Optimal subsampling for large scale Elastic-net regression

Datasets with sheer volume have been generated from fields including computer vision, medical imageology, and astronomy whose large-scale and high-dimensional properties hamper the implementation of classical statistical models. To tackle the computational challenges, one of the efficient approaches is subsampling which draws subsamples from the original large datasets according to a carefully-design task-specific probability distribution to form an informative sketch. The computation cost is reduced by applying the original algorithm to the substantially smaller sketch. Previous studies associated with subsampling focused on non-regularized regression from the computational efficiency and theoretical guarantee perspectives, such as ordinary least square regression and logistic regression. In this article, we introduce a randomized algorithm under the subsampling scheme for the Elastic-net regression which gives novel insights into L1-norm regularized regression problem. To effectively conduct consistency analysis, a smooth approximation technique based on alpha absolute function is firstly employed and theoretically verified. The concentration bounds and asymptotic normality for the proposed randomized algorithm are then established under mild conditions. Moreover, an optimal subsampling probability is constructed according to A-optimality. The effectiveness of the proposed algorithm is demonstrated upon synthetic and real data datasets.Comment: 28 pages, 7 figure

Image Inpainting Anti-Forensics Network via Attention-Guided Hierarchical Reconstruction

Privacy security and property rights protection have gradually attracted the attention of people. Users not only hope that the images edited by themselves will not be forensically investigated, but also hope that the images they share will not be tampered with. Aiming at the problem that inpainted images can be located by forensics, this paper proposes a general anti-forensics framework for image inpainting with copyright protection. Specifically, we employ a hierarchical attention model to symmetrically reconstruct the inpainting results based on existing deep inpainting methods. The hierarchical attention model consists of a structural attention stream and a texture attention stream in parallel, which can fuse hierarchical features to generate high-quality reconstruction results. In addition, the user’s identity information can be symmetrically embedded and extracted to protect copyright. The experimental results not only had high-quality structural texture information, but also had homologous features with the original region, which could mislead the detection of forensics analysis. At the same time, the protection of users’ privacy and property rights is also achieved

Reversible Privacy Protection with the Capability of Antiforensics

In this paper, we propose a privacy protection scheme using image dual-inpainting and data hiding. In the proposed scheme, the privacy contents in the original image are concealed, which are reversible that the privacy content can be perfectly recovered. We use an interactive approach to select the areas to be protected, that is, the protection data. To address the disadvantage that single image inpainting is susceptible to forensic localization, we propose a dual-inpainting algorithm to implement the object removal task. The protection data is embedded into the image with object removed using a popular data hiding method. We further use the pattern noise forensic detection and the objective metrics to assess the proposed method. The results on different scenarios show that the proposed scheme can achieve better visual quality and antiforensic capability than the state-of-the-art works

Efficient Lidar Signal Denoising Algorithm Using Variational Mode Decomposition Combined with a Whale Optimization Algorithm

Although lidar is a powerful active remote sensing technology, lidar echo signals are easily contaminated by noise, particularly in strong background light, which severely affects the retrieval accuracy and the effective detection range of the lidar system. In this study, a coupled variational mode decomposition (VMD) and whale optimization algorithm (WOA) for noise reduction in lidar signals is proposed and demonstrated completely. The combination of optimal VMD parameters of decomposition mode number K and quadratic penalty α was obtained by using the WOA and was critical in acquiring satisfactory analysis results for VMD denoising technology. Then, the Bhattacharyya distance was applied to identify the relevant modes, which were reconstructed to achieve noise filtering. Simulation results show that the performance of the proposed VMD-WOA method is superior to that of wavelet transform, empirical mode decomposition, and its variations. Experimentally, this method was successfully used to filter a lidar echo signal. The signal-to-noise ratio of the denoised signal was increased to 23.92 dB, and the detection range was extended from 6 to 10 km

Co-pyrolysis of Hami coal and mixed plastic: An interaction between primary volatiles study via in-situ Py-TOF-MS

Co-pyrolysis of Hami coal and mixed plastic: An interaction between primary volatiles study via in-situ Py-TOF-M