Histopathological examination primarily relies on hematoxylin and eosin (H&E) and immunohistochemical (IHC) staining. Though IHC provides more crucial molecular information for diagnosis, it is more costly than H&E staining. Stain transfer technology seeks to efficiently generate virtual IHC images from H&E images. While current deep learning-based methods have made progress, they still struggle to maintain pathological and structural consistency across biomarkers without pixel-level aligned reference. To address the problem, we propose an Auxiliary Task supervision-based Stain Transfer method for multi-biomarkers (ATST-Net), which pioneeringly employs human annotation-free masks as ground truth (GT). ATST-Net ensures pathological consistency, structural preservation and style transfer. It automatically annotates H&E masks in a cost-effective manner by utilizing consecutive IHC sections. Multiple auxiliary tasks provide diverse supervisory information on the location and intensity of biomarker expression, ensuring model accuracy and interpretability. We design a pretrained model-based generator to extract deep feature in H&E images, improving generalization performance. Extensive experiments demonstrate the effectiveness of ATST-Net's components. Compared to existing methods, ATST-Net achieves state-of-the-art (SOTA) accuracy on datasets with multiple biomarkers and intensity levels, while also reflecting high practical value. Code is available at https://github.com/SikangSHU/ATST-Net.

This paper investigates the cooperative control of heterogeneous networked multiagent systems (MASs) in the presence of unknown nonlinear disturbances through one novel event-triggered asynchronous quasizero-sum game (ET-AQZSG) scheme. The individuals in the MASs are divided into principals and agents on the basis of their respective state values. A sufficient condition is established to prevent Zeno behavior in the ET-AQZSG scheme, ensuring its practical applicability. Additionally, a separable nonlinear functions (SNLFs) is proposed for assessing global stability. By leveraging norm inequalities and ordinary differential equations, a set of slackness conditions is formulated to ensure both feasibility and solution accuracy and promote convergence to the optimal solution. The proposed approach enhances communication flexibility without compromising computational efficiency. Finally, two numerical examples based on the topology of an electromagnetic actuator system and the topology of IEEE 39-Bus system are presented to demonstrate the efficacy of the ET-AQZSG scheme.

Recently, large language models (LLMs) have demonstrated impressive capabilities and gained widespread applications. However, their direct application to recommendation tasks (e.g., rating prediction task) often falls short of optimal results due to a lack of understanding of collaborative information in recommendations. In this paper, we propose Large lAnguage Model Augmented Recommendation (LAMAR) framework to address this limitation. Instead of relying solely on LLMs, our framework combines their outputs with traditional recommendation models, leveraging both collaborative and semantic information. We further enhance the recommendation performance through an ensemble of diverse prompts and utilize LLMs to extract side information for augmenting traditional recommendation models. Empirical studies on real-world datasets demonstrate that LAMAR outperforms existing approaches, highlighting the benefits of leveraging LLMs in recommendation systems. Code is available at https: //github.com/sichunluo/LAMAR.