L. Lasenby
University of Cambridge
20 Papers
12 Citations
L. Lasenby is an academic researcher from University of Cambridge. The author has contributed to research in topics: Computer science & Environmental science. The author has an hindex of 1, co-authored 1 publications.
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Papers
Scientific discovery in the age of artificial intelligence
Hanchen Wang,Tianfan Fu,Yuanqi Du,Wenhao Gao,Kexin Huang,Ziming Liu,Payal Chandak,Shengchao Liu,Peter Van Katwyk,A Deac,Animashree Anandkumar,Karianne J. Bergen,Carla Gomes,Shirley Ho,Pushmeet Kohli,L. Lasenby,Jure Leskovec,Tie-Yan Liu,Arjun K. Manrai,Debora Marks,Bharath Ramsundar,Le Song,Jimeng Sun,Jian Tang,Petar Veličković,Max Welling,Linfeng Zhang,Connor W. Coley,Yoshua Bengio,Marinka Zitnik +29 more
TL;DR: This work examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deeplearning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
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Omnivision forecasting: Combining satellite and sky images for improved deterministic and probabilistic intra-hour solar energy predictions
TL;DR: In this article , the authors integrate sky images and satellite observations in a single machine learning framework to improve intra-hour (up to 60min ahead) irradiance forecasting, and show that the hybrid model benefits predictions in clear-sky conditions and improves longer-term forecasting.
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Investigating impacts of COVID-19 on urban mobility and emissions
Duo Li,L. Lasenby +1 more
TL;DR: In this paper , the authors exploit different sources of urban mobility data to gain insights into the effects of restrictive policies on the daily mobility and exhaust emissions in pandemic and post-pandemic periods.
13
Mitigating urban motorway congestion and emissions via active traffic management
Duo Li,L. Lasenby +1 more
TL;DR: In this paper , an integrated active traffic management (ATM) system was developed to mitigate urban motorway congestion and emissions in developing countries, which can be used directly with existing road facilities, which provides a cost-effective way for traffic management and decarbonization in developed countries.
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Using a Graph Transformer Network to Predict 3D Coordinates of Proteins via Geometric Algebra Modelling
TL;DR: In this article , a novel geometric algebra (GA) metric based on the relative orientations of amino acid residues is proposed for protein structure prediction, which is used as an additional input feature to a Graph Transformer (GT) to aid the prediction of the 3D coordinates of a protein.
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