Zobrazeno 1 - 10
of 24 204
pro vyhledávání: '"A Youl"'
Autor:
Pasini, Massimiliano Lupo, Choi, Jong Youl, Mehta, Kshitij, Zhang, Pei, Rogers, David, Bae, Jonghyun, Ibrahim, Khaled Z., Aji, Ashwin M., Schulz, Karl W., Polo, Jorda, Balaprakash, Prasanna
We present our work on developing and training scalable, trustworthy, and energy-efficient predictive graph foundation models (GFMs) using HydraGNN, a multi-headed graph convolutional neural network architecture. HydraGNN expands the boundaries of gr
Externí odkaz:
http://arxiv.org/abs/2406.12909
We develop an effective and methodical algorithm for the construction of general covariant four-point $H\ell\ell Z$ vertices, accommodating leptons $\ell=e, \mu$, and designed to handle a boson $H$ of any integer spin, not merely confined to spins up
Externí odkaz:
http://arxiv.org/abs/2405.19167
Ensuring extremely high reliability is essential for channel coding in 6G networks. The next-generation of ultra-reliable and low-latency communications (xURLLC) scenario within 6G networks requires a frame error rate (FER) below 10-9. However, low-d
Externí odkaz:
http://arxiv.org/abs/2405.13413
Error correcting codes (ECCs) are indispensable for reliable transmission in communication systems. The recent advancements in deep learning have catalyzed the exploration of ECC decoders based on neural networks. Among these, transformer-based neura
Externí odkaz:
http://arxiv.org/abs/2405.01033
Autor:
Wang, Xiao, Liu, Siyan, Tsaris, Aristeidis, Choi, Jong-Youl, Aji, Ashwin, Fan, Ming, Zhang, Wei, Yin, Junqi, Ashfaq, Moetasim, Lu, Dan, Balaprakash, Prasanna
Earth system predictability is challenged by the complexity of environmental dynamics and the multitude of variables involved. Current AI foundation models, although advanced by leveraging large and heterogeneous data, are often constrained by their
Externí odkaz:
http://arxiv.org/abs/2404.14712
Autor:
Tsaris, Aristeidis, Zhang, Chengming, Wang, Xiao, Yin, Junqi, Liu, Siyan, Ashfaq, Moetasim, Fan, Ming, Choi, Jong Youl, Wahib, Mohamed, Lu, Dan, Balaprakash, Prasanna, Wang, Feiyi
Vision Transformers (ViTs) are pivotal for foundational models in scientific imagery, including Earth science applications, due to their capability to process large sequence lengths. While transformers for text has inspired scaling sequence lengths i
Externí odkaz:
http://arxiv.org/abs/2405.15780
We conduct a combined analysis to investigate dark matter (DM) with hypercharge anapole moments, focusing on scenarios where Majorana DM particles with spin 1/2, 1, 3/2, and 2 interact exclusively with Standard Model particles through U(1)$_{Y}$ hype
Externí odkaz:
http://arxiv.org/abs/2401.02855
Machine learning (ML) techniques and atomistic modeling have rapidly transformed materials design and discovery. Specifically, generative models can swiftly propose promising materials for targeted applications. However, the predicted properties of m
Externí odkaz:
http://arxiv.org/abs/2311.05407
Low-density parity-check (LDPC) codes have been successfully commercialized in communication systems due to their strong error correction capabilities and simple decoding process. However, the error-floor phenomenon of LDPC codes, in which the error
Externí odkaz:
http://arxiv.org/abs/2310.07194
Publikováno v:
European Physical Journal C: Particles and Fields, Vol 84, Iss 10, Pp 1-27 (2024)
Abstract We develop an effective and methodical algorithm for the construction of general covariant four-point $$H\ell \ell Z$$ H ℓ ℓ Z vertices, accommodating leptons $$\ell =e, \mu $$ ℓ = e , μ , and designed to handle a boson H of any integ
Externí odkaz:
https://doaj.org/article/77d69892502242cd95857d4df8bac96a
