Zobrazeno 1 - 10
of 83 966
pro vyhledávání: '"Alotaibi AT"'
Autor:
Alotaibi, Ibrahim Saleem1 i.alotaibi@seu.edu.sa
Publikováno v:
AAU Journal of Business & Law. 2024, Vol. 8 Issue 2, p52-73. 22p.
The remarkable capabilities of the Segment Anything Model (SAM) for tackling image segmentation tasks in an intuitive and interactive manner has sparked interest in the design of effective visual prompts. Such interest has led to the creation of auto
Externí odkaz:
http://arxiv.org/abs/2410.22048
We investigate the interaction between two flat-top solitons within the cubic-quintic nonlinear Schr\"odinger equation framework. Our study results point towards a significant departure of flat-top solitons collisional characteristics from the conven
Externí odkaz:
http://arxiv.org/abs/2410.18290
Autor:
Hedar, Abdel-Rahman, Abdel-Hakim, Alaa E., Deabes, Wael, Alotaibi, Youseef, Bouazza, Kheir Eddine
Metaheuristic search methods have proven to be essential tools for tackling complex optimization challenges, but their full potential is often constrained by conventional algorithmic frameworks. In this paper, we introduce a novel approach called Dee
Externí odkaz:
http://arxiv.org/abs/2410.17042
Autor:
Cassidy, Bill, Mcbride, Christian, Kendrick, Connah, Reeves, Neil D., Pappachan, Joseph M., Fernandez, Cornelius J., Chacko, Elias, Brüngel, Raphael, Friedrich, Christoph M., Alotaibi, Metib, AlWabel, Abdullah Abdulaziz, Alderwish, Mohammad, Lai, Kuan-Ying, Yap, Moi Hoon
Chronic wounds and associated complications present ever growing burdens for clinics and hospitals world wide. Venous, arterial, diabetic, and pressure wounds are becoming increasingly common globally. These conditions can result in highly debilitati
Externí odkaz:
http://arxiv.org/abs/2410.03359
Autor:
Cioppa, Anthony, Giancola, Silvio, Somers, Vladimir, Joos, Victor, Magera, Floriane, Held, Jan, Ghasemzadeh, Seyed Abolfazl, Zhou, Xin, Seweryn, Karolina, Kowalczyk, Mateusz, Mróz, Zuzanna, Łukasik, Szymon, Hałoń, Michał, Mkhallati, Hassan, Deliège, Adrien, Hinojosa, Carlos, Sanchez, Karen, Mansourian, Amir M., Miralles, Pierre, Barnich, Olivier, De Vleeschouwer, Christophe, Alahi, Alexandre, Ghanem, Bernard, Van Droogenbroeck, Marc, Gorski, Adam, Clapés, Albert, Boiarov, Andrei, Afanasiev, Anton, Xarles, Artur, Scott, Atom, Lim, ByoungKwon, Yeung, Calvin, Gonzalez, Cristian, Rüfenacht, Dominic, Pacilio, Enzo, Deuser, Fabian, Altawijri, Faisal Sami, Cachón, Francisco, Kim, HanKyul, Wang, Haobo, Choe, Hyeonmin, Kim, Hyunwoo J, Kim, Il-Min, Kang, Jae-Mo, Tursunboev, Jamshid, Yang, Jian, Hong, Jihwan, Lee, Jimin, Zhang, Jing, Lee, Junseok, Zhang, Kexin, Habel, Konrad, Jiao, Licheng, Li, Linyi, Gutiérrez-Pérez, Marc, Ortega, Marcelo, Li, Menglong, Lopatto, Milosz, Kasatkin, Nikita, Nemtsev, Nikolay, Oswald, Norbert, Udin, Oleg, Kononov, Pavel, Geng, Pei, Alotaibi, Saad Ghazai, Kim, Sehyung, Ulasen, Sergei, Escalera, Sergio, Zhang, Shanshan, Yang, Shuyuan, Moon, Sunghwan, Moeslund, Thomas B., Shandyba, Vasyl, Golovkin, Vladimir, Dai, Wei, Chung, WonTaek, Liu, Xinyu, Zhu, Yongqiang, Kim, Youngseo, Li, Yuan, Yang, Yuting, Xiao, Yuxuan, Cheng, Zehua, Li, Zhihao
The SoccerNet 2024 challenges represent the fourth annual video understanding challenges organized by the SoccerNet team. These challenges aim to advance research across multiple themes in football, including broadcast video understanding, field unde
Externí odkaz:
http://arxiv.org/abs/2409.10587
Autor:
Alotaibi, Mansour M (AUTHOR), Almuharifi, Fai Y (AUTHOR), Almuhaini, Dina S (AUTHOR), Alsulaiman, Dalya R (AUTHOR), Albader, Maryam A (AUTHOR), Alhejji, Wejdan A (AUTHOR), Alotaibi, Fawaz M (AUTHOR), Asiri, Ibrahim M (AUTHOR), Kurdi, Sawsan M (AUTHOR), Alsultan, Mohammed M (AUTHOR), Almalki, Bassem A (AUTHOR), Alamer, Khalid A (AUTHOR)
Publikováno v:
Patient Preference & Adherence. Jun2024, Vol. 18, p1183-1193. 11p.
Autor:
Prabhushankar, Mohit, Kokilepersaud, Kiran, Quesada, Jorge, Yarici, Yavuz, Zhou, Chen, Alotaibi, Mohammad, AlRegib, Ghassan, Mustafa, Ahmad, Kumakov, Yusufjon
Crowdsourcing annotations has created a paradigm shift in the availability of labeled data for machine learning. Availability of large datasets has accelerated progress in common knowledge applications involving visual and language data. However, spe
Externí odkaz:
http://arxiv.org/abs/2408.11185
Autor:
Alotaibi, Omar, Mark, Brian L.
We present a novel filtering algorithm that employs Bayesian transfer learning to address the challenges posed by mismatched intensity of the noise in a pair of sensors, each of which tracks an object using a nonlinear dynamic system model. In this s
Externí odkaz:
http://arxiv.org/abs/2408.07157
Autor:
Bari, M Saiful, Alnumay, Yazeed, Alzahrani, Norah A., Alotaibi, Nouf M., Alyahya, Hisham A., AlRashed, Sultan, Mirza, Faisal A., Alsubaie, Shaykhah Z., Alahmed, Hassan A., Alabduljabbar, Ghadah, Alkhathran, Raghad, Almushayqih, Yousef, Alnajim, Raneem, Alsubaihi, Salman, Mansour, Maryam Al, Alrubaian, Majed, Alammari, Ali, Alawami, Zaki, Al-Thubaity, Abdulmohsen, Abdelali, Ahmed, Kuriakose, Jeril, Abujabal, Abdalghani, Al-Twairesh, Nora, Alowisheq, Areeb, Khan, Haidar
We present ALLaM: Arabic Large Language Model, a series of large language models to support the ecosystem of Arabic Language Technologies (ALT). ALLaM is carefully trained considering the values of language alignment and knowledge transfer at scale.
Externí odkaz:
http://arxiv.org/abs/2407.15390