Huahu Xu

Clinical and cost outcomes of pre-emptive plerixafor administration in patients with multiple myeloma undergoing stem cell mobilization Author links open overlay panelLeslie A. Andritsos a , Ying Huang b , Ivo Abraham c d e f , Keith Huff g , Scott R. Scrape h , Tao Fan i , Nimer Alkhatib c , Craig C. Hofmeister j , Edward Drea i , Ali McBride d e k Show more Highlights • Plerixafor is approved for CD34+ cell mobilization, but cost considerations limit its routine use.

Loading metrics Open Access Peer-reviewed Research Article Citation: Zhang H, Zhou Y, Xu H, Shi J, Lin X, Gao Y (2025) Anomaly detection in virtual machine logs against irrelevant attribute interference. PLoS ONE 20(1): e0315897. https://doi.org/10.1371/journal.pone.0315897 Editor: Arne Johannssen, University of Hamburg: Universitat Hamburg, GERMANY Received: June 23, 2024; Accepted: December 3, 2024; Published: January 7, 2025 Copyright: © 2025 Zhang et al.

1. Introduction Deep learning-based object detection algorithms rely on large amounts of training data to achieve excellent performance [1,2,3,4]. In real-world scenarios, such as in the medical and aviation fields, there is a problem of scarce sample data. With a restricted sample size, existing deep learning algorithms are not very effective when it comes to object detection. In contrast, humans can learn new knowledge quickly with very few sample examples.