Senchun Chai

Liqun Huang, Long Chen, Baihai Zhang et al.

Brain tumor segmentation remains a challenge in medical image segmentation tasks. With the application of transformer in various computer vision tasks, transformer blocks show the capability of learning long-distance dependency in global space, which is complementary with CNNs. In this paper, we proposed a novel transformer-based generative adversarial network to automatically segment brain tumors with multi-modalities MRI. Our architecture consists of a generator and a discriminator, which are trained in min-max game progress. The generator is based on a typical "U-shaped" encoder-decoder architecture, whose bottom layer is composed of transformer blocks with resnet. Besides, the generator is trained with deep supervision technology. The discriminator we designed is a CNN-based network with multi-scale $L_{1}$ loss, which is proved to be effective for medical semantic image segmentation. To validate the effectiveness of our method, we conducted experiments on BRATS2015 dataset, achieving comparable or better performance than previous state-of-the-art methods.

Yuhan Suo, Runqi Chai, Kaiyuan Chen et al.

With the rapid development of the Internet of Things (IoT), the risks of data tampering and malicious information injection have intensified, making efficient threat detection in large-scale distributed sensor networks a pressing challenge. To address the decline in malicious information detection efficiency as network scale expands, this paper investigates a robust set partitioning strategy and, on this basis, develops a distributed attack detection framework with theoretical guarantees. Specifically, we introduce a gain mutual influence metric to characterize the inter-subset interference arising during gain updates, thereby revealing the fundamental reason for the performance gap between distributed and centralized algorithms. Building on this insight, the set partitioning strategy based on Grassmann distance is proposed, which significantly reduces the computational cost of gain updates while maintaining detection performance, and ensures that the distributed setting under subset partitioning preserves the same theoretical performance bound as the baseline algorithm. Unlike conventional clustering methods, the proposed set partitioning strategy leverages the intrinsic observational features of sensors for robust partitioning, thereby enhancing resilience to noise and interference. Simulation results demonstrate that the proposed method limits the performance gap between distributed and centralized detection to no more than 1.648$\%$, while the computational cost decreases at an order of $O(1/m)$ with the number of subsets $m$. Therefore, the proposed algorithm effectively reduces computational overhead while preserving detection accuracy, offering a practical low-cost and highly reliable security detection solution for edge nodes in large-scale IoT systems.

Yuhan Suo, Senchun Chai, Xudong Zhao et al.

Distributed online learning in multi-agent systems is highly vulnerable to adversarial influence, especially when malicious agents cannot be fully isolated during the transient stage. While existing studies mainly pursue resilient consensus or secure fusion, they pay much less attention to the learning inefficiency and extra evolution cost accumulated during the defense process. This paper addresses this gap by developing a cost-aware distributed online learning framework with strict rejection behavior against adversarial agents.Under this mechanism, the state evolution cost of online adaptation is formulated and the cost amplification effect caused by adversarial interactions is theoretically characterized. To balance robustness, convergence efficiency, and long-term cost, we propose an adaptive adjustment mechanism for the state-evolution rate. The resulting outer-layer update can be equivalently viewed as a constrained online optimization problem. We further establish the well-posedness and regularity of the associated periodic Riccati layer, and show that the outer-layer update ensures feasibility and controlled variation. Based on these properties, closed-loop practical stability is rigorously established via a two-time-scale Lyapunov framework. Simulations demonstrate that the proposed method achieves robust and low-cost convergence under adversarial disturbances. Furthermore, a multi-satellite target tracking scenario with malicious interference further demonstrates the practical effectiveness of the strict rejection behavior.