Abstract: The ability to accurately estimate job runtime properties allows a scheduler to effectively schedule jobs. State-of-the-art online cluster job schedulers use history-based learning, which ...

This issue proposes the creation of an extensive and well-organized examples gallery for the scikit-sampling library. Currently, the usage examples are limited. A comprehensive gallery will ...

Reading through issues and source, sampling seems to be supported but the documentation in the repo's root doesn't mention "sampling" explicitly Adding examples for these would make it more apparent ...

Abstract: Federated learning (FL) is an innovative privacy-preserving machine learning paradigm that enables clients to train a global model without sharing their local data. However, the coexistence ...

Helen Branswell covers issues broadly related to infectious diseases, including outbreaks, preparedness, research, and vaccine development. Follow her on Mastodon and Bluesky. You can reach Helen on ...

Cluster sampling divides a population into smaller clusters, simplifying large-scale research. Cluster sampling is a probability sampling method where researchers divide a population into smaller ...

ABSTRACT: This paper introduces the principle of PPS-based adaptive cluster sampling method and its modified HH estimator and HT estimator calculation method. It compares PPS-based adaptive cluster ...

ABSTRACT: This paper introduces the principle of PPS-based adaptive cluster sampling method and its modified HH estimator and HT estimator calculation method. It compares PPS-based adaptive cluster ...