Zobrazeno 1 - 10
of 129 195
pro vyhledávání: '"A Youssef"'
Autor:
M. S. Abdel-Aty, A Youssef-Soad, W. M. B. Yehia, R. T. E. EL-Nawsany, H. M. K. Kotb, Gamal A. Ahmed, Mohamed E. Hasan, Ehab A. A. Salama, Sobhi F. Lamlom, Fouad H. Saleh, Adnan Noor Shah, Nader R. Abdelsalam
Publikováno v:
BMC Plant Biology, Vol 22, Iss 1, Pp 1-17 (2022)
Abstract To generate high-yielding cultivars with favorable fiber quality traits, cotton breeders can use information about combining ability and gene activity within a population to locate elite parents and potential F1 crosses. To this end, in the
Externí odkaz:
https://doaj.org/article/b6808316899949acaa0cfc92c3c65659
Computing expected information gain (EIG) from prior to posterior (equivalently, mutual information between candidate observations and model parameters or other quantities of interest) is a fundamental challenge in Bayesian optimal experimental desig
Externí odkaz:
http://arxiv.org/abs/2411.08390
Autor:
Allouah, Youssef, Dhasade, Akash, Guerraoui, Rachid, Gupta, Nirupam, Kermarrec, Anne-Marie, Pinot, Rafael, Pires, Rafael, Sharma, Rishi
Federated learning (FL) is an appealing approach to training machine learning models without sharing raw data. However, standard FL algorithms are iterative and thus induce a significant communication cost. One-shot federated learning (OFL) trades th
Externí odkaz:
http://arxiv.org/abs/2411.07182
Autor:
Baptista, Ricardo, Pooladian, Aram-Alexandre, Brennan, Michael, Marzouk, Youssef, Niles-Weed, Jonathan
Conditional simulation is a fundamental task in statistical modeling: Generate samples from the conditionals given finitely many data points from a joint distribution. One promising approach is to construct conditional Brenier maps, where the compone
Externí odkaz:
http://arxiv.org/abs/2411.07154
The emergence of movable antenna (MA) technology has marked a significant advancement in the field of wireless communication research, paving the way for enhanced connectivity, improved signal quality, and adaptability across diverse environments. By
Externí odkaz:
http://arxiv.org/abs/2411.06028
Publikováno v:
Physical Review A 110, 032605 (2024)
Quantum probes, such as single- and two-qubit probes, can accurately measure the temperature of a bosonic bath. The current investigation assesses the precision of temperature estimate using quantum Fisher information and the accompanying quantum sig
Externí odkaz:
http://arxiv.org/abs/2411.05950
This paper explores distributed Reconfigurable Intelligent Surfaces (RISs) by introducing a cooperative dimension that enhances adaptability and performance. It focuses on strategically deploying multiple RISs to improve connectivity with the Base St
Externí odkaz:
http://arxiv.org/abs/2411.05583
Autor:
Boulaimen, Youssef, Fossi, Gabriele, Outemzabet, Leila, Jeanray, Nathalie, Levenets, Oleksandr, Gerart, Stephane, Vachenc, Sebastien, Raieli, Salvatore, Giemza, Joanna
The classification of genetic variants, particularly Variants of Uncertain Significance (VUS), poses a significant challenge in clinical genetics and precision medicine. Large Language Models (LLMs) have emerged as transformative tools in this realm.
Externí odkaz:
http://arxiv.org/abs/2411.05055
Autor:
Mohamed, Youssef, Li, Runjia, Ahmad, Ibrahim Said, Haydarov, Kilichbek, Torr, Philip, Church, Kenneth Ward, Elhoseiny, Mohamed
Research in vision and language has made considerable progress thanks to benchmarks such as COCO. COCO captions focused on unambiguous facts in English; ArtEmis introduced subjective emotions and ArtELingo introduced some multilinguality (Chinese and
Externí odkaz:
http://arxiv.org/abs/2411.03769
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