Zobrazeno 1 - 10
of 1 471
pro vyhledávání: '"Tran Lam"'
Autor:
Murni Po, David J. Pannell, Iain Walker, Sorada Tapsuwan, Fiona Dempster, Daniel S. Mendham, Chris Beadle, Tran Lam Dong, Anh Hai Tran, Hanh Le Thi, Dang Thi Hai Ha
Publikováno v:
Trees, Forests and People, Vol 12, Iss , Pp 100384- (2023)
Acacia plantations are a significant forestry resource in Viet Nam, with the majority of the area under smallholder ownership, typically with 1–5 ha per household. Currently most acacias are grown in short rotations and sold for export as woodchips
Externí odkaz:
https://doaj.org/article/4ce03975cc334ce98fd5c463ce0c49a0
Prior Unsupervised Domain Adaptation (UDA) methods often aim to train a domain-invariant feature extractor, which may hinder the model from learning sufficiently discriminative features. To tackle this, a line of works based on prompt learning levera
Externí odkaz:
http://arxiv.org/abs/2406.09353
Publikováno v:
Journal of Student Research, Volume 10, Issue 1, 2021
This paper examines the association between police drug seizures and drug overdose deaths in Ohio from 2014 to 2018. We use linear regression, ARIMA models, and categorical data analysis to quantify the effect of drug seizure composition and weight o
Externí odkaz:
http://arxiv.org/abs/2405.19199
Autor:
Yu-Chia Hsieh, Shi-Heng Wang, Yi-Yin Chen, Tzu-Lung Lin, Shian-Sen Shie, Ching-Tai Huang, Chen-Hsiang Lee, Yi-Ching Chen, Tran Lam Tu Quyen, Yi-Jiun Pan
Publikováno v:
Emerging Microbes and Infections, Vol 9, Iss 1, Pp 2094-2104 (2020)
ABSTRACTAcinetobacter baumannii emerged as one of the most important pathogens that causes nosocomial infections due to its increased multidrug resistance. Identifying capsular epidemiology in A. baumannii can aid in the development of effective trea
Externí odkaz:
https://doaj.org/article/e83b9d99cd0041ec8a11b1b878ce07b4
Autor:
Carlotto Alice, Sayginer Osman, Szczurek Anna, Tran Lam T. N., Dell’Anna Rossana, Varas Stefano, Babiarczuk Bartosz, Krzak Justyna, Bursi Oreste S., Zonta Daniele, Lukowiak Anna, Righini Giancarlo C., Ferrari Maurizio, Pietralunga Silvia M., Chiasera Alessandro
Publikováno v:
EPJ Web of Conferences, Vol 266, p 06003 (2022)
Flexible SiO2/HfO2 1D photonic crystals and active SiO2–HfO2:Er3+ all-glass flexible planar waveguides fabricated by radio frequency sputtering, are presented. The 1D photonic crystals show a strong dependence of the optical features on the light i
Externí odkaz:
https://doaj.org/article/c81d7ac1d7994456beba12a366241695
Autor:
Tran, Ngoc N., Tran, Lam, Phan, Hoang, Bui, Anh, Pham, Tung, Tran, Toan, Phung, Dinh, Le, Trung
Contrastive learning (CL) is a self-supervised training paradigm that allows us to extract meaningful features without any label information. A typical CL framework is divided into two phases, where it first tries to learn the features from unlabelle
Externí odkaz:
http://arxiv.org/abs/2311.09671
Autor:
Yu-Chia Hsieh, Jia-Wen Wu, Yi-Yin Chen, Tran Lam Tu Quyen, Wei-Chao Liao, Shiao-Wen Li, Yin-Cheng Chen, Yi-Jiun Pan
Publikováno v:
Antibiotics, Vol 10, Iss 10, p 1239 (2021)
Dissemination of multidrug-resistant, particularly tigecycline-resistant, Acinetobacter baumannii is of critical importance, as tigecycline is considered a last-line antibiotic. Acquisition of tet(X), a tigecycline-inactivating enzyme mostly found in
Externí odkaz:
https://doaj.org/article/2bb6c6be5b684afab7cfe95fc10ebef6
Unsupervised domain adaptation (UDA) refers to a domain adaptation framework in which a learning model is trained based on the labeled samples on the source domain and unlabelled ones in the target domain. The dominant existing methods in the field t
Externí odkaz:
http://arxiv.org/abs/2305.18458
Text password has served as the most popular method for user authentication so far, and is not likely to be totally replaced in foreseeable future. Password authentication offers several desirable properties (e.g., low-cost, highly available, easy-to
Externí odkaz:
http://arxiv.org/abs/2212.08796
Multi-Task Learning (MTL) is a widely-used and powerful learning paradigm for training deep neural networks that allows learning more than one objective by a single backbone. Compared to training tasks separately, MTL significantly reduces computatio
Externí odkaz:
http://arxiv.org/abs/2211.13723