Zobrazeno 1 - 10
of 217
pro vyhledávání: '"Nguyen Viet Cuong"'
Autor:
Nguyen Viet Cuong
Publikováno v:
E3S Web of Conferences, Vol 496, p 02001 (2024)
In this work, all-solution-processed resistive switching memory devices in a polymer blend are realised. The memory, in this work, is Write-Once-Read-Many memory (WORM). The polymer blend is the blend of Poly(3,4-ethylenedioxythiophene) Polystyrene S
Externí odkaz:
https://doaj.org/article/3dfc42088b2d4cbab25a1329834b8e1c
Autor:
Nguyen, Viet Cuong, Taher, Mohammad, Hong, Dongwan, Possobom, Vinicius Konkolics, Gopalakrishnan, Vibha Thirunellayi, Raj, Ekta, Li, Zihang, Soled, Heather J., Birnbaum, Michael L., Kumar, Srijan, De Choudhury, Munmun
The rapid evolution of Large Language Models (LLMs) offers promising potential to alleviate the global scarcity of mental health professionals. However, LLMs' alignment with essential mental health counseling competencies remains understudied. We int
Externí odkaz:
http://arxiv.org/abs/2410.22446
Autor:
Nguyen Viet Cuong
Publikováno v:
Human Geographies: Journal of Studies and Research in Human Geography, Vol 15, Iss 1, Pp 53-65 (2021)
Vietnam is among the most rapidly ageing countries in the world. Its ageing index of increased during the past 35 years. In 2019, the ageing index was 49% for the population aged 60 years and older and 33% for the population aged 65 and older. At the
Externí odkaz:
https://doaj.org/article/913670157e194d13a97748118681d3a1
Autor:
Nguyen, Viet Cuong, Jain, Mini, Chauhan, Abhijat, Soled, Heather Jaime, Lesmes, Santiago Alvarez, Li, Zihang, Birnbaum, Michael L., Tang, Sunny X., Kumar, Srijan, De Choudhury, Munmun
Over one in five adults in the US lives with a mental illness. In the face of a shortage of mental health professionals and offline resources, online short-form video content has grown to serve as a crucial conduit for disseminating mental health hel
Externí odkaz:
http://arxiv.org/abs/2407.02662
The rapid advancement of large language models (LLMs) necessitates the development of new benchmarks to accurately assess their capabilities. To address this need for Vietnamese, this work aims to introduce ViLLM-Eval, the comprehensive evaluation su
Externí odkaz:
http://arxiv.org/abs/2404.11086
Despite the ever-strong demand for mental health care globally, access to traditional mental health services remains severely limited expensive, and stifled by stigma and systemic barriers. Thus, over the last few years, young people are increasingly
Externí odkaz:
http://arxiv.org/abs/2304.07417
Molecular dynamics (MD) simulation, which is considered an important tool for studying physical and chemical processes at the atomic scale, requires accurate calculations of energies and forces. Although reliable energies and forces can be obtained b
Externí odkaz:
http://arxiv.org/abs/2111.05603
Autor:
Nguyen, Duong-Nguyen, Pham, Tien-Lam, Nguyen, Viet-Cuong, Kino, Hiori, Miyake, Takashi, Dam, Hieu-Chi
We propose a data-driven method to extract dissimilarity between materials, with respect to a given target physical property. The technique is based on an ensemble method with Kernel ridge regression as the predicting model; multiple random subset sa
Externí odkaz:
http://arxiv.org/abs/2008.08818
Autor:
Dam, Hieu Chi, Nguyen, Viet Cuong, Pham, Tien Lam, Nguyen, Anh Tuan, Terakura, Kiyoyuki, Miyake, Takashi, Kino, Hiori
We analyze Curie temperatures of rare-earth transition metal binary alloys with machine learning method. In order to select important descriptors and descriptor groups, we introduce newly developed subgroup relevance analysis and adopt the hierarchic
Externí odkaz:
http://arxiv.org/abs/1809.04750
Autor:
Nguyen, Duong-Nguyen, Pham, Tien-Lam, Nguyen, Viet-Cuong, Ho, Tuan-Dung, Tran, Truyen, Takahashi, Keisuke, Dam, Hieu-Chi
We developed a method for measuring the similarity between materials, focusing on specific physical properties. The obtained information can be utilized to understand the underlying mechanisms and to support the prediction of the physical properties
Externí odkaz:
http://arxiv.org/abs/1807.10751