Federated Graph Learning (FGL) has emerged as a powerful paradigm for decentralized training of graph neural networks while preserving data privacy. However, existing FGL methods are predominantly designed for static graphs and rely on parameter averaging or distribution alignment, which implicitly assume that all features are equally transferable across clients, overlooking both the spatial and temporal heterogeneity and the presence of client-specific knowledge in real-world graphs. In this work, we identify that such assumptions create a vicious cycle of spurious representation entanglement, client-specific interference, and negative transfer, degrading generalization performance in Federated Learning over Dynamic Spatio-Temporal Graphs (FSTG). To address this issue, we propose a novel causality-inspired framework named SC-FSGL, which explicitly decouples transferable causal knowledge from client-specific noise through representation-level interventions. Specifically, we introduce a Conditional Separation Module that simulates soft interventions through client conditioned masks, enabling the disentanglement of invariant spatio-temporal causal factors from spurious signals and mitigating representation entanglement caused by client heterogeneity. In addition, we propose a Causal Codebook that clusters causal prototypes and aligns local representations via contrastive learning, promoting cross-client consistency and facilitating knowledge sharing across diverse spatio-temporal patterns. Experiments on five diverse heterogeneity Spatio-Temporal Graph (STG) datasets show that SC-FSGL outperforms state-of-the-art methods.

This paper proposes to employ a multiple-input multiple-output (MIMO) radar-based quickest change detection (QCD) for non-contact sudden abnormal heart rate (HR) alerting, where the HR changes from a normal to an abnormal value at an unknown change time. By developing a signal model for the non-contact sudden abnormal HR alerting using MIMO radar, we propose a sequential generalized likelihood ratio test (GLRT) based abnormal HR detector to detect the HR change immediately and evaluate the abnormal HR alerting performance employing mean time to false alarm (MTFA) and worst case average detection delay (WADD). Numerical examples validate the correctness of the theoretical analysis and demonstrate the efficiency of the proposed method by comparing with the state-of-the-art methods.

Low-resolution fine-grained image classification has recently made significant progress, largely thanks to the superresolution techniques and knowledge distillation methods. However, these approaches lead to an exponential increase in the number of parameters and computational complexity of models. In order to solve this problem, in this letter, we propose a Vision Mamba Distillation (ViMD) approach to enhance the effectiveness and efficiency of low-resolution fine-grained image classification. Concretely, a lightweight super-resolution vision Mamba classification network (SRVM-Net) is proposed to improve its capability for extracting visual features by redesigning the classification sub-network with Mamba modeling. Moreover, we design a novel multi-level Mamba knowledge distillation loss boosting the performance, which can transfer prior knowledge obtained from a High-resolution Vision Mamba classification Network (HRVMNet) as a teacher into the proposed SRVM-Net as a student. Extensive experiments on seven public fine-grained classification datasets related to benchmarks confirm our ViMD achieves a new state-of-the-art performance. While having higher accuracy, ViMD outperforms similar methods with fewer parameters and FLOPs, which is more suitable for embedded device applications. Code is available at Github.

This letter investigates the non-contact heart rate (HR) and respiratory rate (RR) joint estimation employing multiple-input multiple-output (MIMO) radar with widely separated antennas. By developing a signal model for the HR and RR estimation using the MIMO radar, where the initial phases for HR and RR, as well as the reflection coefficients, are deterministic but unknown, we propose an HR-RR joint estimator. Unlike the existing methods, the theoretical performance of the HR and RR estimation is analyzed for the first time, where the corresponding Cramer-Rao Bounds (CRBs) are derived. These bounds provide the first theoretical performance benchmark for this type of radar-based estimation and guide the system parameters design to enhance the HR and RR estimation performance, thereby avoiding the complex numerical computations involved in the joint estimator. It is shown that the proposed method outperforms traditional methods and the CRB can provide the guidance for system parameter optimization.

The data-driven InfraRed Small Target Detection (IRSTD) methods have witnessed remarkable advancements in performance. However, these methods typically rely on high-quality pixel-level mask labels, requiring substantial human effort and time for annotation. To tackle this challenge, we propose a Prior Knowledge Enhanced Learning Approach (PKELA), which contains two key strategies: the Prior Knowledge Enhanced Initial Pseudo-label Generation (PKEIPG) strategy and the Teacher Knowledge Guided Label Update (TKGLU) strategy. Specifically, for the PKEIPG strategy, we introduce a Local Contrast Enhancement (LCE) module based on prior knowledge to suppress background interference. Drawing inspiration from the human brain’s processing sequence of visual information, we locate an appropriate neighborhood containing the infrared small target by identifying the target’s edges. Within this neighborhood, high-quality initial pseudo labels are obtained through adaptive threshold segmentation, which provide strong supervisory signals for the data-driven IRSTD method during the initial training phase. For the TKGLU strategy, we employ an Exponential Moving Average (EMA) teacher model that dynamically updates its parameters based on the current model’s state. Furthermore, a memory bank is established to archive the teacher model’s performance, which is treated as prior knowledge. This knowledge is then systematically used to guide the refinement of pseudo labels. Experimental results on the SIRST, NUDT-SIRST, IRSTD-1k, and SIRST3 datasets indicate that our PKELA exhibits superior training performance with single-point labels. By incorporating our approach, the data-driven IRSTD methods could achieve near-full-supervised performance in terms of Probability of Detection (Pd) metric, with Intersection over Union (IoU) and normalized Intersection over Union (nIoU) reaching up to 95.75% and 95.82% of full-supervised performance, respectively. Our code is available at https://gitee.com/mynewspace/pkela.

Consider a quickest detection problem, where a sudden change of parameters needs to be detected as quickly as possible. When unknown parameters exist in the post-change distribution, generalized likelihood ratio test (GLRT) based method is a straightforward option, which may, however, lead to huge computational complexity and storage burden due to the repeated operation of the enumerating all possible change time and estimating unknown parameters every time a new observation sample is received. In this paper, a drift-oriented GLRT (D-GLRT) algorithm is proposed to avoid the repeated enumerations and reduce the storage burden. It is shown that the D-GLRT has lower complexity compared with the conventional GLRT based method, with a little loss of performance. The upper bound of the worst case average detection delay (WADD) of the D-GLRT based method is derived. Numerical results are provided to validate our theoretical analysis.

Heart rate (HR) is one of the most important indicators to assess the health condition of a person, so obtaining an accurate HR estimate is crucial. In this paper, a multiple-input multiple-output (MIMO) radar with dispersed antennas is employed to monitor the physiological signals caused by respiration and heartbeat for non-contact HR estimation. Fourier sine series is used to model the respiration and heartbeat signals measured by the MIMO radar, which enters the return signal from their impact on the human chest movement and incorporates a delay which is time-varying. Bayesian analysis is used for the HR estimation, and the corresponding Cramer-Rao bound (CRB) is derived. Via numerical studies, we show that applying the maximum likelihood (ML) estimation to estimate the HR can lead to improved estimation performance over the existing HR estimation methods. The advantage of using MIMO radar for the HR estimation is also demonstrated.

Graphical models are widely used in cyber security analysis to capture relationships among variables in attack scenarios. However, most models are difficult to build due to the greatly imbalanced data in cyber attacks. To solve this problem, we propose a two-level Bayesian network framework in this paper. We firstly classify the cyber attacks into two levels, one is used to identify general types of attacks and the other is used to classify specific forms. Then we train Bayesian networks for each level. To help administrators cope with threats in time, we propose an analysis method. This method finds the important node's Markov blanket and sorts nodes in it by their influence on each specific form, which could help to understand key threats in cyberspace.

Key element protection of combat system is a challenging problem in modern combat. Effectively evaluating the key elements would be of great help in force deployment in crisis situations. This paper proposes a new evaluation method that combines expert evaluation, PCA (Principal Component Analysis) and TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution). Specifically, we first use expert evaluation to get attributes' values of each element. Then, we apply PCA to extract principal components from elements' attributes. Next, we use TOPSIS to calculate elements' proximities to ideal solutions under different principal components. Finally, we synthesize each element's proximity under different principal component to get the relative superiority. In addition, we test the proposed method in an experimental combat scenario, and show the plausibility of this method.