Zobrazeno 1 - 10
of 17 308
pro vyhledávání: '"QADIR, A."'
Autor:
Qadir, Yasir Abdul, Berdyugin, Andrei V., Piirola, Vilppu, Sakanoi, Takeshi, Kagitani, Masato, Berdyugina, S. V.
This study continues our investigation of early-type binaries using high-precision broad-band polarimetry, focusing on HD 165052, a massive O+O-type binary in the young cluster NGC 6530. Our aim was to monitor linear polarization variations and indep
Externí odkaz:
http://arxiv.org/abs/2410.20991
Autor:
Qadir, Asghar, Jamshaid, Aamina
Chaudhry and Qadir obtained new identities for the gamma function by using a distributional representation for it. Here we obtain new identities for the Riemann zeta function and its family by using that representation for them. This also leads to ne
Externí odkaz:
http://arxiv.org/abs/2409.11029
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
Autor:
Eltaras, Tamer Ahmed, Malluhi, Qutaibah, Savino, Alessandro, Di Carlo, Stefano, Qayyum, Adnan, Qadir, Junaid
In the effort to learn from extensive collections of distributed data, federated learning has emerged as a promising approach for preserving privacy by using a gradient-sharing mechanism instead of exchanging raw data. However, recent studies show th
Externí odkaz:
http://arxiv.org/abs/2406.04227
Iterative parallel-in-time algorithms like Parareal can extend scaling beyond the saturation of purely spatial parallelization when solving initial value problems. However, they require the user to build coarse models to handle the inevitably serial
Externí odkaz:
http://arxiv.org/abs/2404.02521
To cope with the growing prevalence of colorectal cancer (CRC), screening programs for polyp detection and removal have proven their usefulness. Colonoscopy is considered the best-performing procedure for CRC screening. To ease the examination, deep
Externí odkaz:
http://arxiv.org/abs/2401.13315
Autor:
Gill, Sukhpal Singh, Wu, Huaming, Patros, Panos, Ottaviani, Carlo, Arora, Priyansh, Pujol, Victor Casamayor, Haunschild, David, Parlikad, Ajith Kumar, Cetinkaya, Oktay, Lutfiyya, Hanan, Stankovski, Vlado, Li, Ruidong, Ding, Yuemin, Qadir, Junaid, Abraham, Ajith, Ghosh, Soumya K., Song, Houbing Herbert, Sakellariou, Rizos, Rana, Omer, Rodrigues, Joel J. P. C., Kanhere, Salil S., Dustdar, Schahram, Uhlig, Steve, Ramamohanarao, Kotagiri, Buyya, Rajkumar
Publikováno v:
Elsevier Telematics and Informatics Reports, Volume 13, March 2024
Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technologic
Externí odkaz:
http://arxiv.org/abs/2401.02469
Autor:
Byfield, Adam, Poulett, William, Wallace, Ben, Jose, Anusha, Tyagi, Shatakshi, Shembekar, Smita, Qayyum, Adnan, Qadir, Junaid, Bilal, Muhammad
Machine learning (ML) models are becoming integral in healthcare technologies, presenting a critical need for formal assurance to validate their safety, fairness, robustness, and trustworthiness. These models are inherently prone to errors, potential
Externí odkaz:
http://arxiv.org/abs/2311.13978
Autor:
Bilal, Muhammad, Martinho, Dinis, Sim, Reiner, Qayyum, Adnan, Vohra, Hunaid, Caputo, Massimo, Akinosho, Taofeek, Abioye, Sofiat, Khan, Zaheer, Niaz, Waleed, Qadir, Junaid
Coronary angiography analysis is a common clinical task performed by cardiologists to diagnose coronary artery disease (CAD) through an assessment of atherosclerotic plaque's accumulation. This study introduces an end-to-end machine learning solution
Externí odkaz:
http://arxiv.org/abs/2310.17954