Polygon representation learning is essential for diverse applications,
encompassing tasks such as shape coding, building pattern classification, and
geographic question answering. While recent years have seen considerable
advancements in this field, much of the focus has been on single polygons,
overlooking the intricate inner- and inter-polygonal relationships inherent in
multipolygons. To address this gap, our study introduces a comprehensive
framework specifically designed for learning representations of polygonal
geometries, particularly multipolygons. Central to our approach is the
incorporation of a heterogeneous visibility graph, which seamlessly integrates
both inner- and inter-polygonal relationships. To enhance computational
efficiency and minimize graph redundancy, we implement a heterogeneous spanning
tree sampling method. Additionally, we devise a rotation-translation invariant
geometric representation, ensuring broader applicability across diverse
scenarios. Finally, we introduce Multipolygon-GNN, a novel model tailored to
leverage the spatial and semantic heterogeneity inherent in the visibility
graph. Experiments on five real-world and synthetic datasets demonstrate its
ability to capture informative representations for polygonal geometries.

通过引入一个综合框架，该研究主要关注多边形几何图形的学习表示，特别是多重多边形，并采用异构可见性图来捕捉内部和多个多边形之间的关系，同时提出了一个旋转 - 平移不变的几何表示方式，并设计了适用于多重多边形的新型模型 Multipolygon-GNN。实验证明该模型能够获取多边形几何图形的相关表示。

PolygonGNN：多边形几何的异构可视图表示学习

PolygonGNN: Representation Learning for Polygonal Geometries with  Heterogeneous Visibility Graph

Structure fires are responsible for the majority of fire-related deaths
nationwide. In order to assist with the rapid evacuation of trapped people,
this paper proposes the use of a system that determines optimal search paths
for firefighters and exit paths for civilians in real time based on
environmental measurements. Through the use of a LiDAR mapping system evaluated
and verified by a trust range derived from sonar and smoke concentration data,
a proposed solution to low visibility mapping is tested. These independent
point clouds are then used to create distinct maps, which are merged through
the use of a RANSAC based alignment methodology and simplified into a
visibility graph. Temperature and humidity data are then used to label each
node with a danger score, creating an environment tensor. After demonstrating
how a Linear Function Approximation based Natural Policy Gradient RL
methodology outperforms more complex competitors with respect to robustness and
speed, this paper outlines two systems (savior and refugee) that process the
environment tensor to create safe rescue and escape routes, respectively.

通过使用激光雷达测绘系统，结合声纳和烟雾浓度数据，本文提出了一种实时确定消防人员搜索路径和平民撤离路径的系统，通过对独立点云数据的合并和简化，以及根据温湿度数据为每个节点标注危险分数，建立了一个环境张量，并通过线性函数逼近基于自然政策梯度强化学习方法，证明其在鲁棒性和速度方面优于更复杂的竞争方法，最后，提出了两个系统（救助者和难民）用于处理环境张量，分别创建安全救援和逃生路线。

强化学习基于能见度低环境中逃生路径生成

Reinforcement Learning Based Escape Route Generation in Low Visibility  Environments

One major issue that challenges person re-identification (Re-ID) is the
ubiquitous occlusion over the captured persons. There are two main challenges
for the occluded person Re-ID problem, i.e., the interference of noise during
feature matching and the loss of pedestrian information brought by the
occlusions. In this paper, we propose a new approach called Feature Recovery
Transformer (FRT) to address the two challenges simultaneously, which mainly
consists of visibility graph matching and feature recovery transformer. To
reduce the interference of the noise during feature matching, we mainly focus
on visible regions that appear in both images and develop a visibility graph to
calculate the similarity. In terms of the second challenge, based on the
developed graph similarity, for each query image, we propose a recovery
transformer that exploits the feature sets of its $k$-nearest neighbors in the
gallery to recover the complete features. Extensive experiments across
different person Re-ID datasets, including occluded, partial and holistic
datasets, demonstrate the effectiveness of FRT. Specifically, FRT significantly
outperforms state-of-the-art results by at least 6.2\% Rank-1 accuracy and
7.2\% mAP scores on the challenging Occluded-Duke dataset. The code is
available at this https URL

该研究提出了一种名为 Feature Recovery Transformer（FRT）的新方法，通过可见性图匹配和特征恢复变压器来同时解决干扰噪声和遮挡带来的行人信息损失的问题，与其他最新技术相比，在挑战性的 Occluded-Duke 数据集上的准确性提高至少 6.2% Rank-1 和 7.2% mAP 打分。