Tired of Over-smoothing? Stress Graph Drawing Is All You Need!

In designing and applying graph neural networks, we often fall into some optimization pitfalls, the most deceptive of which is that we can only build a deep model by solving over-smoothing. The fundamental reason is that we do not understand how graph neural networks work. Stress graph drawing can offer a unique viewpoint to message iteration in the graph, such as the root of the over-smoothing problem lies in the inability of graph models to maintain an ideal distance between nodes. We further elucidate the trigger conditions of over-smoothing and propose Stress Graph Neural Networks. By introducing the attractive and repulsive message passing from stress iteration, we show how to build a deep model without preventing over-smoothing, how to use repulsive information, and how to optimize the current message-passing scheme to approximate the full stress message propagation. By performing different tasks on 23 datasets, we verified the effectiveness of our attractive and repulsive models and the derived relationship between stress iteration and graph neural networks. We believe that stress graph drawing will be a popular resource for understanding and designing graph neural networks.Comment: 13 pages, 10 figures, 11 tables, and 27 formula

Effects of precipitation variation and trampling disturbance on seedling emergence of annual plants in a semi-arid grassland

Precipitation change and grazing are the main factors influencing vegetation structure and dynamics in semi-arid grassland. However, the effects of precipitation variation and livestock trampling on the seedling emergence patterns of plants remain largely unknown. In this study, an experiment with four gradients of trampling (no-trampling, light, moderate, and heavy) and three precipitation treatments (ambient precipitation, +30% precipitation, and −30% precipitation) was conducted to assess the effects of trampling disturbance and precipitation variation on seedling emergence of annual plants. The results showed that an increase in precipitation significantly improved total seedling emergence by 3.5–3.6 times and seedling density of grasses by more than 4.1 times under trampling conditions, while significantly improving total seedling emergence and density of forbs under no-trampling conditions. Moreover, +30% precipitation significantly improved the seedling proportion of grasses under light, moderate, and heavy trampling, while decreasing the seedling proportion of forbs. Seedling emergence of forbs was more sensitive to trampling disturbance, and seedling emergence of grasses was more sensitive to precipitation changes, especially under trampling conditions. Light and moderate trampling with a +30% precipitation increase promoted seedling emergence of grasses, and no trampling with a +30% precipitation increase improved seedling emergence of forbs. Thus, targeted grazing management measures should be implemented for plant communities dominated by either grasses or forbs under changing precipitation conditions

Samba: Semantic Segmentation of Remotely Sensed Images with State Space Model

High-resolution remotely sensed images pose a challenge for commonly used semantic segmentation methods such as Convolutional Neural Network (CNN) and Vision Transformer (ViT). CNN-based methods struggle with handling such high-resolution images due to their limited receptive field, while ViT faces challenges in handling long sequences. Inspired by Mamba, which adopts a State Space Model (SSM) to efficiently capture global semantic information, we propose a semantic segmentation framework for high-resolution remotely sensed images, named Samba. Samba utilizes an encoder-decoder architecture, with Samba blocks serving as the encoder for efficient multi-level semantic information extraction, and UperNet functioning as the decoder. We evaluate Samba on the LoveDA, ISPRS Vaihingen, and ISPRS Potsdam datasets, comparing its performance against top-performing CNN and ViT methods. The results reveal that Samba achieved unparalleled performance on commonly used remote sensing datasets for semantic segmentation. Our proposed Samba demonstrates for the first time the effectiveness of SSM in semantic segmentation of remotely sensed images, setting a new benchmark in performance for Mamba-based techniques in this specific application. The source code and baseline implementations are available at https://github.com/zhuqinfeng1999/Samba

Semantic Segmentation of Terrestrial Laser Scanning Point Clouds Using Locally Enhanced Image-Based Geometric Representations

Point cloud data acquired using terrestrial laser scanning (TLS) often need to be semantically segmented to support many applications. To this end, various point-, voxel-, and image-based methods have been developed. For large-scale point cloud data, the former two types of methods often require extensive computational effort. In contrast, image-based methods are favorable from the perspective of computational efficiency. However, existing image-based methods are highly dependent on RGB information and do not provide an effective means of representing and utilizing the local geometric characteristics of point cloud data in images. This not only limits the overall segmentation accuracy but also prohibits their application to situations where the RGB information is absent. To overcome such issues, this research proposes a novel image enhancement method to reveal the local geometric characteristics in images derived by the projection of the point cloud coordinates. Based on this method, various feature channel combinations were investigated experimentally. It was found that the new combination IZeDe (i.e., intensity, enhanced Z -coordinate, and enhanced range images) outperformed the conventional I RGB and I RGB D channel combinations. As such, the approach can be used to replace the RGB channels for semantic segmentation. Using this new combination and the pretrained HR-EHNet considered, a mean intersection over union (mIoU) of 74.2% and an overall accuracy (OA) of 92.1% were achieved on the Semantic3D benchmark, which sets a new state of the art (SOTA) for the semantic segmentation accuracy of image-based methods

Selecting Optimal Combination of Data Channels for Semantic Segmentation in City Information Modelling (CIM)

Over the last decade, a 3D reconstruction technique has been developed to present the latest as-is information for various objects and build the city information models. Meanwhile, deep learning based approaches are employed to add semantic information to the models. Studies have proved that the accuracy of the model could be improved by combining multiple data channels (e.g., XYZ, Intensity, D, and RGB). Nevertheless, the redundant data channels in large-scale datasets may cause high computation cost and time during data processing. Few researchers have addressed the question of which combination of channels is optimal in terms of overall accuracy (OA) and mean intersection over union (mIoU). Therefore, a framework is proposed to explore an efficient data fusion approach for semantic segmentation by selecting an optimal combination of data channels. In the framework, a total of 13 channel combinations are investigated to pre-process data and the encoder-to-decoder structure is utilized for network permutations. A case study is carried out to investigate the efficiency of the proposed approach by adopting a city-level benchmark dataset and applying nine networks. It is found that the combination of IRGB channels provide the best OA performance, while IRGBD channels provide the best mIoU performance.</jats:p

EXPLORING AN INDIVIDUAL THERMAL SENSATION ANALYSIS MODEL FOR HOSPITAL INPATIENTS BASED ON COMPARATIVE STUDIES

ABSTRACT This research investigated the key factors that influenced patients’ individual thermal sensations in a rehabilitation ward. Maintaining thermal comfort is important for occupant's well-being in healthcare facilities. The commonly used Predicted Mean Vote (PMV) thermal comfort model has limitations on considering an individual's needs, especially if the individual has impaired health. There was a lack of thermal sensation studies in medical settings. This study conducted a ten-week fieldwork in a real rehabilitation environment in order to develop a thermal sensation analysis model that could help understand individual patient's thermal needs. Traditional statistical models and artificial neural network (ANN)-based models, using real-world data including spatial and healthcare-related parameters, were established for a comparative study. The results of the study unveiled the substantial influence of spatial and healthcare-related parameters on inpatients’ indoor thermal sensations. Furthermore, the ANN-based model demonstrated better performance in aligning with real-world conditions and in providing more accurate prediction outcomes compared to the traditional statistical model. These findings can be used by hospital designers and engineers to optimize the overall quality of the thermal environment within a healthcare environment.</jats:p