Zobrazeno 1 - 10
of 4 088
pro vyhledávání: '"Chi Hua"'
Autor:
Frank J. Ambrosio, Michelle R. Scribner, Sage M. Wright, James R. Otieno, Emma L. Doughty, Andrew Gorzalski, Danielle Denise Siao, Steve Killian, Chi Hua, Emily Schneider, Michael Tran, Vici Varghese, Kevin G. Libuit, Mark Pandori, Joel R. Sevinsky, David Hess
Publikováno v:
Frontiers in Public Health, Vol 11 (2023)
IntroductionThe clinical incidence of antimicrobial-resistant fungal infections has dramatically increased in recent years. Certain fungal pathogens colonize various body cavities, leading to life-threatening bloodstream infections. However, the iden
Externí odkaz:
https://doaj.org/article/58b6f6a7664c41a1ac2f3663aeb43c37
Autor:
Andrew Gorzalski, Frank J. Ambrosio, Lauryn Massic, Michelle R. Scribner, Danielle Denise Siao, Chi Hua, Phillip Dykema, Emily Schneider, Chidinma Njoku, Kevin Libuit, Joel R. Sevinsky, Stephanie Van Hooser, Mark Pandori, David Hess
Publikováno v:
Frontiers in Public Health, Vol 11 (2023)
A Candida auris outbreak has been ongoing in Southern Nevada since August 2021. In this manuscript we describe the sequencing of over 200 C. auris isolates from patients at several facilities. Genetically distinct subgroups of C. auris were detected
Externí odkaz:
https://doaj.org/article/d476c96b47524fb0864e4d6758d6790d
The evaluation of synthetic data generation is crucial, especially in the retail sector where data accuracy is paramount. This paper introduces a comprehensive framework for assessing synthetic retail data, focusing on fidelity, utility, and privacy.
Externí odkaz:
http://arxiv.org/abs/2406.13130
The promise of tabular generative models is to produce realistic synthetic data that can be shared and safely used without dangerous leakage of information from the training set. In evaluating these models, a variety of methods have been proposed to
Externí odkaz:
http://arxiv.org/abs/2406.13012
Autor:
Wang, Chi-Hua, Cheng, Guang
We present BadGD, a unified theoretical framework that exposes the vulnerabilities of gradient descent algorithms through strategic backdoor attacks. Backdoor attacks involve embedding malicious triggers into a training dataset to disrupt the model's
Externí odkaz:
http://arxiv.org/abs/2405.15979
With the proliferation of generative AI and the increasing volume of generative data (also called as synthetic data), assessing the fidelity of generative data has become a critical concern. In this paper, we propose a discriminative approach to esti
Externí odkaz:
http://arxiv.org/abs/2405.15337
Publikováno v:
IEEE Access, Vol 8, Pp 198730-198739 (2020)
Water quality monitoring using Wireless Sensor Networks (WSNs) is essential in aquaculture water quality management. In the field of water quality monitoring, dissolved oxygen (DO) is a key parameter, and its prediction can provide decision support f
Externí odkaz:
https://doaj.org/article/4173606a22e5466fa8475e44b1c9ae28
Devising procedures for downstream task-oriented generative model selections is an unresolved problem of practical importance. Existing studies focused on the utility of a single family of generative models. They provided limited insights on how synt
Externí odkaz:
http://arxiv.org/abs/2401.00974
Exploring generative model training for synthetic tabular data, specifically in sequential contexts such as credit card transaction data, presents significant challenges. This paper addresses these challenges, focusing on attaining both high fidelity
Externí odkaz:
http://arxiv.org/abs/2401.00965