This paper focuses on the state estimation problem in distributed sensor networks, where intermittent packet dropouts, corrupted observations, and unknown noise covariances coexist. To tackle this challenge, we formulate the joint estimation of system states, noise parameters, and network reliability as a Bayesian variational inference problem, and propose a novel variational Bayesian adaptive Kalman filter (VB-AKF) to approximate the joint posterior probability densities of the latent parameters. Unlike existing AKF that separately handle missing data and measurement outliers, the proposed VB-AKF adopts a dual-mask generative model with two independent Bernoulli random variables, explicitly characterizing both observable communication losses and latent data authenticity. Additionally, the VB-AKF integrates multiple concurrent multiple observations into the adaptive filtering framework, which significantly enhances statistical identifiability. Comprehensive numerical experiments verify the effectiveness and asymptotic optimality of the proposed method, showing that both parameter identification and state estimation asymptotically converge to the theoretical optimal lower bound with the increase in the number of sensors.

Firstly,when all the operands (i.e., weights, activations, errors and gradients) for general matrix multiplication (GEMM) and convolution computations are reduced to 8 bits, most DNNs suffer noticeable accuracy degradation (e.g., Figure 1(a)).

To address this issue, several studies have been conducted and have shown promising results.

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Below, we will introduce the details of each stage separately. In practical applications of image steganography, it is common to hide a single subject in an image, and this is also a problem that our method excels at solving. We employed two methods to obtain "Prompt1" and "Prompt2": an ChatGPT to generate the modified "Prompt2". The specific process of generating "Prompt2" is shown in Fig. A.1. We present examples from the Stego260 dataset in Fig. A.2, where each example consists of an image We show images from three categories: humans, animals, and general objects.

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Low-complexity single-image super-resolution based on nonnegative neighbor embedding.

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