Zobrazeno 1 - 10
of 9 198
pro vyhledávání: '"TRAN VAN ON"'
Autor:
Liu, Shang-Ching, Tran, Van Nhiem, Chen, Wenkai, Cheng, Wei-Lun, Huang, Yen-Lin, Liao, I-Bin, Li, Yung-Hui, Zhang, Jianwei
Affordance understanding, the task of identifying actionable regions on 3D objects, plays a vital role in allowing robotic systems to engage with and operate within the physical world. Although Visual Language Models (VLMs) have excelled in high-leve
Externí odkaz:
http://arxiv.org/abs/2410.11564
Autor:
Sarwar, Zain, Tran, Van, Bhagoji, Arjun Nitin, Feamster, Nick, Zhao, Ben Y., Chakraborty, Supriyo
Machine learning (ML) models often require large amounts of data to perform well. When the available data is limited, model trainers may need to acquire more data from external sources. Often, useful data is held by private entities who are hesitant
Externí odkaz:
http://arxiv.org/abs/2410.08432
Autor:
Brubaker, Ben, Dasher, A. Suki, Hu, Michael, Jain, Nupur, Li, Yifan, Lin, Yi, Mihaila, Maria, Tran, Van, Ünel, I. Deniz
We use algebraic methods in statistical mechanics to represent a multi-parameter class of polynomials in severable variables as partition functions of a new family of solvable lattice models. The class of polynomials, defined by A.N. Kirillov, is der
Externí odkaz:
http://arxiv.org/abs/2410.07960
Autor:
Fan, Yi-Zhong, Li, Yao-Yu, Lu, Chih-Ting, Luo, Xiao-Yi, Tang, Tian-Peng, Tran, Van Que, Tsai, Yue-Lin Sming
The precision measurements of the muon magnetic moment and the $W$ boson mass have sparked interest in the potential deviations from standard model (SM) predictions. While it may be premature to attribute any excesses in these precision measurements
Externí odkaz:
http://arxiv.org/abs/2410.00638
Employing effective field theory techniques, we advance computations of thermal parameters that enter predictions for the gravitational wave spectra from first-order electroweak phase transitions. Working with the real-singlet-extended Standard Model
Externí odkaz:
http://arxiv.org/abs/2409.17554
Autor:
Tran, Van Hong, Mehrotra, Aarushi, Sharma, Ranya, Chetty, Marshini, Feamster, Nick, Frankenreiter, Jens, Strahilevitz, Lior
To protect consumer privacy, the California Consumer Privacy Act (CCPA) mandates that businesses provide consumers with a straightforward way to opt out of the sale and sharing of their personal information. However, the control that businesses enjoy
Externí odkaz:
http://arxiv.org/abs/2409.09222
The advancements in generative AI have enabled the improvement of audio synthesis models, including text-to-speech and voice conversion. This raises concerns about its potential misuse in social manipulation and political interference, as synthetic s
Externí odkaz:
http://arxiv.org/abs/2409.07390
Autor:
Tran, Van Que, Yuan, Tzu-Chiang
We explore a novel scenario involving Abelian-non-Abelian kinetic mixing within the framework of the Standard Model Effective Field Theory (SMEFT) and its extension with a real triplet scalar field. In SMEFT, this mixing arises exclusively from a dim
Externí odkaz:
http://arxiv.org/abs/2408.11626
We investigate the possibility of a strong first-order electroweak phase transition during the early universe within the framework of the gauged two-Higgs doublet model (G2HDM) and explore its detectability through stochastic gravitational wave signa
Externí odkaz:
http://arxiv.org/abs/2408.05167
Autor:
Tran, Van Duy, Le, Tran Xuan Hieu, Tran, Thi Diem, Pham, Hoai Luan, Le, Vu Trung Duong, Vu, Tuan Hai, Nguyen, Van Tinh, Nakashima, Yasuhiko
Kolmogorov-Arnold Networks (KANs), a novel type of neural network, have recently gained popularity and attention due to the ability to substitute multi-layer perceptions (MLPs) in artificial intelligence (AI) with higher accuracy and interoperability
Externí odkaz:
http://arxiv.org/abs/2407.17790