PointeNet: A Lightweight Framework for Effective and Efficient Point Cloud Analysis

Current methodologies in point cloud analysis predominantly explore 3D geometries, often achieved through the introduction of intricate learnable geometric extractors in the encoder or by deepening networks with repeated blocks. However, these approaches inevitably lead to a significant number of learnable parameters, resulting in substantial computational costs and imposing memory burdens on CPU/GPU. Additionally, the existing strategies are primarily tailored for object-level point cloud classification and segmentation tasks, with limited extensions to crucial scene-level applications, such as autonomous driving. In response to these limitations, we introduce PointeNet, an efficient network designed specifically for point cloud analysis. PointeNet distinguishes itself with its lightweight architecture, low training cost, and plug-and-play capability, effectively capturing representative features. The network consists of a Multivariate Geometric Encoding (MGE) module and an optional Distance-aware Semantic Enhancement (DSE) module. The MGE module employs operations of sampling, grouping, and multivariate geometric aggregation to lightweightly capture and adaptively aggregate multivariate geometric features, providing a comprehensive depiction of 3D geometries. The DSE module, designed for real-world autonomous driving scenarios, enhances the semantic perception of point clouds, particularly for distant points. Our method demonstrates flexibility by seamlessly integrating with a classification/segmentation head or embedding into off-the-shelf 3D object detection networks, achieving notable performance improvements at a minimal cost. Extensive experiments on object-level datasets, including ModelNet40, ScanObjectNN, ShapeNetPart, and the scene-level dataset KITTI, demonstrate the superior performance of PointeNet over state-of-the-art methods in point cloud analysis

Transitioning from File-Based HPC Workflows to Streaming Data Pipelines with openPMD and ADIOS2

This paper aims to create a transition path from file-based IO to streaming-based workflows for scientific applications in an HPC environment. By using the openPMP-api, traditional workflows limited by filesystem bottlenecks can be overcome and flexibly extended for in situ analysis. The openPMD-api is a library for the description of scientific data according to the Open Standard for Particle-Mesh Data (openPMD). Its approach towards recent challenges posed by hardware heterogeneity lies in the decoupling of data description in domain sciences, such as plasma physics simulations, from concrete implementations in hardware and IO. The streaming backend is provided by the ADIOS2 framework, developed at Oak Ridge National Laboratory. This paper surveys two openPMD-based loosely-coupled setups to demonstrate flexible applicability and to evaluate performance. In loose coupling, as opposed to tight coupling, two (or more) applications are executed separately, e.g. in individual MPI contexts, yet cooperate by exchanging data. This way, a streaming-based workflow allows for standalone codes instead of tightly-coupled plugins, using a unified streaming-aware API and leveraging high-speed communication infrastructure available in modern compute clusters for massive data exchange. We determine new challenges in resource allocation and in the need of strategies for a flexible data distribution, demonstrating their influence on efficiency and scaling on the Summit compute system. The presented setups show the potential for a more flexible use of compute resources brought by streaming IO as well as the ability to increase throughput by avoiding filesystem bottlenecks

CenterTrack3D: Improved CenterTrack More Suitable for Three-Dimensional Objects

Abstract Compared with two-dimensional (2D) multi-object tracking (MOT) algorithms, three-dimensional (3D) multi-object tracking algorithms have more research significance and broad application prospects in the unmanned vehicles research field. Aiming at the problem of 3D multi-object detection and tracking, in this paper, the multi-object tracker CenterTrack, which focuses on 2D multi-object tracking task while ignoring object 3D information, is improved mainly from two aspects of detection and tracking, and the improved network is called CenterTrack3D. In terms of detection, CenterTrack3D uses the idea of attention mechanism to optimize the way that the previous-frame image and the heatmap of previous-frame tracklets are added to the current-frame image as input, and second convolutional layer of the hm output head is replaced by dynamic convolution layer, which further improves the ability to detect occluded objects. In terms of tracking, a cascaded data association algorithm based on 3D Kalman filter is proposed to make full use of the 3D information of objects in the image and increase the robustness of the 3D multi-object tracker. The experimental results show that, compared with the original CenterTrack and the existing 3D multi-object tracking methods, CenterTrack3D achieves 88.75% MOTA for cars and 59.40% MOTA for pedestrians and is very competitive on the KITTI tracking benchmark test set.</jats:p

Multi-Scale Spatio-Temporal Evolution Characteristics and Influencing Factors of Jobs-Housing Balance in Shenzhen in the Context of COVID-19 Pandemic

The outbreak of the coronavirus disease (COVID-19) pandemic, subsequent pandemic containment measures, and economic fallout have had a profound impact on people's employment choices, residential preferences, and travel habits. However, limited empirical studies have investigated longitudinal changes in the jobs-housing relationship, which is a critical intersection of these three elements during the global health crisis. Based on mobile signaling data from 2017 to 2022, this study employed visualization, employment activity compactness, and multinomial logistic regression to explore the multi-scale spatiotemporal evolution characteristics and causes of jobs-housing balance in Shenzhen during the COVID-19 pandemic. The results were as follows. (1) The spatial distribution patterns of residences and workplaces experienced the following evolutionary process: aggregation before the COVID-19 pandemic, dispersal during the strict control period of the pandemic, and, in the normalization phase, continuous dispersal in the first circle, as well as a rebound in the second and third circles. (2) Prior to the COVID-19 pandemic, jobs-housing relationships deteriorated; during the strict control period, employment self-containment remained stable, while cross-unit commuting distance significantly decreased, employment self-containment steadily improved, and cross-unit commuting distance returned to pre-pandemic levels. This trend is most prominently manifested at the 1 km grid scale across the three research scales. (3) Spatial heterogeneity affected the evolution trends of jobs-housing relationships before and during the pandemic, with public transportation accessibility and socioeconomic characteristics of residents being the main reasons for differentiation in evolutionary types. During the pandemic, regions with high dependency ratios, low average ages, and low educational levels exhibited delayed optimization of employment self-containment compared to regions with poor public transportation accessibility. Highly educated individuals were more likely to experience a continuous reduction in cross-unit commuting distances; migrant workers, older individuals, and female groups witnessed a decrease in cross-district commuting distances during the strict control period but a rebound extension during the normalization phase. The results contribute to a better understanding of the evolution of job-housing relationships during public health crises and provide support for future urban management

Variation of Ground Temperature along the Stratum Depth in Ice-rich Tundra

The pile foundation in the permafrost region is in a negative temperature environment, so the concrete is affected by the negative temperature of the surrounding soil.It not only affects the formation of concrete strength, but also leads to engineering quality accidents in serious cases.Based on the actual measurement of temperature at different strata depths and the comprehensive consideration of surface temperature, terrestrial heat flux and other parameters, the law curve of temperature change along depth in Greater Khingan is established.The calculated results of the curve are consistent with the measured results of ground temperature.The results show that the variation trend of ground temperature along the strata depth at different monitoring sites is basically the same. From June to November, the ground temperature at different depths tends to be constant.From December to May, the ground temperature at any depth within the depth range of 0 to 5.5m follows the law of the cosine function.Below 5.5m, the earth temperature no longer varies with depth.The research results can be used as reference for pile foundation construction under negative temperature environment.</jats:p

Variation of Ground Temperature along the Stratum Depth in Ice-rich Tundra of Hinggan Mountains Region, NE China

A pile foundation in a permafrost region is in a negative-temperature environment, so concrete is affected by the negative temperature of the surrounding soil. It not only affects the formation of concrete strength but also leads to engineering quality accidents in serious cases. With the support of the two permafrost bridge projects of the national highway from Beijing to Mohe in the Greater Khingan Mountains region, a systematic remote dynamic monitoring method for ground temperature in ice-rich tundra is proposed. Based on the actual measurement of temperature at different strata depths and the comprehensive consideration of surface temperature, terrestrial heat flux and other parameters, the ground temperature profile evolution in relation to depth in Greater Khingan was established. The theoretical ground temperature profile curve is similar to the measured profile. The results show that the variation trends of ground temperatures in relation to the strata depth at different monitoring sites is similar, and all show seasonal variation: From June to November, the ground temperature at different depths tends to be constant. From December to May, the ground temperature at any depth within the range of 0 to 5.5 m follows the curve of the cosine function. Below 5.5 m, the earth temperature no longer varies with depth. The research results can be used as reference for pile foundation construction in a negative-temperature environment in ice-rich tundra

From Clustered to Networked: Multi-Dimensional and Multi-Scale Performance Evaluation of Polycentric Urban Structure Evolution in Shenzhen, China

Polycentric urban structures are increasingly advocated to enhance economic efficiency, promote spatial equity, and support environmental sustainability. However, their roles in shaping urban performance remain insufficiently understood, particularly regarding the contributions of clustered and networked patterns to performance across different dimensions and scales. This study adopts a longitudinal framework that distinguishes the evolutionary stages of polycentric urban structures and evaluates multi-dimensional, multi-scale performance, taking Shenzhen, China as a case study. The results show that Shenzhen&rsquo;s polycentric structure evolved nonlinearly in its clustered pattern and linearly in its networked characteristics, with steady improvements in spatial and economic performance contrasting with the inverted U-shaped trajectory in social performance. Clustered and networked polycentric structures contribute differently: improvements in spatial performance are driven by multiple indicators, major economic indicators (at constant prices) increase with strengthened networked characteristics, and social performance benefits from clustered patterns only when public service provision is coordinated. This research provides new evidence for the co-evolution of polycentric structures and urban performance, suggesting that the effectiveness of polycentric development lies not in choosing between clustered and networked forms, but in strategically integrating them&mdash;optimizing scale&ndash;distance coordination among centers to enhance the clustered pattern, while differentiating center functions to strengthen networked characteristics

Variation of Ground Temperature along the Stratum Depth in Ice-rich Tundra of Hinggan Mountains Region, NE China

A pile foundation in a permafrost region is in a negative-temperature environment, so concrete is affected by the negative temperature of the surrounding soil. It not only affects the formation of concrete strength but also leads to engineering quality accidents in serious cases. With the support of the two permafrost bridge projects of the national highway from Beijing to Mohe in the Greater Khingan Mountains region, a systematic remote dynamic monitoring method for ground temperature in ice-rich tundra is proposed. Based on the actual measurement of temperature at different strata depths and the comprehensive consideration of surface temperature, terrestrial heat flux and other parameters, the ground temperature profile evolution in relation to depth in Greater Khingan was established. The theoretical ground temperature profile curve is similar to the measured profile. The results show that the variation trends of ground temperatures in relation to the strata depth at different monitoring sites is similar, and all show seasonal variation: From June to November, the ground temperature at different depths tends to be constant. From December to May, the ground temperature at any depth within the range of 0 to 5.5 m follows the curve of the cosine function. Below 5.5 m, the earth temperature no longer varies with depth. The research results can be used as reference for pile foundation construction in a negative-temperature environment in ice-rich tundra.</jats:p