Testing Independence of Bivariate Censored Data using Random Walk on Restricted Permutation Graph

In this paper, we propose a procedure to test the independence of bivariate censored data, which is generic and applicable to any censoring types in the literature. To test the hypothesis, we consider a rank-based statistic, Kendall's tau statistic. The censored data defines a restricted permutation space of all possible ranks of the observations. We propose the statistic, the average of Kendall's tau over the ranks in the restricted permutation space. To evaluate the statistic and its reference distribution, we develop a Markov chain Monte Carlo (MCMC) procedure to obtain uniform samples on the restricted permutation space and numerically approximate the null distribution of the averaged Kendall's tau. We apply the procedure to three real data examples with different censoring types, and compare the results with those by existing methods. We conclude the paper with some additional discussions not given in the main body of the paper

Self-Supervised Video Representation Learning with Space-Time Cubic Puzzles

Self-supervised tasks such as colorization, inpainting and zigsaw puzzle have been utilized for visual representation learning for still images, when the number of labeled images is limited or absent at all. Recently, this worthwhile stream of study extends to video domain where the cost of human labeling is even more expensive. However, the most of existing methods are still based on 2D CNN architectures that can not directly capture spatio-temporal information for video applications. In this paper, we introduce a new self-supervised task called as \textit{Space-Time Cubic Puzzles} to train 3D CNNs using large scale video dataset. This task requires a network to arrange permuted 3D spatio-temporal crops. By completing \textit{Space-Time Cubic Puzzles}, the network learns both spatial appearance and temporal relation of video frames, which is our final goal. In experiments, we demonstrate that our learned 3D representation is well transferred to action recognition tasks, and outperforms state-of-the-art 2D CNN-based competitors on UCF101 and HMDB51 datasets.Comment: Accepted to AAAI 201

Weakly- and Self-Supervised Learning for Content-Aware Deep Image Retargeting

This paper proposes a weakly- and self-supervised deep convolutional neural network (WSSDCNN) for content-aware image retargeting. Our network takes a source image and a target aspect ratio, and then directly outputs a retargeted image. Retargeting is performed through a shift map, which is a pixel-wise mapping from the source to the target grid. Our method implicitly learns an attention map, which leads to a content-aware shift map for image retargeting. As a result, discriminative parts in an image are preserved, while background regions are adjusted seamlessly. In the training phase, pairs of an image and its image-level annotation are used to compute content and structure losses. We demonstrate the effectiveness of our proposed method for a retargeting application with insightful analyses.Comment: 10 pages, 11 figures. To appear in ICCV 2017, Spotlight Presentatio

Spatial Delay Line Canceler-Based Sidelobe Blanking for Low Radar-Cross-Section Target

This study proposes a sidelobe blanking (SLB) system with a spatial delay line canceler (DLC) and non-coherent integrator in a uniform linear array. After the equations for the target and noise power in the SLB system were established, SLB-ratio functions for the proposed and conventional SLB channels were developed. Using these ratio functions, the optimal SLB thresholds for the general detectable target and low radar-cross-section (RCS) target were estimated. The results of the SLB thresholds were confirmed by the Monte Carlo simulation, which indicated that the proposed SLB channel provides reliable performance without false SLB decisions in the sidelobe region. Using the estimated optimal threshold, the proposed SLB channel provides reliable performance, particularly for low-RCS targets. In contrast, the conventional SLB channel produces numerous false SLB decisions in the sidelobe region. The proposed synthesis is a simple but powerful method for obtaining the reliable SLB ratio. The SLB channel in various array antenna systems can be developed based on this method