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of 13 290
pro vyhledávání: '"Jarman AT"'
Longwave infrared (LWIR) hyperspectral imaging can be used for many tasks in remote sensing, including detecting and identifying effluent gases by LWIR sensors on airborne platforms. Once a potential plume has been detected, it needs to be identified
Externí odkaz:
http://arxiv.org/abs/2411.15378
Publikováno v:
Bit Numer Math 64, 26 (2024)
In this paper, we consider large-scale ranking problems where one is given a set of (possibly non-redundant) pairwise comparisons and the underlying ranking explained by those comparisons is desired. We show that stochastic gradient descent approache
Externí odkaz:
http://arxiv.org/abs/2407.02656
Deep learning identification models have shown promise for identifying gas plumes in Longwave IR hyperspectral images of urban scenes, particularly when a large library of gases are being considered. Because many gases have similar spectral signature
Externí odkaz:
http://arxiv.org/abs/2401.13068
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-14 (2024)
Abstract Respiratory complex I is pivotal for cellular energy conversion, harnessing energy from NADH:ubiquinone oxidoreduction to drive protons across energy-transducing membranes for ATP synthesis. Despite detailed structural information on complex
Externí odkaz:
https://doaj.org/article/b603bc4cca4b4c6eb81d0fbe26032711
Autor:
Alyssa M. Budd, Suk Yee Yong, Matthew J. Heydenrych, Benjamin Mayne, Oliver Berry, Simon Jarman
Publikováno v:
Communications Biology, Vol 7, Iss 1, Pp 1-13 (2024)
Abstract Animal age at maturity can be used as a universal and simple predictor of species extinction risk. At present, methods to estimate age at maturity are typically species-specific, limiting comparisons among species, or are infeasible due to p
Externí odkaz:
https://doaj.org/article/07e73c12b1dc4f83be957b53517328f9
Autor:
Clodagh Towns, Zih-Hua Fang, Manuela M. X. Tan, Simona Jasaityte, Theresa M. Schmaderer, Eleanor J. Stafford, Miriam Pollard, Russel Tilney, Megan Hodgson, Lesley Wu, Robyn Labrum, Jason Hehir, James Polke, Lara M. Lange, Anthony H. V. Schapira, Kailash P. Bhatia, Parkinson’s Families Project (PFP) Study Group, Global Parkinson’s Genetics Program (GP2), Andrew B. Singleton, Cornelis Blauwendraat, Christine Klein, Henry Houlden, Nicholas W. Wood, Paul R. Jarman, Huw R. Morris, Raquel Real
Publikováno v:
npj Parkinson's Disease, Vol 10, Iss 1, Pp 1-13 (2024)
Abstract The Parkinson’s Families Project is a UK-wide study aimed at identifying genetic variation associated with familial and early-onset Parkinson’s disease (PD). We recruited individuals with a clinical diagnosis of PD and age at motor sympt
Externí odkaz:
https://doaj.org/article/492a58c74a1046dba388813690ffc138
Autor:
Pellow-Jarman, Aidan, McFarthing, Shane, Sinayskiy, Ilya, Park, Daniel K., Pillay, Anban, Petruccione, Francesco
Publikováno v:
Sci Rep 14, 16011 (2024)
The Quantum Approximate Optimization Algorithm (QAOA) is a variational quantum algorithm for Near-term Intermediate-Scale Quantum computers (NISQ) providing approximate solutions for combinatorial optimiz\-ation problems. The QAOA utilizes a quantum-
Externí odkaz:
http://arxiv.org/abs/2307.10149
Autor:
Pepper, Ian, Cox, Carol, Fee, Ruth, Horgan, Shane, Jarman, Rod, Jones, Matthew, Policek, Nicoletta, Rogers, Colin, Tattum, Clive
Publikováno v:
Higher Education, Skills and Work-Based Learning, 2024, Vol. 14, Issue 5, pp. 1106-1120.
Autor:
Aidan Pellow-Jarman, Shane McFarthing, Ilya Sinayskiy, Daniel K. Park, Anban Pillay, Francesco Petruccione
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract The Quantum Approximate Optimization Algorithm (QAOA) is a variational quantum algorithm for Near-term Intermediate-Scale Quantum computers (NISQ) providing approximate solutions for combinatorial optimization problems. The QAOA utilizes a q
Externí odkaz:
https://doaj.org/article/4f12781da6c949c9a140e085e0b99760
Publikováno v:
Quantum Mach. Intell. 6, 45 (2024)
Kernel methods are an important class of techniques in machine learning. To be effective, good feature maps are crucial for mapping non-linearly separable input data into a higher dimensional (feature) space, thus allowing the data to be linearly sep
Externí odkaz:
http://arxiv.org/abs/2302.02980