Zobrazeno 1 - 10
of 17 559
pro vyhledávání: '"ANAGNOSTOPOULOS, A."'
Modern edge data centers simultaneously handle multiple Deep Neural Networks (DNNs), leading to significant challenges in workload management. Thus, current management systems must leverage the architectural heterogeneity of new embedded systems to e
Externí odkaz:
http://arxiv.org/abs/2411.17867
Computational Fluid Dynamics (CFD) simulations are essential for analyzing and optimizing fluid flows in a wide range of real-world applications. These simulations involve approximating the solutions of the Navier-Stokes differential equations using
Externí odkaz:
http://arxiv.org/abs/2411.16245
Autor:
Paramanayakam, Varatheepan, Karatzas, Andreas, Anagnostopoulos, Iraklis, Stamoulis, Dimitrios
The advanced function-calling capabilities of foundation models open up new possibilities for deploying agents to perform complex API tasks. However, managing large amounts of data and interacting with numerous APIs makes function calling hardware-in
Externí odkaz:
http://arxiv.org/abs/2411.15399
Multi-source logs provide a comprehensive overview of ongoing system activities, allowing for in-depth analysis to detect potential threats. A practical approach for threat detection involves explicit extraction of entity triples (subject, action, ob
Externí odkaz:
http://arxiv.org/abs/2411.15354
Autor:
G., Daniel M. Jimenez, Solans, David, Heikkila, Mikko, Vitaletti, Andrea, Kourtellis, Nicolas, Anagnostopoulos, Aris, Chatzigiannakis, Ioannis
Recent advances in machine learning have highlighted Federated Learning (FL) as a promising approach that enables multiple distributed users (so-called clients) to collectively train ML models without sharing their private data. While this privacy-pr
Externí odkaz:
http://arxiv.org/abs/2411.12377
In this paper we propose a methodology combining Federated Learning (FL) with Cross-view Image Geo-localization (CVGL) techniques. We address the challenges of data privacy and heterogeneity in autonomous vehicle environments by proposing a personali
Externí odkaz:
http://arxiv.org/abs/2411.04692
This paper proposes a novel localization framework based on collaborative training or federated learning paradigm, for highly accurate localization of autonomous vehicles. More specifically, we build on the standard approach of KalmanNet, a recurrent
Externí odkaz:
http://arxiv.org/abs/2411.05847
Autor:
Eckhoff, Maren, Selimi, Valmir, Aranovitch, Alexander, Lyons, Ian, Briggs, Emily, Hou, Jennifer, Devereson, Alex, Macak, Matej, Champagne, David, Anagnostopoulos, Chris
Many therapies are effective in treating multiple diseases. We present an approach that leverages methods developed in natural language processing and real-world data to prioritize potential, new indications for a mechanism of action (MoA). We specif
Externí odkaz:
http://arxiv.org/abs/2410.19174
Autor:
Rigas, Spyros, Papachristou, Michalis, Papadopoulos, Theofilos, Anagnostopoulos, Fotios, Alexandridis, Georgios
Physics-Informed Neural Networks (PINNs) have emerged as a robust framework for solving Partial Differential Equations (PDEs) by approximating their solutions via neural networks and imposing physics-based constraints on the loss function. Traditiona
Externí odkaz:
http://arxiv.org/abs/2407.17611
Autor:
Hirasawa, Mitsuaki, Anagnostopoulos, Konstantinos N., Azuma, Takehiro, Hatakeyama, Kohta, Nishimura, Jun, Papadoudis, Stratos, Tsuchiya, Asato
The Lorentzian type IIB matrix model is a promising candidate for a nonperturbative formulation of superstring theory. Recently we performed complex Langevin simulations by adding a Lorentz invariant mass term as an IR regulator and found a (1+1)-dim
Externí odkaz:
http://arxiv.org/abs/2407.03491