Zobrazeno 1 - 10
of 1 009
pro vyhledávání: '"Hosking, J."'
Autor:
Plappert, H., Hobson-Merrett, C., Gibbons, B., Baker, E., Bevan, S., Clark, M., Creanor, S., Davies, L., Denyer, R., Frost, J., Gask, L., Gibson, J., Gill, L., Gwernan-Jones, R., Hardy, P., Hosking, J., Huxley, P., Jeffrey, A., Jones, B., Marwaha, S., Pinold, V., Planner, C., Rawcliffe, T., Reilly, Siobhan T., Richards, D., Williams, L., Birchwood, M., Byng, R.
Yes
Current NHS policy encourages an integrated approach to provision of mental and physical care for individuals with long term mental health problems. The 'PARTNERS2' complex intervention is designed to support individuals with psychosis in a
Current NHS policy encourages an integrated approach to provision of mental and physical care for individuals with long term mental health problems. The 'PARTNERS2' complex intervention is designed to support individuals with psychosis in a
Externí odkaz:
http://hdl.handle.net/10454/18577
Autor:
Parthipan, Raghul, Anand, Mohit, Christensen, Hannah M., Hosking, J. Scott, Wischik, Damon J.
Machine learning (ML) has recently shown significant promise in modelling atmospheric systems, such as the weather. Many of these ML models are autoregressive, and error accumulation in their forecasts is a key problem. However, there is no clear def
Externí odkaz:
http://arxiv.org/abs/2405.14714
Autor:
Vaughan, Anna, Markou, Stratis, Tebbutt, Will, Requeima, James, Bruinsma, Wessel P., Andersson, Tom R., Herzog, Michael, Lane, Nicholas D., Chantry, Matthew, Hosking, J. Scott, Turner, Richard E.
Weather forecasting is critical for a range of human activities including transportation, agriculture, industry, as well as the safety of the general public. Machine learning models have the potential to transform the complex weather prediction pipel
Externí odkaz:
http://arxiv.org/abs/2404.00411
Autor:
Rogers, Martin S J, Fox, Maria, Fleming, Andrew, van Zeeland, Louisa, Wilkinson, Jeremy, Hosking, J. Scott
Synthetic Aperture Radar (SAR) imagery is the primary data type used for sea ice mapping due to its spatio-temporal coverage and the ability to detect sea ice independent of cloud and lighting conditions. Automatic sea ice detection using SAR imagery
Externí odkaz:
http://arxiv.org/abs/2401.06009
Autor:
Bruinsma, Wessel P., Markou, Stratis, Requiema, James, Foong, Andrew Y. K., Andersson, Tom R., Vaughan, Anna, Buonomo, Anthony, Hosking, J. Scott, Turner, Richard E.
Conditional neural processes (CNPs; Garnelo et al., 2018a) are attractive meta-learning models which produce well-calibrated predictions and are trainable via a simple maximum likelihood procedure. Although CNPs have many advantages, they are unable
Externí odkaz:
http://arxiv.org/abs/2303.14468
Autor:
Andersson, Tom R., Bruinsma, Wessel P., Markou, Stratis, Requeima, James, Coca-Castro, Alejandro, Vaughan, Anna, Ellis, Anna-Louise, Lazzara, Matthew A., Jones, Dani, Hosking, J. Scott, Turner, Richard E.
Environmental sensors are crucial for monitoring weather conditions and the impacts of climate change. However, it is challenging to place sensors in a way that maximises the informativeness of their measurements, particularly in remote regions like
Externí odkaz:
http://arxiv.org/abs/2211.10381
Autor:
Ravuri, Aditya, Andersson, Tom R., Kazlauskaite, Ieva, Tebbutt, Will, Turner, Richard E., Hosking, J. Scott, Lawrence, Neil D., Kaiser, Markus
Ice cores record crucial information about past climate. However, before ice core data can have scientific value, the chronology must be inferred by estimating the age as a function of depth. Under certain conditions, chemicals locked in the ice disp
Externí odkaz:
http://arxiv.org/abs/2210.16568
The modelling of small-scale processes is a major source of error in climate models, hindering the accuracy of low-cost models which must approximate such processes through parameterization. Red noise is essential to many operational parameterization
Externí odkaz:
http://arxiv.org/abs/2203.14814
Autor:
Rogers, Martin S.J., Fox, Maria, Fleming, Andrew, van Zeeland, Louisa, Wilkinson, Jeremy, Hosking, J. Scott
Publikováno v:
In Remote Sensing of Environment 1 May 2024 305
A new model is presented for multisite statistical downscaling of temperature and precipitation using convolutional conditional neural processes (convCNPs). ConvCNPs are a recently developed class of models that allow deep learning techniques to be a
Externí odkaz:
http://arxiv.org/abs/2101.07950