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 ...
Abstract: Euclidean distance transforms are fundamental in image processing and computer vision, with critical applications in medical image analysis and computer graphics. However, existing ...
In this paper, the notion of equitable partitions (EP) is used to study the eigenvalues of Euclidean distance matrices (EDMs). In particular, EP is used to obtain the characteristic polynomials of ...
KEYWORDS: Vehicle Routing Problem (VRP), Google Maps Distance Matrix API, Python, Pulp, Mixed Integer Linear Programming, Transportation, Optimisation Problem, Time Window, Mathematical Modelling ...
Background: In-feed antibiotic growth promoters (AGPs) have been a cornerstone in the livestock industry due to their role in enhancing growth and feed efficiency. However, concerns over antibiotic ...
Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Plastic pollution poses a significant and persistent threat to the health and ...
ABSTRACT: Purpose: This study describes a machine-learning approach utilizing patients' anatomical changes to predict parotid mean dose changes in fractionated radiotherapy for head-and-neck cancer, ...
Discover how nvmath-python leverages NVIDIA CUDA-X math libraries for high-performance matrix operations, optimizing deep learning tasks with epilog fusion, as detailed by Szymon Karpiński.
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