Zobrazeno 1 - 10
of 65
pro vyhledávání: '"Axenie, Cristian"'
A digital twin of the human neuromuscular system can substantially improve the prediction of injury risks and the evaluation of the readiness to return to sport. Reinforcement learning (RL) algorithms already learn physical quantities unmeasurable in
Autor:
Axenie, Cristian
The stability--robustness--resilience--adaptiveness continuum in neuronal processing follows a hierarchical structure that explains interactions and information processing among the different time scales. Interestingly, using "canonical" neuronal com
Externí odkaz:
http://arxiv.org/abs/2404.14799
Antifragile Perimeter Control: Anticipating and Gaining from Disruptions with Reinforcement Learning
Autor:
Sun, Linghang, Makridis, Michail A., Genser, Alexander, Axenie, Cristian, Grossi, Margherita, Kouvelas, Anastasios
The optimal operation of transportation networks is often susceptible to unexpected disruptions, such as traffic incidents and social events. Many established control strategies rely on mathematical models that struggle to cope with real-world uncert
Externí odkaz:
http://arxiv.org/abs/2402.12665
Autor:
Sun, Linghang, Zhang, Yifan, Axenie, Cristian, Grossi, Margherita, Kouvelas, Anastasios, Makridis, Michail A.
Major cities worldwide experience problems with the performance of their road transportation systems. The continuous increase in traffic demand presents a substantial challenge to the optimal operation of urban road networks and the efficiency of tra
Externí odkaz:
http://arxiv.org/abs/2402.00924
Autor:
Axenie, Cristian, López-Corona, Oliver, Makridis, Michail A., Akbarzadeh, Meisam, Saveriano, Matteo, Stancu, Alexandru, West, Jeffrey
Antifragility characterizes the benefit of a dynamical system derived from the variability in environmental perturbations. Antifragility carries a precise definition that quantifies a system's output response to input variability. Systems may respond
Externí odkaz:
http://arxiv.org/abs/2312.13991
Autor:
Kondylakis, Haridimos, Axenie, Cristian, (Kiran) Bastola, Dhundy, Katehakis, Dimitrios G, Kouroubali, Angelina, Kurz, Daria, Larburu, Nekane, Macía, Iván, Maguire, Roma, Maramis, Christos, Marias, Kostas, Morrow, Philip, Muro, Naiara, Núñez-Benjumea, Francisco José, Rampun, Andrik, Rivera-Romero, Octavio, Scotney, Bryan, Signorelli, Gabriel, Wang, Hui, Tsiknakis, Manolis, Zwiggelaar, Reyer
Publikováno v:
Journal of Medical Internet Research, Vol 22, Iss 12, p e22034 (2020)
BackgroundThe status of the data-driven management of cancer care as well as the challenges, opportunities, and recommendations aimed at accelerating the rate of progress in this field are topics of great interest. Two international workshops, one co
Externí odkaz:
https://doaj.org/article/607ec175822b4e048508951756e2a0c0
Computational models and simulations are not just appealing because of their intrinsic characteristics across spatiotemporal scales, scalability, and predictive power, but also because the set of problems in cancer biomedicine that can be addressed c
Externí odkaz:
http://arxiv.org/abs/2310.20031
Autor:
Axenie, Cristian, Saveriano, Matteo
Mobile robots are ubiquitous. Such vehicles benefit from well-designed and calibrated control algorithms ensuring their task execution under precise uncertainty bounds. Yet, in tasks involving humans in the loop, such as elderly or mobility impaired,
Externí odkaz:
http://arxiv.org/abs/2302.05117
Autor:
Axenie, Cristian, Grossi, Margherita
Existing traffic control systems only possess a local perspective over the multiple scales of traffic evolution, namely the intersection level, the corridor level, and the region level respectively. But luckily, despite its complex mechanics, traffic
Externí odkaz:
http://arxiv.org/abs/2210.10460
Autor:
Axenie, Cristian, Scherr, Wolfgang, Wieder, Alexander, Torres, Anibal Siguenza, Meng, Zhuoxiao, Du, Xiaorui, Sottovia, Paolo, Foroni, Daniele, Grossi, Margherita, Bortoli, Stefano, Brasche, Götz
Publikováno v:
In Expert Systems With Applications 1 May 2024 241