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pro vyhledávání: '"Girgis, A. A."'
Akademický článek
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This letter introduces a machine-learning approach to learning the semantic dynamics of correlated systems with different control rules and dynamics. By leveraging the Koopman operator in an autoencoder (AE) framework, the system's state evolution is
Externí odkaz:
http://arxiv.org/abs/2410.02303
Autor:
McKinney, Evan, Falstin, Girgis, Yusuf, Israa G., Agarwal, Gaurav, Hatridge, Michael, Jones, Alex K.
This paper addresses frequency crowding constraints in modular quantum architecture design, focusing on the SNAIL-based quantum modules. Two key objectives are explored. First, we present physics-informed design constraints by describing a physical m
Externí odkaz:
http://arxiv.org/abs/2409.18262
In this work, we propose a novel data-driven machine learning (ML) technique to model and predict the dynamics of the wireless propagation environment in latent space. Leveraging the idea of channel charting, which learns compressed representations o
Externí odkaz:
http://arxiv.org/abs/2409.10045
Akademický článek
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Autor:
Makowski, Piotr1 p.makowski.piotr@gmail.com
Publikováno v:
Journal of Islamic Archaeology. 2019, Vol. 6 Issue 2, p209-239. 31p.
In remote control systems, transmitting large data volumes (e.g. video feeds) from wireless sensors to faraway controllers is challenging when the uplink channel capacity is limited (e.g. RedCap devices or massive wireless sensor networks). Furthermo
Externí odkaz:
http://arxiv.org/abs/2406.04853
Autor:
Martino Diez
When the 13th-century Coptic official al-Makīn Ibn al-ʿAmīd was thrown into prison by Sultan Baybars, he set out to compile a summary of Biblical, Graeco-Roman, and Islamic history for his own consolation. His work, which drew from a vast array of
Autor:
سيف الدين ناصر خز, منتظر رزاق حسن
Publikováno v:
Larq Journal for Philosophy, Linguistics & Social Sciences; 2024, Vol. 16 Issue 4, Part 1, p160-175, 16p
Autor:
Rowe, Luke, Girgis, Roger, Gosselin, Anthony, Carrez, Bruno, Golemo, Florian, Heide, Felix, Paull, Liam, Pal, Christopher
Evaluating autonomous vehicle stacks (AVs) in simulation typically involves replaying driving logs from real-world recorded traffic. However, agents replayed from offline data are not reactive and hard to intuitively control. Existing approaches addr
Externí odkaz:
http://arxiv.org/abs/2403.19918