为了增强Windows远程桌面通过FRP和Nginx配置的安全性，限制失败登录尝试是非常重要的一环。我将介绍多个层面上的失败登录限制配置方法。

Fail2ban是一个强大的安全工具，能够监控服务器日志文件，检测可疑活动，并自动配置防火墙规则来阻止发起这些活动的IP地址。下面是详细的配置和使用方法：

根据您提供的frps.toml和frpc.toml配置，我将详细说明如何通过Nginx反向代理，实现使用域名xx.xx访问Windows远程桌面的完整配置流程。

Centre des Mat´eriaux、Centre de Mise en Forme des Mat´eriaux、Centre de Morphologie Math´ematique

Transformers have recently gained considerable popularity for capturing long-range dependencies in the medical image segmentation. However, most transformer-based segmentation methods primarily focus on modeling global dependencies and fail to fully explore the complementary nature of different dimensional dependencies within features. These methods simply treat the aggregation of multi-dimensional dependencies as auxiliary modules for incorporating context into the Transformer architecture, thereby limiting the model’s capability to learn rich feature representations. To address this issue, we introduce the Dual Attention Encoder with Joint Preservation (DANIE) for medical image segmentation, which synergistically aggregates spatial-channel dependencies across both local and global areas through attention learning. Additionally, we design a lightweight aggregation mechanism, termed Joint Preservation, which learns a composite feature representation, allowing different dependencies to complement each other. Without bells and whistles, our DANIE significantly improves the performance of previous state-of-the-art methods on five popular medical image segmentation benchmarks, including Synapse, ACDC, ISIC 2017, ISIC 2018 and GlaS.

在DRIVE数据集上的改进效果预估：

Medical image segmentation methods based on deep learning network are mainly divided into CNN and Transformer. However, CNN struggles to capture long-distance dependencies, while Transformer suffers from high computational complexity and poor local feature learning. To efficiently extract and fuse local features and long-range dependencies, this paper proposes Rolling-Unet, which is a CNN model combined with MLP. Specifically, we propose the core R-MLP module, which is responsible for learning the long-distance dependency in a single direction of the whole image. By controlling and combining R-MLP modules in different directions, OR-MLP and DOR-MLP modules are formed to capture long-distance dependencies in multiple directions. Further, Lo2 block is proposed to encode both local context information and long-distance dependencies without excessive computational burden. Lo2 block has the same parameter size and computational complexity as a 3×3 convolution. The experimental results on four public datasets show that Rolling-Unet achieves superior performance compared to the state-of- the-art methods.

Recently, many excellent weakly supervised semantic segmentation (WSSS) works are proposed based on class activation mapping (CAM). However, there are few works that consider the characteristics ofmedical images. In this paper, we find that there are mainly two challenges of medical images in WSSS: i) the boundary of object foreground and background is not clear; ii) the co-occurrence phenomenon is very severe in training stage. We thus propose a Causal CAM (C-CAM) method to overcome the above challenges. Our method is motivated by two cause-effect chains including category-causality chain and anatomy- causality chain. The category-causality chain represents the image content (cause) affects the category (effect). The anatomy-causality chain represents the anatomical structure (cause) affects the organ segmentation (effect). Extensive experiments were conducted on three public medical image data sets. Our C-CAM generates the best pseudo masks with the DSC of 77.26%, 80.34% and 78.15% on ProMRI, ACDC and CHAOS compared with other CAM-like methods. The pseudo masks ofC-CAM are further used to improve the segmentation performance for organ segmentation tasks. Our C-CAM achieves DSC of 83.83% on ProMRI and DSC of87.54% on ACDC, which outperforms state-of-the-art WSSS methods. Our code is available at https://github.com/Tian-lab/C-CAM.