Zobrazeno 1 - 10
of 96 377
pro vyhledávání: '"Ammar OF"'
Internal crack detection has been a subject of focus in structural health monitoring. By focusing on crack detection in structural datasets, it is demonstrated that deep learning (DL) methods can effectively analyze seismic wave fields interacting wi
Externí odkaz:
http://arxiv.org/abs/2411.10389
We present Y-MAP-Net, a Y-shaped neural network architecture designed for real-time multi-task learning on RGB images. Y-MAP-Net, simultaneously predicts depth, surface normals, human pose, semantic segmentation and generates multi-label captions, al
Externí odkaz:
http://arxiv.org/abs/2411.10334
Micro Crack detection using deep neural networks (DNNs) through an automated pipeline using wave fields interacting with the damaged areas is highly sought after. These high-dimensional spatio-temporal crack data are limited, and these datasets have
Externí odkaz:
http://arxiv.org/abs/2411.10015
Autor:
Jahin, Ammar, Lin, Shi-Zeng
We study the effect of the electron wavefunction on Kohn-Luttinger superconductivity. The role of the wavefunction is encoded in a complex form factor describing the topology and geometry of the bands. We show that the electron wavefunction significa
Externí odkaz:
http://arxiv.org/abs/2411.09664
Autor:
Zhao, Liang, Geng, Shenglin, Tang, Xiongyan, Hawbani, Ammar, Sun, Yunhe, Xu, Lexi, Tarchi, Daniele
Low Earth Orbit (LEO) satellite constellations have seen significant growth and functional enhancement in recent years, which integrates various capabilities like communication, navigation, and remote sensing. However, the heterogeneity of data colle
Externí odkaz:
http://arxiv.org/abs/2411.07752
Consider $E$ a vector bundle over a smooth curve $C$. We compute the $\delta$-invariant of all ample ($\mathbb{Q}$-) line bundles on $\mathbb{P}(E)$ when $E$ is strictly Mumford semistable. We also investigate the case when one assumes that the Harde
Externí odkaz:
http://arxiv.org/abs/2411.05976
Autor:
Liu, Puze, Günster, Jonas, Funk, Niklas, Gröger, Simon, Chen, Dong, Bou-Ammar, Haitham, Jankowski, Julius, Marić, Ante, Calinon, Sylvain, Orsula, Andrej, Olivares-Mendez, Miguel, Zhou, Hongyi, Lioutikov, Rudolf, Neumann, Gerhard, Zhalehmehrabi, Amarildo Likmeta Amirhossein, Bonenfant, Thomas, Restelli, Marcello, Tateo, Davide, Liu, Ziyuan, Peters, Jan
Machine learning methods have a groundbreaking impact in many application domains, but their application on real robotic platforms is still limited. Despite the many challenges associated with combining machine learning technology with robotics, robo
Externí odkaz:
http://arxiv.org/abs/2411.05718
In real-world applications where confidence is key, like autonomous driving, the accurate detection and appropriate handling of classes differing from those used during training are crucial. Despite the proposal of various unknown object detection ap
Externí odkaz:
http://arxiv.org/abs/2411.05564
We consider a strategic decision-making problem where a logistics provider (LP) seeks to locate collection and delivery points (CDPs) with the objective to reduce total logistics costs. The customers maximize utility that depends on their perception
Externí odkaz:
http://arxiv.org/abs/2411.04200
Autor:
Grosnit, Antoine, Maraval, Alexandre, Doran, James, Paolo, Giuseppe, Thomas, Albert, Beevi, Refinath Shahul Hameed Nabeezath, Gonzalez, Jonas, Khandelwal, Khyati, Iacobacci, Ignacio, Benechehab, Abdelhakim, Cherkaoui, Hamza, El-Hili, Youssef Attia, Shao, Kun, Hao, Jianye, Yao, Jun, Kegl, Balazs, Bou-Ammar, Haitham, Wang, Jun
We introduce Agent K v1.0, an end-to-end autonomous data science agent designed to automate, optimise, and generalise across diverse data science tasks. Fully automated, Agent K v1.0 manages the entire data science life cycle by learning from experie
Externí odkaz:
http://arxiv.org/abs/2411.03562