Zobrazeno 1 - 10
of 56 722
pro vyhledávání: '"NAWAZ, A."'
Autor:
Dong, Gaoneng, Babar, Ali Nawaz, Christiansen, Rasmus Ellebæk, Hansen, Søren Engelberth, Stobbe, Søren, Yu, Yi, Mørk, Jesper
The emergence of dielectric bowtie cavities enable optical confinement with ultrahigh quality factor and ultra-small optical mode volumes with perspectives for enhanced light-matter interaction. Experimental work has so far emphasized the realization
Externí odkaz:
http://arxiv.org/abs/2412.08471
Autor:
Junejo, Naveed Ur Rehman, Nawaz, Muhammad Wasim, Huang, Qingsheng, Dong, Xiaoqing, Wang, Chang, Zheng, Gengzhong
The ability to accurately predict and analyze student performance in online education, both at the outset and throughout the semester, is vital. Most of the published studies focus on binary classification (Fail or Pass) but there is still a signific
Externí odkaz:
http://arxiv.org/abs/2412.05938
For the validation and verification of automotive radars, datasets of realistic traffic scenarios are required, which, how ever, are laborious to acquire. In this paper, we introduce radar scene synthesis using GANs as an alternative to the real data
Externí odkaz:
http://arxiv.org/abs/2410.13526
Autor:
Kuzminykh, Ievgeniia, Nawaz, Tareita, Shenzhang, Shihao, Ghita, Bogdan, Raphael, Jeffery, Xiao, Hannan
AI tools, particularly large language modules, have recently proven their effectiveness within learning management systems and online education programmes. As feedback continues to play a crucial role in learning and assessment in schools, educators
Externí odkaz:
http://arxiv.org/abs/2410.11904
Autor:
Nawaz, Umair, Awais, Muhammad, Gani, Hanan, Naseer, Muzammal, Khan, Fahad, Khan, Salman, Anwer, Rao Muhammad
Capitalizing on vast amount of image-text data, large-scale vision-language pre-training has demonstrated remarkable zero-shot capabilities and has been utilized in several applications. However, models trained on general everyday web-crawled data of
Externí odkaz:
http://arxiv.org/abs/2410.01407
Most recommender systems adopt collaborative filtering (CF) and provide recommendations based on past collective interactions. Therefore, the performance of CF algorithms degrades when few or no interactions are available, a scenario referred to as c
Externí odkaz:
http://arxiv.org/abs/2409.17864
Autor:
Yang, David, Ha, Sung Soo, Choi, Sungwook, Liu, Jialun, Treuherz, Daniel, Zhang, Nan, An, Zheyi, Ngo, Hieu Minh, Nawaz, Muhammad Mahmood, Suzana, Ana F., Wu, Longlong, Nisbet, Gareth, Porter, Daniel G., Kim, Hyunjung, Robinson, Ian K.
Strontium titanate is a classic quantum paraelectric oxide material that has been widely studied in bulk and thin films. It exhibits a well-known cubic-to-tetragonal antiferrodistortive phase transition at 105 K, characterized by the rotation of oxyg
Externí odkaz:
http://arxiv.org/abs/2409.07595
In this paper we compute genus 0 orbifold Gromov--Witten invariants of Calabi--Yau threefold complete intersections in weighted projective stacks, regardless of convexity conditions. The traditional quantumn Lefschetz principle may fail even for inva
Externí odkaz:
http://arxiv.org/abs/2409.06193
Autor:
Saeed, Muhammad Saad, Nawaz, Shah, Zaheer, Muhammad Zaigham, Khan, Muhammad Haris, Nandakumar, Karthik, Yousaf, Muhammad Haroon, Sajjad, Hassan, De Schepper, Tom, Schedl, Markus
Multimodal networks have demonstrated remarkable performance improvements over their unimodal counterparts. Existing multimodal networks are designed in a multi-branch fashion that, due to the reliance on fusion strategies, exhibit deteriorated perfo
Externí odkaz:
http://arxiv.org/abs/2408.07445
Autor:
Liaqat, Muhammad Irzam, Nawaz, Shah, Zaheer, Muhammad Zaigham, Saeed, Muhammad Saad, Sajjad, Hassan, De Schepper, Tom, Nandakumar, Karthik, Schedl, Muhammad Haris Khan Markus
Multimodal learning has demonstrated remarkable performance improvements over unimodal architectures. However, multimodal learning methods often exhibit deteriorated performances if one or more modalities are missing. This may be attributed to the co
Externí odkaz:
http://arxiv.org/abs/2407.16243