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pro vyhledávání: '"Norrish, A."'
Artificial Intelligence for Theorem Proving has given rise to a plethora of benchmarks and methodologies, particularly in Interactive Theorem Proving (ITP). Research in the area is fragmented, with a diverse set of approaches being spread across seve
Externí odkaz:
http://arxiv.org/abs/2403.03401
Autor:
Olga Boleti, Gabrielle Norrish, Ella Field, Kathleen Dady, Kim Summers, Gauri Nepali, Vinay Bhole, Orhan Uzun, Amos Wong, Piers E. F. Daubeney, Graham Stuart, Precylia Fernandes, Karen McLeod, Maria Ilina, Muhammad Najih Liaqath Ali, Tara Bharucha, Grazia Delle Donne, Elspeth Brown, Katie Linter, Caroline B. Jones, Jonathan Searle, William Regan, Sujeev Mathur, Nicola Boyd, Zdenka Reinhardt, Sophie Duignan, Terence Prendiville, Satish Adwani, Juan Pablo Kaski
Publikováno v:
ESC Heart Failure, Vol 11, Iss 2, Pp 923-936 (2024)
Abstract Aims This study aimed to describe the natural history and predictors of all‐cause mortality and sudden cardiac death (SCD)/equivalent events in children with a RASopathy syndrome and hypertrophic cardiomyopathy (HCM). Methods and results T
Externí odkaz:
https://doaj.org/article/774801a5c3044f99889a58c53cfad13e
Publikováno v:
Complementary Therapies in Medicine, Vol 80, Iss , Pp 103013- (2024)
Objectives: This study aims to investigate the effectiveness of cupping therapy on low back pain (LBP). Methods: Medline, Embase, Scopus and WANFANG databases were searched for relevant cupping RCTs on low back pain articles up to 2023. A complementa
Externí odkaz:
https://doaj.org/article/7adb4c9a5a8443048f559ceeac2660e4
Autor:
Maxwell, Luke, Nava, Tobia, Norrish, Alan, Kobezda, Tamas, Pizzimenti, Marc, Brassett, Cecilia, Pasapula, Chandra
Publikováno v:
In The Foot June 2024 59
Autor:
Pang, Joe, Hussain, Ali, Yan, Mathhew, Kapur, Karan, Solomou, Georgios, Brassett, Cecilia, Pasapula, Chandra, Norrish, Alan R.
Publikováno v:
In The Foot June 2024 59
Publikováno v:
In Complementary Therapies in Medicine March 2024 80
We propose a novel approach to interactive theorem-proving (ITP) using deep reinforcement learning. The proposed framework is able to learn proof search strategies as well as tactic and arguments prediction in an end-to-end manner. We formulate the p
Externí odkaz:
http://arxiv.org/abs/2102.09756
Autor:
Boleti, Olga D., Roussos, Sotirios, Norrish, Gabrielle, Field, Ella, Oates, Stephanie, Tollit, Jennifer, Nepali, Gauri, Bhole, Vinay, Uzun, Orhan, Daubeney, Piers E.F., Stuart, Graham A., Fernandes, Precylia, McLeod, Karen, Ilina, Maria, Liaqath, Muhammad Najih Ali, Bharucha, Tara, Delle Donne, Grazia, Brown, Elspeth, Linter, Katie, Khodaghalian, Bernadette, Jones, Caroline, Searle, Jonathan, Mathur, Sujeev, Boyd, Nicola, Reindhardt, Zdenka, Duignan, Sophie, Prendiville, Terence, Adwani, Satish, Zenker, Martin, Wolf, Cordula Maria, Kaski, Juan Pablo
Publikováno v:
In International Journal of Cardiology 15 December 2023 393
Publikováno v:
In The Foot September 2023 56
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