Zobrazeno 1 - 10
of 9 163
pro vyhledávání: '"USMAN, MUHAMMAD"'
Rank aggregation techniques play a crucial role in identifying the best alternatives, but the inherent imbalance and incompleteness of real-world data pose significant challenges. The HodgeRank algorithm, which utilizes discrete exterior calculus on
Externí odkaz:
http://arxiv.org/abs/2407.20452
Autor:
Usman, Muhammad, Qadir, Asghar
Publikováno v:
Proceedings of the Fifteenth Marcel Grossmann Meeting, pp. 1233-1238 (2022)
Scalar fields which are favorite among the possible candidates for the dark energy usually have degenerate minima at $\pm \phi_{min}$. In the presented work, we discuss a two Higgs doublet model with the non-degenerate vacuum named inert uplifted dou
Externí odkaz:
http://arxiv.org/abs/2407.17932
Autor:
Usman, Muhammad, Qadir, Asghar
Publikováno v:
International Journal of Modern Physics D, Vol. 28, No. 16 (2019) 2040008 (11 pages)
Scalar fields are favorite among the possible candidates for the dark energy. Most frequently discussed are those with degenerate minima at $\pm \phi_{min}$. In this paper, a slightly modified two-Higgs doublet model is taken to contain the Higgs fie
Externí odkaz:
http://arxiv.org/abs/2407.09840
Quantum computing has the potential to solve complex problems beyond the capabilities of classical computers. However, its practical use is currently limited due to early-stage quantum software engineering and the constraints of Noisy Intermediate-Sc
Externí odkaz:
http://arxiv.org/abs/2407.02828
The quantum cloud computing paradigm presents unique challenges in task placement due to the dynamic and heterogeneous nature of quantum computation resources. Traditional heuristic approaches fall short in adapting to the rapidly evolving landscape
Externí odkaz:
http://arxiv.org/abs/2407.02748
Integration of quantum computing in generative machine learning models has the potential to offer benefits such as training speed-up and superior feature extraction. However, the existing quantum generative adversarial networks (QGANs) fail to genera
Externí odkaz:
http://arxiv.org/abs/2406.13196
A scalable and programmable quantum computer holds the potential to solve computationally intensive tasks that classical computers cannot accomplish within a reasonable time frame, achieving quantum advantage. However, the vulnerability of the curren
Externí odkaz:
http://arxiv.org/abs/2406.12267
Large Language Models (LLMs) have demonstrated remarkable capabilities, revolutionizing the integration of AI in daily life applications. However, they are prone to hallucinations, generating claims that contradict established facts, deviating from p
Externí odkaz:
http://arxiv.org/abs/2406.09155
Autor:
Usman, Muhammad, Shahid, M Husnain, Ejaz, Maheen, Hani, Ummay, Fatima, Nayab, Khan, Abdul Rehman, Khan, Asifullah, Mirza, Nasir Majid
Jet tagging is an essential categorization problem in high energy physics. In recent times, Deep Learning has not only risen to the challenge of jet tagging but also significantly improved its performance. In this article, we proposed an idea of a ne
Externí odkaz:
http://arxiv.org/abs/2406.06638
This paper proposes a composite inner-product computation unit based on left-to-right (LR) arithmetic for the acceleration of convolution neural networks (CNN) on hardware. The efficacy of the proposed L2R-CIPU method has been shown on the VGG-16 net
Externí odkaz:
http://arxiv.org/abs/2406.00360