Zobrazeno 1 - 10
of 4 434
pro vyhledávání: '"Nguyen, Vu A."'
Magnetic anisotropy slows down magnetic relaxation and plays a prominent role in the design of permanent magnets. Coordination compounds of Co(II) in particular exhibit large magnetic anisotropy in the presence of low-coordination environments and ha
Externí odkaz:
http://arxiv.org/abs/2409.04418
Regression testing of software is a crucial but time-consuming task, especially in the context of user interface (UI) testing where multiple microservices must be validated simultaneously. Test case prioritization (TCP) is a cost-efficient solution t
Externí odkaz:
http://arxiv.org/abs/2408.00705
The ubiquity of missing data has sparked considerable attention and focus on tabular data imputation methods. Diffusion models, recognized as the cutting-edge technique for data generation, demonstrate significant potential in tabular data imputation
Externí odkaz:
http://arxiv.org/abs/2407.18013
API testing has increasing demands for software companies. Prior API testing tools were aware of certain types of dependencies that needed to be concise between operations and parameters. However, their approaches, which are mostly done manually or u
Externí odkaz:
http://arxiv.org/abs/2407.10227
Autor:
Mariano, Lorenzo A., Nguyen, Vu Ha Anh, Petersen, Jonatan B., Björnsson, Magnus, Bendix, Jesper, Eaton, Gareth R., Eaton, Sandra S., Lunghi, Alessandro
Magnetic resonance is a prime method for the study of chemical and biological structures and their dynamical processes. The interpretation of these experiments relies on considering the spin of electrons as the sole relevant degree of freedom. By app
Externí odkaz:
http://arxiv.org/abs/2407.01380
Autor:
Hai, Vu Tuan, Viet, Nguyen Tan, Urbaneja, Jesus, Linh, Nguyen Vu, Tran, Lan Nguyen, Ho, Le Bin
Publikováno v:
Machine Learning: Science and Technology (2024)
Quantum compilation is the process of converting a target unitary operation into a trainable unitary represented by a quantum circuit. It has a wide range of applications, including gate optimization, quantum-assisted compiling, quantum state prepara
Externí odkaz:
http://arxiv.org/abs/2407.01010
Selecting suitable data for training machine learning models is crucial since large, web-scraped, real datasets contain noisy artifacts that affect the quality and relevance of individual data points. These artifacts will impact the performance and g
Externí odkaz:
http://arxiv.org/abs/2406.01130
Classification with rejection emerges as a learning paradigm which allows models to abstain from making predictions. The predominant approach is to alter the supervised learning pipeline by augmenting typical loss functions, letting model rejection i
Externí odkaz:
http://arxiv.org/abs/2405.18686
Publikováno v:
Transactions on Machine Learning Research 2024
Bayesian Optimization (BO) is an effective method for finding the global optimum of expensive black-box functions. However, it is well known that applying BO to high-dimensional optimization problems is challenging. To address this issue, a promising
Externí odkaz:
http://arxiv.org/abs/2402.03104
Autor:
Tuyen Nguyen‐Duc, Huu Vu‐Xuan‐Son, Hieu Do‐Dinh, Nam Nguyen‐Vu‐Nhat, Goro Fujita, Son Tran‐Thanh
Publikováno v:
IET Renewable Power Generation, Vol 18, Iss 14, Pp 2589-2604 (2024)
Abstract The advancement of Photovoltaic technology has undergone rapid acceleration in recent years. Nonetheless, the most significant drawback of Photovoltaic is its intermittence, making it an obvious source of power fluctuation. This study propos
Externí odkaz:
https://doaj.org/article/10f6ddcbe88a4d14be611346dd0cd721
