Zobrazeno 1 - 10
of 2 755
pro vyhledávání: '"Omar, Mohamed A."'
Autor:
Omar, Mohamed, Troyka, Justin M.
Given a set of $I \subseteq \mathbb{N}$, consider the sequences $\{d_n(I)\},\{p_n(I)\}$ where for any $n$, $d_n(I)$ and $p_n(I)$ respectively count the number of permutations in the symmetric group $\mathfrak{S}_n$ whose descent set (respectively pea
Externí odkaz:
http://arxiv.org/abs/2407.12719
Autor:
Omar, Mohamed
Motivated by the work of Grinberg and Stanley, we investigate the enumeration of permutations with a given $X$-descent set, a generalization of the classical notion of a descent set. Our work expands on recent developments of Diaz-Lopez, Harris, Insk
Externí odkaz:
http://arxiv.org/abs/2402.10443
Autor:
Ataiefard, Foozhan, Ahmed, Walid, Hajimolahoseini, Habib, Asani, Saina, Javadi, Farnoosh, Hassanpour, Mohammad, Awad, Omar Mohamed, Wen, Austin, Liu, Kangling, Liu, Yang
Vision transformers are known to be more computationally and data-intensive than CNN models. These transformer models such as ViT, require all the input image tokens to learn the relationship among them. However, many of these tokens are not informat
Externí odkaz:
http://arxiv.org/abs/2401.15293
Autor:
Javadi, Farnoosh, Ahmed, Walid, Hajimolahoseini, Habib, Ataiefard, Foozhan, Hassanpour, Mohammad, Asani, Saina, Wen, Austin, Awad, Omar Mohamed, Liu, Kangling, Liu, Yang
Massive transformer-based models face several challenges, including slow and computationally intensive pre-training and over-parametrization. This paper addresses these challenges by proposing a versatile method called GQKVA, which generalizes query,
Externí odkaz:
http://arxiv.org/abs/2311.03426
Autor:
Awad, Omar Mohamed, Hajimolahoseini, Habib, Lim, Michael, Gosal, Gurpreet, Ahmed, Walid, Liu, Yang, Deng, Gordon
This paper presents our proposed approach that won the first prize at the ICLR competition on Hardware Aware Efficient Training. The challenge is to achieve the highest possible accuracy in an image classification task in less than 10 minutes. The tr
Externí odkaz:
http://arxiv.org/abs/2309.03965
Autor:
Almehizia Abdulrahman A., Al-Omar Mohamed A., Al-Obaid Abdulrahman M., Naglah Ahmed M., Bhat Mashooq A., Ghabbour Hazem A., Khatab Tamer K., Hassan Ashraf S.
Publikováno v:
Polish Journal of Chemical Technology, Vol 26, Iss 3, Pp 63-69 (2024)
In reaction to the expanding predominance of diabetes mellitus, curcumin nanoparticles stacked on carboxymethyl cellulose (CMC) composite were effectively synthesized, characterized, and examined utilizing UV/Vis and FTIR spectroscopy combined with t
Externí odkaz:
https://doaj.org/article/9e43792f46ef47caa90a8e19eeb9be9a
Text-to-video retrieval systems have recently made significant progress by utilizing pre-trained models trained on large-scale image-text pairs. However, most of the latest methods primarily focus on the video modality while disregarding the audio si
Externí odkaz:
http://arxiv.org/abs/2307.12964
Autor:
Reham Abdel Haleem Abo El Wafa, Mohamed Ibrahim Sayed Ahmed, Mona Wagdy Ayad, Omar Mohamed Ghallab, Dalia Mohamed Tarek Farghaly
Publikováno v:
Alexandria Journal of Medicine, Vol 60, Iss 1, Pp 301-311 (2024)
Background Acute myeloid leukemia (AML) is a challenging heterogenous hematologic malignancy characterized by suboptimal outcomes. Genetic characterization of AML enhanced the understanding of individualized patient’s risk and led to the developmen
Externí odkaz:
https://doaj.org/article/6deeb67ecef648428c526baf147f5dcb
Effective modeling of complex spatiotemporal dependencies in long-form videos remains an open problem. The recently proposed Structured State-Space Sequence (S4) model with its linear complexity offers a promising direction in this space. However, we
Externí odkaz:
http://arxiv.org/abs/2303.14526
Autor:
Zhu, Wentao, Omar, Mohamed
Audio event has a hierarchical architecture in both time and frequency and can be grouped together to construct more abstract semantic audio classes. In this work, we develop a multiscale audio spectrogram Transformer (MAST) that employs hierarchical
Externí odkaz:
http://arxiv.org/abs/2303.10757