Zobrazeno 1 - 10
of 35 294
pro vyhledávání: '"A. Kaveh"'
Autor:
Yang, Liu, Paischer, Fabian, Hassani, Kaveh, Li, Jiacheng, Shao, Shuai, Li, Zhang Gabriel, He, Yun, Feng, Xue, Noorshams, Nima, Park, Sem, Long, Bo, Nowak, Robert D, Gao, Xiaoli, Eghbalzadeh, Hamid
Sequential dense retrieval models utilize advanced sequence learning techniques to compute item and user representations, which are then used to rank relevant items for a user through inner product computation between the user and all item representa
Externí odkaz:
http://arxiv.org/abs/2411.18814
Software performance modeling plays a crucial role in developing and maintaining software systems. A performance model analytically describes the relationship between the performance of a system and its runtime activities. This process typically exam
Externí odkaz:
http://arxiv.org/abs/2411.17548
Investigating the microscopic details of the proximity effect is crucial for both key experimental applications and fundamental inquiries into nanoscale devices featuring superconducting elements. In this work, we develop a framework motivated by exp
Externí odkaz:
http://arxiv.org/abs/2411.12733
Autor:
Ghadi, Farshad Rostami, Wong, Kai-Kit, Kaveh, Masoud, Xu, H., New, W. K., Lopez-Martinez, F. Javier, Shin, Hyundong
Fluid antenna system (FAS) is gaining attention as an innovative technology for boosting diversity and multiplexing gains. As a key innovation, it presents the possibility to overcome interference by position reconfigurability on one radio frequency
Externí odkaz:
http://arxiv.org/abs/2410.20930
The Martian isotopic record displays a dichotomy in volatile compositions. Interior volatiles from the mantle record a chondritic heritage (e.g., H, N, Kr, Xe) whereas the atmospheric reservoir of Kr and Xe - which do not currently experience escape
Externí odkaz:
http://arxiv.org/abs/2410.15508
Autor:
Wang, Limei, Hassani, Kaveh, Zhang, Si, Fu, Dongqi, Yuan, Baichuan, Cong, Weilin, Hua, Zhigang, Wu, Hao, Yao, Ning, Long, Bo
Transformers serve as the backbone architectures of Foundational Models, where a domain-specific tokenizer helps them adapt to various domains. Graph Transformers (GTs) have recently emerged as a leading model in geometric deep learning, outperformin
Externí odkaz:
http://arxiv.org/abs/2410.13798
Large Language Models (LLMs), despite achieving state-of-the-art results in a number of evaluation tasks, struggle to maintain their performance when logical reasoning is strictly required to correctly infer a prediction. In this work, we propose Arg
Externí odkaz:
http://arxiv.org/abs/2410.12997
Autor:
Xu, Zhe, Hassani, Kaveh, Zhang, Si, Zeng, Hanqing, Yasunaga, Michihiro, Wang, Limei, Fu, Dongqi, Yao, Ning, Long, Bo, Tong, Hanghang
Language Models (LMs) are increasingly challenging the dominance of domain-specific models, including Graph Neural Networks (GNNs) and Graph Transformers (GTs), in graph learning tasks. Following this trend, we propose a novel approach that empowers
Externí odkaz:
http://arxiv.org/abs/2410.02296
Publikováno v:
مهندسی عمران شریف, Vol 37.2, Iss 1.1, Pp 71-84 (2021)
Engineering structures are prone to damage over their service life as a result of natural disaster so that damage spreading may lead to many casualties. In order to prevent these catastrophic events, early damage detection must be carried out. By con
Externí odkaz:
https://doaj.org/article/13fa8f5166b5482a9b7b9d10aad5e1f6
Backscatter communication (BC) emerges as a pivotal technology for ultra-low-power energy harvesting applications, but its practical deployment is often hampered by notable security vulnerabilities. Physical layer authentication (PLA) offers a promis
Externí odkaz:
http://arxiv.org/abs/2410.02778