Zobrazeno 1 - 10
of 35 425
pro vyhledávání: '"Cuong To"'
Autor:
Jennifer N. Chousal, Robert Morey, Srimeenakshi Srinivasan, Katherine Lee, Wei Zhang, Ana Lisa Yeo, Cuong To, Kyucheol Cho, V. Gabriel Garzo, Mana M. Parast, Louise C. Laurent, Heidi Cook-Andersen
Publikováno v:
Cell Reports, Vol 43, Iss 2, Pp 113701- (2024)
Summary: Human embryo implantation is remarkably inefficient, and implantation failure remains among the greatest obstacles in treating infertility. Gene expression data from human embryos have accumulated rapidly in recent years; however, identifica
Externí odkaz:
https://doaj.org/article/13df70b330c94714a5a518c7f339944c
Autor:
Vuong, Tuan-Cuong, Nguyen, Cong Chi, Pham, Van-Cuong, Le, Thi-Thanh-Huyen, Tran, Xuan-Nam, Van Luong, Thien
Publikováno v:
NOLTA 2024
This paper proposes a novel intrusion detection method for unmanned aerial vehicles (UAV) in the presence of recent actual UAV intrusion dataset. In particular, in the first stage of our method, we design an autoencoder architecture for effectively e
Externí odkaz:
http://arxiv.org/abs/2410.02827
Autor:
Le, Cuong Chi, Truong-Vinh, Hoang-Chau, Phan, Huy Nhat, Le, Dung Duy, Nguyen, Tien N., Bui, Nghi D. Q.
Predicting program behavior and reasoning about code execution remain significant challenges in software engineering, particularly for large language models (LLMs) designed for code analysis. While these models excel at understanding static syntax, t
Externí odkaz:
http://arxiv.org/abs/2410.23402
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
We analyze the error rates of the Hamiltonian Monte Carlo algorithm with leapfrog integrator for Bayesian neural network inference. We show that due to the non-differentiability of activation functions in the ReLU family, leapfrog HMC for networks wi
Externí odkaz:
http://arxiv.org/abs/2410.22065
Autor:
Manh, Cuong Tran, Vo, Hieu Dinh
While smart contracts are foundational elements of blockchain applications, their inherent susceptibility to security vulnerabilities poses a significant challenge. Existing training datasets employed for vulnerability detection tools may be limited,
Externí odkaz:
http://arxiv.org/abs/2410.21685
Publikováno v:
CIKM MMSR 2024
Large Language Models (LLMs) have demonstrated potential as effective search relevance evaluators. However, there is a lack of comprehensive guidance on which models consistently perform optimally across various contexts or within specific use cases.
Externí odkaz:
http://arxiv.org/abs/2410.19974
Autor:
Le, Bang Giang, Ta, Viet Cuong
In this work, we study the problem of finding Pareto optimal policies in multi-agent reinforcement learning problems with cooperative reward structures. We show that any algorithm where each agent only optimizes their reward is subject to suboptimal
Externí odkaz:
http://arxiv.org/abs/2410.19372
We perform a systematic study of inelastic nuclear rainbow scattering for the \oc system to the 2$^+$ (4.44 MeV) state of $^{12}$C at incident energies of 100--608 MeV with the coupled-channels method. The recently generalized nearside-farside decomp
Externí odkaz:
http://arxiv.org/abs/2410.13234
Continual Event Detection (CED) poses a formidable challenge due to the catastrophic forgetting phenomenon, where learning new tasks (with new coming event types) hampers performance on previous ones. In this paper, we introduce a novel approach, Lif
Externí odkaz:
http://arxiv.org/abs/2410.08905