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1. Systematic Enumeration and Identification of Unique Spatial Topologies of 3D Systems Using Spatial Graph Representations

   https://doi.org/10.1115/DETC2021-66900  

   Peddada, Satya R.; Dunfield, Nathan M.; Zeidner, Lawrence E.; James, Kai A.; Allison, James T. (August 2021, 47th Design Automation Conference (DAC))

   Systematic enumeration and identification of unique 3D spatial topologies of complex engineering systems such as automotive cooling layouts, hybrid-electric power trains, and aero-engines are essential to search their exhaustive design spaces to identify spatial topologies that can satisfy challenging system requirements. However, efficient navigation through discrete 3D spatial topology options is a very challenging problem due to its combinatorial nature and can quickly exceed human cognitive abilities at even moderate complexity levels. Here we present a new, efficient, and generic design framework that utilizes mathematical spatial graph theory to represent, enumerate, and identify distinctive 3D topological classes for an abstract engineering system, given its system architecture (SA) — its components and interconnections. Spatial graph diagrams (SGDs) are generated for a given SA from zero to a specified maximum crossing number. Corresponding Yamada polynomials for all the planar SGDs are then generated. SGDs are categorized into topological classes, each of which shares a unique Yamada polynomial. Finally, for each topological class, one 3D geometric model is generated for an SGD with the fewest interconnect crossings. Several case studies are shown to illustrate the different features of our proposed framework. Design guidelines are also provided for practicing engineers to aid the utilization of this framework for application to different types of real-world problems. 

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2. ST-FiT: Inductive Spatial-Temporal Forecasting with Limited Training Data

   https://doi.org/10.1609/aaai.v39i11.33310  

   Lei, Zhenyu; Dong, Yushun; Li, Jundong; Chen, Chen (April 2025, Proceedings of the AAAI Conference on Artificial Intelligence)

   Spatial-temporal graphs are widely used in a variety of real-world applications. Spatial-Temporal Graph Neural Networks (STGNNs) have emerged as a powerful tool to extract meaningful insights from this data. However, in real-world applications, most nodes may not possess any available temporal data during training. For example, the pandemic dynamics of most cities on a geographical graph may not be available due to the asynchronous nature of outbreaks. Such a phenomenon disagrees with the training requirements of most existing spatial-temporal forecasting methods, which jeopardizes their effectiveness and thus blocks broader deployment. In this paper, we propose to formulate a novel problem of inductive forecasting with limited training data. In particular, given a spatial-temporal graph, we aim to learn a spatial-temporal forecasting model that can be easily generalized onto those nodes without any available temporal training data. To handle this problem, we propose a principled framework named ST-FiT. ST-FiT consists of two key learning components: temporal data augmentation and spatial graph topology learning. With such a design, ST-FiT can be used on top of any existing STGNNs to achieve superior performance on the nodes without training data. Extensive experiments verify the effectiveness of ST-FiT in multiple key perspectives. 

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3. A geometric framework for reaction enumeration in computational nucleic acid devices

   https://doi.org/10.1098/rsif.2023.0259  

   Kumar, Sarika; Lakin, Matthew R (November 2023, Journal of The Royal Society Interface)

   Cascades of DNA strand displacement reactions enable the design of potentially large circuits with complex behaviour. Computational modelling of such systems is desirable to enable rapid design and analysis. In previous work, the expressive power of graph theory was used to enumerate reactions implementing strand displacement across a wide range of complex structures. However, coping with the rich variety of possible graph-based structures required enumeration rules with complicated side-conditions. This paper presents an alternative approach to tackle the problem of enumerating reactions at domain level involving complex structures by integrating with a geometric constraint solving algorithm. The rule sets from previous work are simplified by replacing side-conditions with a general check on the geometric plausibility of structures generated by the enumeration algorithm. This produces a highly general geometric framework for reaction enumeration. Here, we instantiate this framework to solve geometric constraints by a structure sampling approach in which we randomly generate sets of coordinates and check whether they satisfy all the constraints. We demonstrate this system by applying it to examples from the literature where molecular geometry plays an important role, including DNA hairpin and remote toehold reactions. This work therefore enables integration of reaction enumeration and structural modelling. 

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4. Deciphering high-order structures in spatial transcriptomes with graph-guided Tucker decomposition

   https://doi.org/10.1093/bioinformatics/btae245  

   Broadbent, Charles; Song, Tianci; Kuang, Rui (June 2024, Bioinformatics)

   Abstract  Spatial transcripome (ST) profiling can reveal cells’ structural organizations and functional roles in tissues. However, deciphering the spatial context of gene expressions in ST data is a challenge—the high-order structure hiding in whole transcriptome space over 2D/3D spatial coordinates requires modeling and detection of interpretable high-order elements and components for further functional analysis and interpretation. This paper presents a new method GraphTucker—graph-regularized Tucker tensor decomposition for learning high-order factorization in ST data. GraphTucker is based on a nonnegative Tucker decomposition algorithm regularized by a high-order graph that captures spatial relation among spots and functional relation among genes. In the experiments on several Visium and Stereo-seq datasets, the novelty and advantage of modeling multiway multilinear relationships among the components in Tucker decomposition are demonstrated as opposed to the Canonical Polyadic Decomposition and conventional matrix factorization models by evaluation of detecting spatial components of gene modules, clustering spatial coefficients for tissue segmentation and imputing complete spatial transcriptomes. The results of visualization show strong evidence that GraphTucker detect more interpretable spatial components in the context of the spatial domains in the tissues. Availability and implementationhttps://github.com/kuanglab/GraphTucker. 

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5. Navigation Tasks in Desktop VR Environments to Improve the Spatial Orientation Skill of Building Engineers

   https://doi.org/10.3390/buildings11100492  

   Carbonell-Carrera, Carlos; Saorin, Jose; Jaeger, Allison (October 2021, Buildings)

   Virtual reality is a powerful tool for teaching 3D digital technologies in building engineering, as it facilitates the spatial perception of three-dimensional space. Spatial orientation skill is necessary for understanding 3D space. With VR, users navigate through virtually designed buildings and must be constantly aware of their position relative to other elements of the environment (orientation during navigation). In the present study, 25 building engineering students performed navigation tasks in a desktop-VR environment workshop. Performance of students using the desktop-VR was compared to a previous workshop in which navigation tasks were carried out using head-mounted displays. The Perspective Taking/Spatial Orientation Test measured spatial orientation skill. A questionnaire on user experience in the virtual environment was also administered. The gain in spatial orientation skill was 12.62%, similar to that obtained with head-mounted displays (14.23%). The desktop VR environment is an alternative to the HMD-VR environment for planning strategies to improve spatial orientation. Results from the user-experience questionnaire showed that the desktop VR environment strategy was well perceived by students in terms of interaction, 3D visualization, navigation, and sense of presence. Unlike in the HDM VR environment, student in the desktop VR environment did not report feelings of fatigue or dizziness. 

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