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HyperNetX (HNX) is an open source Python library for the analysis and visualization of complex network data modeled as hypergraphs. Initially released in 2019, HNX facilitates exploratory data analysis of complex networks using algebraic…
This study explores the integration of Lidar, Synthetic Aperture Radar (SAR), and optical imagery through advanced artificial intelligence techniques for enhanced urban mapping. By fusing these diverse geospatial datasets, we aim to…
Accurate prediction of pedestrian and bicyclist paths is integral to the development of reliable autonomous vehicles in dense urban environments. The interactions between vehicle and pedestrian or bicyclist have a significant impact on the…
The open-source PyNX toolkit [Favre-Nicolin et al (2011) arXiv:1010.2641, Mandula et al (2016)] has been extended to provide tools for coherent X-ray imaging data analysis and simulation. All calculations can be executed on graphical…
In this paper, we introduce eipy--an open-source Python package for developing effective, multi-modal heterogeneous ensembles for classification. eipy simultaneously provides both a rigorous, and user-friendly framework for comparing and…
Monitoring vegetation dynamics is crucial for addressing global environmental challenges like degradation and deforestation, but traditional remote sensing methods are often complex and resource-intensive. To overcome these barriers, we…
Perception research is increasingly modelled using streetscapes, yet many approaches still rely on pixel features or object co-occurrence statistics, overlooking the explicit relations that shape human perception. This study proposes a…
Pedestrian trajectory prediction in urban scenarios is essential for automated driving. This task is challenging because the behavior of pedestrians is influenced by both their own history paths and the interactions with others. Previous…
Online social networks convey rich information about geospatial facets of reality. However in most cases, geographic information is not explicit and structured, thus preventing its exploitation in real-time applications. We address this…
Tracing the sequence of library and system calls that a program makes is very helpful in the characterization of its interactions with the surrounding environment and ultimately of its semantics. Due to entanglements of real-world software…
This paper introduces \textit{measurement trees}, a novel class of metrics designed to combine various constructs into an interpretable multi-level representation of a measurand. Unlike conventional metrics that yield single values,…
In this paper, we present a nonlinear analysis software toolkit, which can help in biomechanical gait data analysis by implementing various nonlinear statistical analysis algorithms. The toolkit is proposed to tackle the need for an…
Performance analysis is a critical step in the oft-repeated, iterative process of performance tuning of parallel programs. Per-process, per-thread traces (detailed logs of events with timestamps) enable in-depth analysis of parallel program…
Geospatial data analysis plays a crucial role in tackling intricate societal challenges such as urban planning and climate modeling. However, employing tools like GeoPandas, a prominent Python library for geospatial data manipulation,…
Convolutional neural networks (CNNs) have demonstrated their superiority in numerous computer vision tasks, yet their computational cost results prohibitive for many real-time applications such as pedestrian detection which is usually…
Computer simulation has become one of the most important tools in scientific research in many disciplines. Benefiting from the dynamical trajectories regulated by versatile interatomic interactions, various material properties can be…
Accurately estimating traffic variables across unequipped portions of a network remains a significant challenge due to the limited coverage of sensor-equipped links, such as loop detectors and probe vehicles. A common approach is to apply…
Deep learning continues to push state-of-the-art performance for the semantic segmentation of color (i.e., RGB) imagery; however, the lack of annotated data for many remote sensing sensors (i.e. hyperspectral imagery (HSI)) prevents…
Regional planning processes and associated redevelopment projects can be complex due to the vast amount of diverse data involved. However, all of this data shares a common geographical reference, especially in the renaturation of former…
Community detection plays a crucial role in understanding the structural organization of complex networks. Previous methods, particularly those from statistical physics, primarily focus on the analysis of mesoscopic network structures and…