نتایج جستجو برای: advisor

تعداد نتایج: 6763  

Journal: :NACADA Journal 1981

Journal: :International Journal of Computer Applications 2019

Journal: :SSRN Electronic Journal 2017

Journal: :Southeast Asian Review of English 2019

Journal: :Open Forum Infectious Diseases 2022

Abstract Background We have previously reported on a US based national surveillance study from 6 geographically dispersed medical centers for the susceptibility and epidemiology of C. difficile isolates. This current survey was conducted with isolates collected in 2020-21 specific attention to ridinilazole as well nine comparators. A summary susceptibilities 300 tested against 10 antimicrobial ...

Journal: :Open Forum Infectious Diseases 2022

Abstract Background The incidence of carbapenem-resistant organisms (CROs) has increased over the past 3 decades. Carbapenem-resistance due to metallo-β-lactamases (MBLs) such as Verona integron-encoded metallo-β-lactamase (VIM) are particularly problematic limited treatment options. We describe a multi-species outbreak VIM-producing CROs (VIM CROs) in tertiary care hospital along with our expe...

Journal: :Open Forum Infectious Diseases 2022

Abstract Background Several new β-lactam antibiotics were recently developed for treatment of serious infections due to gram-negative bacteria (GNB) with difficult-to-treat resistance (DTR). This multicenter retrospective cohort study examined temporal trends in prevalence GNB DTR and the use β-lactams southeastern United States. Methods The DTR, including Enterobacterales, Pseudomonas aerugino...

1999
Yasushi Ogawa

NTCIR is one of the rst projects to construct, using a TREC-like contest operation, a practical Japanese IR test collection. This report discusses design and the administration issues, such as the size and genre of the collection, the description of topics, tasks, the pre-test, and the collaboration with the IREX project.

1973
Ahmed F. M. Mustafa

Journal: :CoRR 2017
Romain Laroche Mehdi Fatemi Joshua Romoff Harm van Seijen

This article deals with a novel branch of Separation of Concerns, called Multi-Advisor Reinforcement Learning (MAd-RL), where a single-agent RL problem is distributed to n learners, called advisors. Each advisor tries to solve the problem with a different focus. Their advice is then communicated to an aggregator, which is in control of the system. For the local training, three off-policy bootst...

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