Increasingly stringent service-level objectives demand fast and accurate congestion control (CC) in data center networks. We observe that the feedback loop used by existing data center sender-driven CC schemes is inherently limited as it suffers from an inevitable delay. Consequently, a portion of traffic, which we call disobedient traffic, could finish before the feedback loop, thereby escaping the control of these schemes and exerting a negative impact on their performance. Furthermore, as link speeds continue to climb, the proportion and impact of disobedient traffic are concurrently escalating, exacerbating these issues. In this paper, we propose Compass, a solution implemented entirely on switch data plane to mitigate the impact of disobedient traffic. Compass utilizes sketching techniques to efficiently estimate the sending rate of disobedient traffic, and seamlessly integrates into HPCC and PowerTCP through incorporating the sending rate of disobedient traffic into their high-precision rate control algorithms. Such integration enhances network performance without incurring additional bandwidth overhead or modifications to host-side logic. Additionally, Compass supports incremental brownfield deployment, and all these properties make it highly practical for production deployment. Extensive simulations show that Compass improves both throughput and latency. For instance, Compass reduces tail flow completion times of medium and large flows by up to 35% for PowerTCP and 17% for HPCC.

A new method, called PSL-DefakeHop, is proposed to detect fake satellite images based on the parallel subspace learning (PSL) framework in this work. The DefakeHop method was developed previously for detection of deepfake generated faces under the successive subspace learning (SSL) framework. PSL is proposed to extract features from responses of multiple single-stage filter banks (or called PixelHops), which operate in parallel, and it improves SSL that extracts features from multi-stage cascaded filter banks. PSL has two advantages. First, PSL preserves discriminant features often lie in high-frequency channels, which are however ignored by SSL. Second, decisions from multiple filter banks can be ensembled to further improve detection accuracy. To demonstrate the effectiveness of the proposed PSL-DefakeHop method, we evaluate it on the UW Fake Satellite Image dataset and observe perfect classification performance (i.e., 100% F1 score, precision and recall).

An image anomaly localization method based on the successive subspace learning (SSL) framework, called Anomaly-Hop, is proposed in this work. AnomalyHop consists of three modules: 1) feature extraction via successive subspace learning (SSL), 2) normality feature distributions modeling via Gaussian models, and 3) anomaly map generation and fusion. Comparing with state-of-the-art image anomaly localization methods based on deep neural networks (DNNs), AnomalyHop is mathematically transparent, easy to train, and fast in its inference speed. Besides, its area under the ROC curve (ROC-AUC) performance on the MVTec AD dataset is 95.9%, which is among the best of several benchmarking methods.

Features that capture textural patterns of a certain class of images are crucial for texture segmentation tasks. This paper introduces a data-centric approach to efficiently extract and represent textural information, which adapts to a wide variety of textures. Based on the strong self-similarities and quasi-periodicity in texture images, the proposed method first constructs a representative texture pattern set for the given image by leveraging the patch clustering strategy. Then, pixel-wise texture features are designed according to the similarities between local patches and the representative textural patterns. Moreover, the proposed feature is generic and flexible, and can perform segmentation task by integrating it into various segmentation approaches easily. Extensive experimental results on both textural and natural image segmentation show that the segmentation method using the proposed features achieves very competitive or even better performance compared with the stat-of-the-art methods.

A hierarchical spatial-spectral correlation (HSSC) method is proposed for texture analysis in this work. The HSSC method first applies a multi-stage spatial-spectral transform to input texture patches, which is known as the Saak transform. Then, it conducts a correlation analysis on Saak transform coefficients to obtain texture features of high discriminant power. To demonstrate the effectiveness of the HSSC method, we conduct extensive experiments on texture classification and show that it offers very competitive results comparing with state-of-the-art methods.