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SAMCNet for Spatial-configuration-based Classification: A Summary of Results
[article]
2022
arXiv
pre-print
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In addition, the related deep neural networks are limited to category pairs and do not explore larger subsets of point categories. ...
To overcome these limitations, we propose a Spatial-interaction Aware Multi-Category deep neural Network (SAMCNet) architecture and contribute novel local reference frame characterization and point pair ...
We also thank Kim Koffolt and the Spatial Computing Research Group for valuable comments and refinements. ...
arXiv:2112.12219v2
fatcat:ti6yt5qhfrgephaz7bknpco6dm
What We Know About the Brain Structure–Function Relationship
2018
Behavioral Sciences
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to elucidate multi-scale relationships and to infer disorder mechanisms. ...
The main hypothesis is that the anatomic architecture conditions, but does not determine, the neural network dynamic. ...
The FC is dealt with as a purely spatial measure and this static description is a moot point given that the non-stationary of resting-state connectivity turns around the way that functional interactions ...
doi:10.3390/bs8040039
pmid:29670045
pmcid:PMC5946098
fatcat:nahupzt2enfarcaa5ju3qrbbwm
Research topic trend prediction of scientific papers based on spatial enhancement and dynamic graph convolution network
[article]
2022
arXiv
pre-print
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Our model combines a graph convolutional neural network (GCN) and Temporal Convolutional Network (TCN), specifically, GCNs are used to learn the spatial dependencies of research topics a and use space ...
To simultaneously capture the spatial dependencies and temporal changes between research topics, we propose a deep neural network-based research topic hotness prediction algorithm, a spatiotemporal convolutional ...
For example, with the rise of graph neural networks, The transportation network begins to use the graph neural network [5] [6] to capture the spatial features, and the research on intelligent transportation ...
arXiv:2203.16256v1
fatcat:lyowmrwct5ggtpnro5nt7e6zvy
Learning cell communication from spatial graphs of cells
[article]
2021
bioRxiv
pre-print
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We address these limitations using spatial molecular profiling data with node-centric expression modeling (NCEM), a computational method based on graph neural networks which reconciles variance attribution ...
These events cannot be directly observed in molecular profiling assays of single cells and have to be inferred. ...
of the Bavarian Research Association "ForInter" (Interaction of human brain cells), by the Wellcome Trust Grant 108413/A/15/D and by the Helmholtz Association's Initiative and Networking Fund through Helmholtz ...
doi:10.1101/2021.07.11.451750
fatcat:k3efxkguhncxhj3e3ihrgsbr7u
Attentive Gated Graph Neural Network for Image Scene Graph Generation
2020
Symmetry
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In this work, we translate the scene graph into an Attentive Gated Graph Neural Network which can propagate a message by visual relationship embedding. ...
Image scene graph is a semantic structural representation which can not only show what objects are in the image, but also infer the relationships and interactions among them. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/sym12040511
fatcat:5v57eu723bglpm44pib6xj2fje
Leveraging Spatio-Temporal Graphs and Knowledge Graphs: Perspectives in the Field of Maritime Transportation
2021
ISPRS International Journal of Geo-Information
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While the integration of time within GIS has long been a domain of major interest, alternative modelling and data manipulation approaches derived from graph and knowledge-based principles provide many ...
This paper introduces a prospective study of the potential of spatio-temporal graphs (ST-graphs) and knowledge graphs (K-graphs) for the modelling of geographical phenomena. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/ijgi10080541
fatcat:xardl3g6qrcmxeq2mauoyk7wjy
Positional Encoder Graph Neural Networks for Geographic Data
[article]
2022
arXiv
pre-print
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Graph neural networks (GNNs) provide a powerful and scalable solution for modeling continuous spatial data. ...
spatial autocorrelation in the data in parallel with the main task. ...
Specific GNN architectures including Graph Convolutional Networks [20] , Graph Attention Networks [31] and GraphSAGE [12] are powerful methods for inference and representation learning with spatial ...
arXiv:2111.10144v2
fatcat:7kfyjso6hjebros6j7jjqeeenu
Multi-Resolution Graph Neural Network for Large-Scale Pointcloud Segmentation
[article]
2020
arXiv
pre-print
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We reduce the computation demand by utilizing a graph neural network on the preformed pointcloud graphs and retain the precision of the segmentation with a bidirectional network that fuses feature embedding ...
We present MuGNet, a memory-efficient, end-to-end graph neural network framework to perform semantic segmentation on large-scale pointclouds. ...
All of the frameworks sequentially infer the graph embeddings with only forward network paths. ...
arXiv:2009.08924v1
fatcat:6sczmd7lzvd3rjh6eevjvlhoza
Survey of Knowledge Graph Approaches and Applications
2020
Journal on Artificial Intelligence
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On the basis of comprehensively expounding the definition and architecture of knowledge graph, this paper reviews the key technologies of knowledge graph construction, including the research progress of ...
With the advent of the era of big data, knowledge engineering has received extensive attention. How to extract useful knowledge from massive data is the key to big data analysis. ...
on spatial distribution); b) reasoning based on neural network; c) mixed reasoning (mixed rules and distributed representation reasoning, mixed neural network and distributed representation reasoning) ...
doi:10.32604/jai.2020.09968
fatcat:yvggvqhexze43jyomytsf7kfr4
Multi-Robot Collaborative Perception with Graph Neural Networks
[article]
2022
arXiv
pre-print
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2022 accepted arXiv:2201.01760v2 fatcat:7zyv3mnwdzbddb7neq3fdvpq2y
In this paper, we propose a general-purpose Graph Neural Network (GNN) with the main goal to increase, in multi-robot perception tasks, single robots' inference perception accuracy as well as resilience ...
Several experiments both using photo-realistic and real data gathered from multiple aerial robots' viewpoints show the effectiveness of the proposed approach in challenging inference conditions including ...
This paper was recommended for publication by Editor Cesar Cadena upon evaluation of the Associate Editor and Reviewers' comments. ...
arXiv:2201.01760v1
fatcat:tx3w52t5crdidiaf7s5hwepiom
Incorporating biological structure into machine learning models in biomedicine
2020
Current Opinion in Biotechnology
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Examples of such data include DNA or RNA sequences, gene sets or pathways, gene interaction or coexpression networks, ontologies, and phylogenetic trees. ...
The area of research would benefit from performant open source implementations and independent benchmarking efforts. ...
Acknowledgement The authors would like to thank Daniel Himmelstein for a critical reading of the manuscript and helpful discussion. ...
doi:10.1016/j.copbio.2019.12.021
pmid:31962244
pmcid:PMC7308204
fatcat:dldjmhskujb4nhg5v7h2vw3fxa
Advantages of using graph databases to explore chromatin conformation capture experiments
2021
BMC Bioinformatics
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The use of graph databases, instead, supports both the analysis and the visualisation of the spatial pattern present in Hi-C data, in particular for comparing different experiments or for re-mapping omics ...
These concepts have been implemented in NeoHiC, an open-source and user-friendly web application for the progressive visualisation and analysis of Hi-C networks based on the use of the Neo4j graph database ...
The full contents of the supplement are available at https ://bmcbi oinfo rmati cs.biome dcent ral.com/artic les/suppl ement s/volum e-22-suppl ement -2. ...
doi:10.1186/s12859-020-03937-0
pmid:33902433
fatcat:hmiaaz7tgza4hc74cflhovjva4
Semantic Enhancement of Human Urban Activity Chain Construction Using Mobile Phone Signaling Data
2021
ISPRS International Journal of Geo-Information
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However, the spatial and temporal uncertainty of the ubiquitous mobile sensing data brings a huge challenge for modeling and analyzing human activities. ...
Semantic activity chain modeling with activity purpose provides scientific methodological support for the analysis and decision-making of human behavior, urban planning, traffic management, green sustainable ...
(1) Graph construction In graph neural networks, the graph connection of common social networks and paper citation networks can be directly built based on the data's subscription and citation relationships ...
doi:10.3390/ijgi10080545
fatcat:5bkfpitm3nhxhipjrbpe6wat5y
Spatial Commonsense Graph for Object Localisation in Partial Scenes
[article]
2022
arXiv
pre-print
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The SCG is used to estimate the unknown position of the target object in two steps: first, we feed the SCG into a novel Proximity Prediction Network, a graph neural network that uses attention to perform ...
The proposed solution is based on a novel scene graph model, the Spatial Commonsense Graph (SCG), where objects are the nodes and edges define pairwise distances between them, enriched by concept nodes ...
First, we construct a spatial commonsense graph (SCG) from the known scene by enriching the scene graph with concept relationships, resulting in edges of three types: UsedFor (orange edges), AtLocation ...
arXiv:2203.05380v2
fatcat:wxvwmthcz5aktgvtnmloy2dmre
Spatio-Temporal Features in Action Recognition Using 3D Skeletal Joints
2019
Sensors
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This paper explores different interpretations of both the spatial and the temporal dimensions of a sequence of frames describing an action. ...
Using 3D skeleton joints extracted from depth images taken with time-of-flight (ToF) cameras has been a popular solution for accomplishing these tasks. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s19020423
fatcat:fwdwbvh7x5bxzoje6iceelpnui
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