Results 11 to 20 of about 8,739,015 (304)

Machine Learning of Spatial Data [PDF]

ISPRS International Journal of Geo-Information, 2021
Properties of spatially explicit data are often ignored or inadequately handled in machine learning for spatial domains of application. At the same time, resources that would identify these properties and investigate their influence and methods to handle them in machine learning applications are lagging behind. In this survey of the literature, we seek
Jean-Claude Thill, Behnam Nikparvar
exaly   +3 more sources

Comparing Immersive and Non-Immersive VR: Effects on Spatial Learning and Aesthetic Experience in Museum Settings [PDF]

Brain Sciences
Background/Objectives: The use of virtual reality (VR) solutions in design has rapidly increased globally. However, it remains unclear to what extent these technologies enhance people’s cognitive abilities.
Laura Piccardi, Marcello Massidda, Laura Travaglini, Sofia Pescarin, Marco Giancola, Massimiliano Palmiero, Matteo Deflorian, Sabrina Apollaro, Roberta Lista, Raffaella Nori   +9 more
doaj   +2 more sources

Could Spatial Learning in the Early Stages of Life Consistently Affect the Long-Term Memory of Leopard Geckos (Eublepharis macularius)? [PDF]

Animals
(1) Background: This study investigates the development of spatial navigation and long-term memory in the leopard gecko (Eublepharis macularius) to address gaps in understanding reptilian cognitive ontogeny.
Aleksandra Chomik, Eliška Pšeničková, Petra Frýdlová, Daniel Frynta, Markéta Janovcová, Eva Landová   +5 more
doaj   +2 more sources

Probiotic supplementation prevents stress-impaired spatial learning and enhances the effects of environmental enrichment [PDF]

Frontiers in Microbiomes
Probiotics are live microorganisms that offer health benefits, influencing the microbiota-gut-brain axis. Probiotics can improve cognitive functions, including learning and memory, by modulating the gut microbiota, reducing inflammation, and producing ...
Cassandra M. Flynn, Lara M. Blackburn, Qi Yuan   +2 more
doaj   +2 more sources

Variable Selection and Allocation in Joint Models via Gradient Boosting Techniques

Mathematics, 2023
Modeling longitudinal data (e.g., biomarkers) and the risk for events separately leads to a loss of information and bias, even though the underlying processes are related to each other.
Colin Griesbach, Andreas Mayr, Elisabeth Bergherr   +2 more
doaj   +1 more source

Learned Spatial Data Partitioning

Proceedings of the Sixth International Workshop on Exploiting Artificial Intelligence Techniques for Data Management, 2023
Due to the significant increase in the size of spatial data, it is essential to use distributed parallel processing systems to efficiently analyze spatial data. In this paper, we first study learned spatial data partitioning, which effectively assigns groups of big spatial data to computers based on locations of data by using machine learning ...
Keizo Hori, Yuya Sasaki 0001, Daichi Amagata, Yuki Murosaki, Makoto Onizuka   +4 more
openaire   +3 more sources

Adapting hippocampus multi-scale place field distributions in cluttered environments optimizes spatial navigation and learning

Frontiers in Computational Neuroscience, 2022
Extensive studies in rodents show that place cells in the hippocampus have firing patterns that are highly correlated with the animal's location in the environment and are organized in layers of increasing field sizes or scales along its dorsoventral ...
Pablo Scleidorovich, Jean-Marc Fellous, Alfredo Weitzenfeld   +2 more
doaj   +1 more source

The Spatial Learning Task of Lhermitte and Signoret (1972): Normative Data in Adults Aged 18–45

Frontiers in Psychology, 2022
ObjectiveThe Spatial Learning Task of Lhermitte and Signoret is an object-location arbitrary associative learning task. The task was originally developed to evaluate adults with severe amnesia.
Alana Collins, Michael M. Saling, Sarah J. Wilson, Graeme D. Jackson, Graeme D. Jackson, Chris Tailby, Chris Tailby   +6 more
doaj   +1 more source

Spatially Consistent Representation Learning [PDF]

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021
Self-supervised learning has been widely used to obtain transferrable representations from unlabeled images. Especially, recent contrastive learning methods have shown impressive performances on downstream image classification tasks. While these contrastive methods mainly focus on generating invariant global representations at the image-level under ...
Byungseok Roh, Wuhyun Shin, Ildoo Kim, Sungwoong Kim   +3 more
openaire   +3 more sources

Effective Data Management and Spatial Analytics

, 2021
This webinar introduced the implementations of effective data management and spatial analytics by academic and industry leaders of the field, and discuss the directions for further enhancement through integration with a cloud-based platform for research ...
Spatial Data Lab
core   +1 more source