Zobrazeno 1 - 10
of 31 248
pro vyhledávání: '"Younis, A."'
Publikováno v:
Connected and Autonomous Vehicles in Smart Cities. CRC Press, 2020. 133-172
Traffic management and on-road safety have been a concern for the transportation authorities and the engineering communities for many years. Most of the implemented technologies for intelligent highways focus on safety measures and increased driver a
Externí odkaz:
http://arxiv.org/abs/2410.02785
Publikováno v:
Journal of Intelligent Transportation Systems 28.5 (2024): 636-650
The major advances in intelligent transportation systems are pushing societal services toward autonomy where road management is to be more agile in order to cope with changes and continue to yield optimal performance. However, the pedestrian experien
Externí odkaz:
http://arxiv.org/abs/2409.11623
Autor:
Younis, Khalid
A number is said to be $y$-smooth if all of its prime factors are less than or equal to $y.$ For all $17/30<\theta\leq 1,$ we show that the density of $y$-smooth numbers in the short interval $[x,x+x^{\theta}]$ is asymptotically equal to the density
Externí odkaz:
http://arxiv.org/abs/2409.05761
Autor:
Schuessler, Alexander, Younis, Rayan, Paik, Jamie, Wagner, Martin, Mathis-Ullrich, Franziska, Kunz, Christian
Training and prototype development in robot-assisted surgery requires appropriate and safe environments for the execution of surgical procedures. Current dry lab laparoscopy phantoms often lack the ability to mimic complex, interactive surgical tasks
Externí odkaz:
http://arxiv.org/abs/2409.03535
Compilation of quantum programs into circuits expressed with discrete gate sets is essential for fault-tolerant quantum computing. Optimal methods for discovering high-precision implementations of unitaries in discrete gate sets such as the Clifford+
Externí odkaz:
http://arxiv.org/abs/2409.00433
Topological data analysis (TDA) uncovers crucial properties of objects in medical imaging. Methods based on persistent homology have demonstrated their advantages in capturing topological features that traditional deep learning methods cannot detect
Externí odkaz:
http://arxiv.org/abs/2408.07905
Autor:
Kwiatkowski, Ariel, Towers, Mark, Terry, Jordan, Balis, John U., De Cola, Gianluca, Deleu, Tristan, Goulão, Manuel, Kallinteris, Andreas, Krimmel, Markus, KG, Arjun, Perez-Vicente, Rodrigo, Pierré, Andrea, Schulhoff, Sander, Tai, Jun Jet, Tan, Hannah, Younis, Omar G.
Reinforcement Learning (RL) is a continuously growing field that has the potential to revolutionize many areas of artificial intelligence. However, despite its promise, RL research is often hindered by the lack of standardization in environment and a
Externí odkaz:
http://arxiv.org/abs/2407.17032
Autor:
Younis, Sohaib, Seeger, Bernhard
Continual learning is a challenging problem in machine learning, especially for image classification tasks with imbalanced datasets. It becomes even more challenging when it involves learning new classes incrementally. One method for incremental clas
Externí odkaz:
http://arxiv.org/abs/2406.09052
Autor:
Younis, Omar G., Corinzia, Luca, Athanasiadis, Ioannis N., Krause, Andreas, Buhmann, Joachim M., Turchetta, Matteo
Crop breeding is crucial in improving agricultural productivity while potentially decreasing land usage, greenhouse gas emissions, and water consumption. However, breeding programs are challenging due to long turnover times, high-dimensional decision
Externí odkaz:
http://arxiv.org/abs/2406.03932
Existing numerical optimizers deployed in quantum compilers use expensive $\mathcal{O}(4^n)$ matrix-matrix operations. Inspired by recent advances in quantum machine learning (QML), QFactor-Sample replaces matrix-matrix operations with simpler $\math
Externí odkaz:
http://arxiv.org/abs/2405.12866