Network telemetry is an essential part of network management and infrastructure. Among them, cardinality telemetry provides statistics on network connectivity and distribution. Network-wide cardinality telemetry refers to the deployment of multiple telemetry nodes in network for cardinality estimate. This requires the deployed data structure to be mergeable, enabling the consolidation of data from different nodes. Unfortunately, existing mergeable data structures can’t simultaneously address two important criterions of cardinality telemetry: measurement accuracy and estimation interval. We propose CardSketch, aiming to adjust attention to cardinality telemetry based on changes of the network state. CardSketch incorporates a shift attention mechanism that leverages the randomness of hash functions to achieve unbiased transformations between data structures. This mechanism enables real-time selection of cardinality estimation methods based on the network’s state while preserving the original telemetry information as much as possible during the attention shift. We have implemented prototypes of CardSketch in software and hardware. Through extensive experimentation, the results demonstrate that CardSketch achieves excellent cardinality telemetry with minimal memory overhead. Even with a mere 50KB of memory space, it achieves a measurement precision of 87.75% and a measurement recall of 91.49%. Additionally, CardSketch supports multi-point aggregation and arbitrary partial key queries.

Hyperspectral image (HSI) data has a wide range of spectral information that is valuable for numerous tasks. HSI data encounters some challenges, such as small training samples, data scarcity, and redundant information. Researchers present numerous investigations to address these challenges, with convolutional neural networks (CNNs) being extensively used in HSI classification because of their capacity to extract features from data. Moreover, vision transformers have demonstrated their ability in the remote sensing field. However, the training of these models required a significant amount of labeled training data. We proposed a vision-based transformer module that consists of a multiscale feature extractor to extract joint spectral-spatial low-level, shallow features. For high-level semantic feature extraction, we proposed a regional attention mechanism with a spatially gated module. We tested the proposed model on four publicly available HSI datasets: Pavia University, Salinas, Xuzhou, Loukia, and the Houston 2013 dataset. Using only 1%, 1%, 1%, 2%, and 2% of the training samples from the five datasets, we achieved the best classification in terms of overall accuracy (OA), average accuracy (AA), and Kappa coefficient.

To quantify uncertainties in inverse problems of partial differential equations (PDEs), we formulate them into statistical inference problems using Bayes' formula. Recently, well-justified infinite-dimensional Bayesian analysis methods have been developed to construct dimension-independent algorithms. However, there are three challenges for these infinite-dimensional Bayesian methods: prior measures usually act as regularizers and are not able to incorporate prior information efficiently; complex noises, such as more practical non-i.i.d. distributed noises, are rarely considered; and time-consuming forward PDE solvers are needed to estimate posterior statistical quantities. To address these issues, an infinite-dimensional inference framework has been proposed based on the infinite-dimensional variational inference method and deep generative models. Specifically, by introducing some measure equivalence assumptions, we derive the evidence lower bound in the infinite-dimensional setting and provide possible parametric strategies that yield a general inference framework called the Variational Inverting Network (VINet). This inference framework can encode prior and noise information from learning examples. In addition, relying on the power of deep neural networks, the posterior mean and variance can be efficiently and explicitly generated in the inference stage. In numerical experiments, we design specific network structures that yield a computable VINet from the general inference framework. Numerical examples of linear inverse problems of an elliptic equation and the Helmholtz equation are presented to illustrate the effectiveness of the proposed inference framework.