Phasor measurement units (PMUs) are increasingly being deployed in power systems due to their high sampling rates and diverse data sampling types. However, this undoubtedly poses significant challenges to data centers in terms of data storage and transmission. This article proposes an adaptive rank-based tensor ring (TR) method for PMU data compression to address these issues. More specifically, we first extend the orders of the PMU measurement data to achieve high-order tensorization. Subsequently, based on using the alternating least-squares method to decompose the high-order data TR, we introduce a rank-increment strategy to obtain adaptive ranks. Using a TR data structure, the proposed method can transform high-order data with exponentially increasing volumes into a polynomial scale. This allows us to achieve cost-effective PMU data compression. The simulation results using real-world PMU measurement data reveal the excellent performance of our proposed method.

Deep learning-based methods have achieved significant successes on solving the blind super-resolution (BSR) problem. However, most of them request supervised pretraining on labelled datasets. This paper proposes an unsupervised kernel estimation model, named dynamic kernel prior (DKP), to realize an unsupervised and pretraining-free learning-based algorithm for solving the BSR problem. DKP can adaptively learn dynamic kernel priors to realize real-time kernel estimation, and thereby enables superior HR image restoration performances. This is achieved by a Markov chain Monte Carlo sampling process on random kernel distributions. The learned kernel prior is then assigned to optimize a blur kernel estimation network, which entails a network-based Langevin dynamic optimization strategy. These two techniques ensure the accuracy of the kernel estimation. DKP can be easily used to replace the kernel estimation models in the existing methods, such as Double-DIP and FKP-DIP, or be added to the off-the-shelf image restoration model, such as diffusion model. In this paper, we incorporate our DKP model with DIP and diffusion model, referring to DIP-DKP and Diff-DKP, for validations. Extensive simulations on Gaussian and motion kernel scenarios demonstrate that the proposed DKP model can significantly improve the kernel estimation with comparable runtime and memory usage, leading to state-of-the-art BSR results. The code is available at https://github.com/XYLGroup/DKP.

Face anti-spoofing (FAS) is vital to ensure the security of the face recognition systems, for which the essential task is to capture the unique spoof face features. Most of the existing methods extract spoof features from the whole faces, overlooking clues in local face patches. Meanwhile, researchers usually use intermediate parameters as a proxy in face classification, but this requires the design of additional loss functions. To solve these problems, we propose a multimodal proxy-free FAS model which uses contrastive language image pre-training (CLIP) as the backbone. Specifically, we use patches cropped from the original face to augment the data, forcing the network to learn local spoof features, such as the edges of printing attacks. At the same time, we introduce dynamic central difference convolutional (DCDC) adapter to extract fine-grained features in patches. Furthermore, we propose to adopt a proxy-free pairwise similarity learning (PSL) loss to achieve the goal that the maximum intra-class distance is smaller than the minimum inter-class distance. Experiments on several benchmark datasets show that the proposed method achieves state-of-the-art performance.