are considered for the first time in this paper. Experimental results and analysis show that the proposed algorithm performs better in terms of capacity, robustness, and security, compared with the state-of-the-art coverless steganography algorithms