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pro vyhledávání: '"A. Malleson"'
We introduce a new benchmark analysis focusing on 3D canine pose estimation from monocular in-the-wild images. A multi-modal dataset 3DDogs-Lab was captured indoors, featuring various dog breeds trotting on a walkway. It includes data from optical ma
Externí odkaz:
http://arxiv.org/abs/2406.14412
Global problems, such as pandemics and climate change, require rapid international coordination and diffusion of policy. These phenomena are rare however, with one notable example being the international policy response to the COVID-19 pandemic in ea
Externí odkaz:
http://arxiv.org/abs/2302.11277
Autor:
Malleson, Nick, Birkin, Mark, Birks, Daniel, Ge, Jiaqi, Heppenstall, Alison, Manley, Ed, McCulloch, Josie, Ternes, Patricia
Publikováno v:
AI Communications 35, 393--406 (2022)
Agent-based modelling (ABM) is a facet of wider Multi-Agent Systems (MAS) research that explores the collective behaviour of individual `agents', and the implications that their behaviour and interactions have for wider systemic behaviour. The method
Externí odkaz:
http://arxiv.org/abs/2210.06955
Publikováno v:
Crime Science, Vol 13, Iss 1, Pp 1-11 (2024)
Abstract This study of recorded crime trends in England & Wales spans three and a half years, that is, two covid pandemic years from March 2020 and 18 ‘post-pandemic’ months following cessation of covid restrictions. Observed crime rates were com
Externí odkaz:
https://doaj.org/article/e4466bf04f854a4588729f38dc0f263b
Autor:
Nedelsky, Jennifer, author, Malleson, Tom, author
Externí odkaz:
https://doi.org/10.1093/oso/9780190642754.001.0001
Autor:
Tang, Daniel, Malleson, Nick
Every day, weather forecasting centres around the world make use of noisy, incomplete observations of the atmosphere to update their weather forecasts. This process is known as data assimilation, data fusion or state estimation and is best expressed
Externí odkaz:
http://arxiv.org/abs/2205.01616
Autor:
Giles, Oscar, Hosseini, Kasra, Mingas, Grigorios, Strickson, Oliver, Bowler, Louise, Smith, Camila Rangel, Wilde, Harrison, Lim, Jen Ning, Mateen, Bilal, Amarasinghe, Kasun, Ghani, Rayid, Heppenstall, Alison, Lomax, Nik, Malleson, Nick, O'Reilly, Martin, Vollmerteke, Sebastian
Synthetic datasets are often presented as a silver-bullet solution to the problem of privacy-preserving data publishing. However, for many applications, synthetic data has been shown to have limited utility when used to train predictive models. One p
Externí odkaz:
http://arxiv.org/abs/2203.01363
Autor:
Minh Kieu, Alexis Comber, Hang Nguyen Thi Thuy, Thanh Bui Quang, Phe Hoang Huu, Nick Malleson
Publikováno v:
Scientific Data, Vol 11, Iss 1, Pp 1-10 (2024)
Abstract Many cities are facing challenges caused by the increasing use of motorised transport and Hanoi, Vietnam, is no exception. The proliferation of petrol powered motorbikes has caused serious problems of congestion, pollution, and road safety.
Externí odkaz:
https://doaj.org/article/2a153d9daef34e24a0d34fec74185d65
GWR is a popular approach for investigating the spatial variation in relationships between response and predictor variables, and critically for investigating and understanding process spatial heterogeneity. The geographically weighted (GW) framework
Externí odkaz:
http://arxiv.org/abs/2109.14542