A mathematical problem solved by Susanna Heikkilä relates to the classification of quasiregularly elliptic 4-manifolds, ...

I've long been fascinated by the fundamental mystery of our universe's origin. In my work, I explore an alternative to the ...

Machine learning has expanded beyond traditional Euclidean spaces in recent years, exploring representations in more complex geometric structures. Non-Euclidean representation learning is a growing ...

So we experimented to know whether AI giants—Deepseek and ChatGPT—can solve GATE 2024 questions. So here's what they ...

Add a description, image, and links to the euclidean-spaces topic page so that developers can more easily learn about it.

Although the telescope is just getting started on its mission, it has already delivered some impressive snapshots of the dark ...

Algorithms are developed to calculate the Euclidean distance spectra employing tree-search and A-star algorithm. The complexity of proposed algorithms are further reduced using trellis minimization.

This work is based on our paper DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes, which appeared at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.

Abstract: Euclidean distance transforms are fundamental in image processing and computer vision, with critical applications in medical image analysis and computer graphics. However, existing ...

Theoretical physicists James Hartle and Stephen Hawking, for example, proposed that rather than having a strict beginning as ...