Zobrazeno 1 - 10
of 6 503
pro vyhledávání: '"Seif P"'
To alleviate the training burden in federated learning while enhancing convergence speed, Split Federated Learning (SFL) has emerged as a promising approach by combining the advantages of federated and split learning. However, recent studies have lar
Externí odkaz:
http://arxiv.org/abs/2412.07813
In this paper, we address the problem of feature selection in the context of multi-label learning, by using a new estimator based on implicit regularization and label embedding. Unlike the sparse feature selection methods that use a penalized estimat
Externí odkaz:
http://arxiv.org/abs/2411.11436
Autor:
Seif, Ali, Zarei, Mina
This study explores the dynamics of two-layer multiplex networks, focusing on how frequency distributions among mirror nodes influence phase transitions and synchronization across layers. We present a Regular frequency assignment model for duplex net
Externí odkaz:
http://arxiv.org/abs/2411.12094
Autor:
Naseri, Mostafa, Ashtari, Pooya, Seif, Mohamed, De Poorter, Eli, Poor, H. Vincent, Shahid, Adnan
In wireless communications, efficient image transmission must balance reliability, throughput, and latency, especially under dynamic channel conditions. This paper presents an adaptive and progressive pipeline for learned image compression (LIC)-base
Externí odkaz:
http://arxiv.org/abs/2411.10650
Collaborative inference among multiple wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications, particularly for sensing and computer vision. This approach typically involves a three-stage process: a
Externí odkaz:
http://arxiv.org/abs/2410.19917
Autor:
Czekster, Ricardo M., Perez, Alexeis Garcia, Kavakli-Thorne, Manolya, Nasri, Seif Allah El Mesloul, Shaikh, Siraj
Digital Twin (DT) technologies promise to remove cyber-physical barriers in systems and services and provide seamless management of distributed resources effectively. Ideally, full-fledged instantiations of DT offer bi-directional features for physic
Externí odkaz:
http://arxiv.org/abs/2410.08479
Autor:
Wang, Yu-Xin, Bringewatt, Jacob, Seif, Alireza, Brady, Anthony J., Oh, Changhun, Gorshkov, Alexey V.
In this work, we propose a new form of exponential quantum advantage in the context of sensing correlated noise. Specifically, we focus on the problem of estimating parameters associated with Lindblad dephasing dynamics, and show that entanglement ca
Externí odkaz:
http://arxiv.org/abs/2410.05878
Tracing a student's knowledge growth given the past exercise answering is a vital objective in automatic tutoring systems to customize the learning experience. Yet, achieving this objective is a non-trivial task as it involves modeling the knowledge
Externí odkaz:
http://arxiv.org/abs/2410.01836
Social interactions promote well-being, yet challenges like geographic distance and mental health conditions can limit in-person engagement. Advances in AI agents are transferring communication, particularly in mental health, where AI chatbots provid
Externí odkaz:
http://arxiv.org/abs/2409.15550
Autor:
Schrecengost, Zachariah S., Hejazine, Seif, Barrett, Jordan V., Démery, Vincent, Paulsen, Joseph D.
We study the deformation of a liquid interface with arbitrary principal curvatures by a flat circular sheet. We use the membrane limit, where the sheet is inextensible yet free to bend and compress, and restrict ourselves to small slopes. We find tha
Externí odkaz:
http://arxiv.org/abs/2409.13042