Zobrazeno 1 - 10
of 1 854
pro vyhledávání: '"Vasilaki, A"'
Learning representations of underlying environmental dynamics from partial observations is a critical challenge in machine learning. In the context of Partially Observable Markov Decision Processes (POMDPs), state representations are often inferred f
Externí odkaz:
http://arxiv.org/abs/2411.07832
Publikováno v:
JCAP06(2024)027
The formation and decay of metastable bound states can deplete significantly the density of multi-TeV thermal-relic dark matter. The effect depends on the interplay of bound-state formation, ionisation, transition and decay processes. Existing calcul
Externí odkaz:
http://arxiv.org/abs/2402.13069
Autor:
Manneschi, Luca, Vidamour, Ian T., Stenning, Kilian D., Swindells, Charles, Venkat, Guru, Griffin, David, Gui, Lai, Sonawala, Daanish, Donskikh, Denis, Hariga, Dana, Stepney, Susan, Branford, Will R., Gartside, Jack C., Hayward, Thomas, Ellis, Matthew O. A., Vasilaki, Eleni
Physical computing has the potential to enable widespread embodied intelligence by leveraging the intrinsic dynamics of complex systems for efficient sensing, processing, and interaction. While individual devices provide basic data processing capabil
Externí odkaz:
http://arxiv.org/abs/2401.07387
Autor:
Kilian D. Stenning, Jack C. Gartside, Luca Manneschi, Christopher T. S. Cheung, Tony Chen, Alex Vanstone, Jake Love, Holly Holder, Francesco Caravelli, Hidekazu Kurebayashi, Karin Everschor-Sitte, Eleni Vasilaki, Will R. Branford
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-15 (2024)
Abstract Physical neuromorphic computing, exploiting the complex dynamics of physical systems, has seen rapid advancements in sophistication and performance. Physical reservoir computing, a subset of neuromorphic computing, faces limitations due to i
Externí odkaz:
https://doaj.org/article/1b9a5b0ed0ff4844bcb8ea4ebed5879d
Autor:
Eleftheria Vasilaki, Yu Bai, Mohamad Moustafa Ali, Anders Sundqvist, Aristidis Moustakas, Carl-Henrik Heldin
Publikováno v:
Cell Communication and Signaling, Vol 22, Iss 1, Pp 1-18 (2024)
Abstract Background p63 is a transcription factor with intrinsic pioneer factor activity and pleiotropic functions. Transforming growth factor β (TGFβ) signaling via activation and cooperative action of canonical, SMAD, and non-canonical, MAP-kinas
Externí odkaz:
https://doaj.org/article/c69b2398770746ad995d89ef0e1932d9
Autor:
Ellis, Matthew O. A., Welbourne, Alex, Kyle, Stephan J., Fry, Paul W., Allwood, Dan A., Hayward, Thomas J., Vasilaki, Eleni
The impressive performance of artificial neural networks has come at the cost of high energy usage and CO$_2$ emissions. Unconventional computing architectures, with magnetic systems as a candidate, have potential as alternative energy-efficient hard
Externí odkaz:
http://arxiv.org/abs/2303.01886
Autor:
Allwood, Dan A, Ellis, Matthew O A, Griffin, David, Hayward, Thomas J, Manneschi, Luca, Musameh, Mohammad F KH, O'Keefe, Simon, Stepney, Susan, Swindells, Charles, Trefzer, Martin A, Vasilaki, Eleni, Venkat, Guru, Vidamour, Ian, Wringe, Chester
Neural networks have revolutionized the area of artificial intelligence and introduced transformative applications to almost every scientific field and industry. However, this success comes at a great price; the energy requirements for training advan
Externí odkaz:
http://arxiv.org/abs/2212.04851
Autor:
Stenning, Kilian D., Gartside, Jack C., Manneschi, Luca, Cheung, Christopher T. S., Chen, Tony, Vanstone, Alex, Love, Jake, Holder, Holly H., Caravelli, Francesco, Kurebayashi, Hidekazu, Everschor-Sitte, Karin, Vasilaki, Eleni, Branford, Will R.
Publikováno v:
Nature Communications 2024
Physical neuromorphic computing, exploiting the complex dynamics of physical systems, has seen rapid advancements in sophistication and performance. Physical reservoir computing, a subset of neuromorphic computing, faces limitations due to its relian
Externí odkaz:
http://arxiv.org/abs/2211.06373
Autor:
Vidamour, Ian, Swindells, Charles, Venkat, Guru, Manneschi, Luca, Fry, Paul, Welbourne, Alexander, Rowan-Robinson, Richard, Backes, Dirk, Maccherozzi, Francisco, Dhesi, Sarnjeet, Vasilaki, Eleni, Allwood, Daniel, Hayward, Thomas
In-materia reservoir computing (RC) leverages the intrinsic physical responses of functional materials to perform complex computational tasks. Magnetic metamaterials are exciting candidates for RC due to their huge state space, nonlinear emergent dyn
Externí odkaz:
http://arxiv.org/abs/2206.04446
Autor:
Papadimitriou, Katerina, Sapountzaki, Galini, Vasilaki, Kyriaki, Efthimiou, Eleni, Fotinea, Stavroula-Evita, Potamianos, Gerasimos
Publikováno v:
In Computer Vision and Image Understanding December 2024 249