Zobrazeno 1 - 10
of 5 916
pro vyhledávání: '"A. Galligan"'
Autor:
Blackwell, J., Krasny, M. J., O'Brien, A., Ashkan, K., Galligan, J., Destrade, M., Colgan, N.
Publikováno v:
Journal of Magnetic Resonance Imaging, 55 (2022) 389-403
Magnetic Resonance Imaging (MRI) has become a popular modality in guiding minimally invasive thermal therapies, due to its advanced, non-ionizing, imaging capabilities and its ability to record changes in temperature. A variety of MR thermometry tech
Externí odkaz:
http://arxiv.org/abs/2403.01219
We establish that a large, flexible class of long, high redundancy error correcting codes can be efficiently and accurately decoded with guessing random additive noise decoding (GRAND). Performance evaluation demonstrates that it is possible to const
Externí odkaz:
http://arxiv.org/abs/2310.10737
Autor:
Kevin Schofield, Shayna Maddern, Yueteng Zhang, Grace E. Mastin, Rachel Knight, Wei Wang, James Galligan, Christopher Hulme
Publikováno v:
Beilstein Journal of Organic Chemistry, Vol 20, Iss 1, Pp 2270-2279 (2024)
The utility of bio-isosteres is broad in drug discovery and methodology herein enables the preparation of deuterium-labeled products is the most fundamental of known bio-isosteric replacements. As such we report the use of both [D1]-aldehydes and [D2
Externí odkaz:
https://doaj.org/article/faf573af030a4151bcf777a1b3ca1748
Publikováno v:
2023 IEEE Global Communications Conference (Globecom)
Guessing Random Additive Noise Decoding (GRAND) is a family of hard- and soft-detection error correction decoding algorithms that provide accurate decoding of any moderate redundancy code of any length. Here we establish a method through which any so
Externí odkaz:
http://arxiv.org/abs/2305.05777
We investigate the performance and characteristics of currently available VB and MCMC software to explore the practicability of available approaches and provide guidance for clinical practitioners. Two case studies are used to fully explore the metho
Externí odkaz:
http://arxiv.org/abs/2304.03733
Publikováno v:
Natural Language Processing Journal 2 (2023) 100003
Sentiment analysis AKA opinion mining is one of the most widely used NLP applications to identify human intentions from their reviews. In the education sector, opinion mining is used to listen to student opinions and enhance their learning-teaching p
Externí odkaz:
http://arxiv.org/abs/2302.04359
Autor:
Ercan, Furkan, Galligan, Kevin, Starobinski, David, Medard, Muriel, Duffy, Ken R., Yazicigil, Rabia Tugce
Random jammers that overpower transmitted signals are a practical concern for many wireless communication protocols. As such, wireless receivers must be able to cope with standard channel noise and jamming (intentional or unintentional). To address t
Externí odkaz:
http://arxiv.org/abs/2301.09778
Autor:
Shaik, Thanveer, Tao, Xiaohui, Li, Yan, Dann, Christopher, Mcdonald, Jacquie, Redmond, Petrea, Galligan, Linda
Publikováno v:
IEEE Access, vol. 10, pp. 56720-56739, 2022
Artificial Intelligence (AI) is a fast-growing area of study that stretching its presence to many business and research domains. Machine learning, deep learning, and natural language processing (NLP) are subsets of AI to tackle different areas of dat
Externí odkaz:
http://arxiv.org/abs/2301.08826
Publikováno v:
Frontiers in Applied Mathematics and Statistics, Vol 10 (2024)
IntroductionBayesian approaches to patient phenotyping in clinical observational studies have been limited by the computational challenges associated with applying the Markov Chain Monte Carlo (MCMC) approach to real-world data. Approximate Bayesian
Externí odkaz:
https://doaj.org/article/c28465e534c04c5dbdaa50ee6be12f6a
Autor:
Caoimhe Dalton, Lisa Murphy, Carmel Ann Galligan, Susan O’Gorman, Larry Bacon, Claudine Howard‐James, Rachel Dillon, Holly Fitzgerald
Publikováno v:
Skin Health and Disease, Vol 4, Iss 5, Pp n/a-n/a (2024)
Externí odkaz:
https://doaj.org/article/51a1c5737d3949109a6ccd1ea0ca1332