Outputs (112)

Reconstructing the Antarctic ice sheet shape at the Last Glacial Maximum using ice core data (2023)
Journal Article
Turner, F. E., Buck, C. E., Jones, J. M., Sime, L., Vallet, I. M., & Wilkinson, R. D. (2023). Reconstructing the Antarctic ice sheet shape at the Last Glacial Maximum using ice core data. Journal of the Royal Statistical Society: Series C, 72(5), 1493-1511. https://doi.org/10.1093/jrsssc/qlad078

The Antarctic ice sheet (AIS) is the Earth's largest store of frozen water; understanding how it has changed in the past allows us to improve our future projections of how it, and thus sea levels, may change. In this paper, we use previous reconstruc... Read More about Reconstructing the Antarctic ice sheet shape at the Last Glacial Maximum using ice core data.

Multiscale asymptotic analysis reveals how cell growth and subcellular compartments affect tissue-scale hormone transport (2023)
Journal Article
Kiradjiev, K. B., & Band, L. R. (2023). Multiscale asymptotic analysis reveals how cell growth and subcellular compartments affect tissue-scale hormone transport. Bulletin of Mathematical Biology, 85, Article 101. https://doi.org/10.1007/s11538-023-01199-4

Determining how cell-scale processes lead to tissue-scale patterns is key to understanding how hormones and morphogens are distributed within biological tissues and control developmental processes. In this article, we use multiscale asymptotic analys... Read More about Multiscale asymptotic analysis reveals how cell growth and subcellular compartments affect tissue-scale hormone transport.

Operations in connective K-theory (2023)
Journal Article
Vishik, A., & Merkurjev, A. (2023). Operations in connective K-theory. Algebra and Number Theory, 17(9), 1595–1636. https://doi.org/10.2140/ant.2023.17.1595

We classify additive operations in connective K-theory with various torsion-free coefficients. We discover that the answer for the integral case requires understanding of the ˆZ case. Moreover, although integral additive operations are topologically... Read More about Operations in connective K-theory.

Machine learning the dimension of a Fano variety (2023)
Journal Article
Kasprzyk, A. M., Coates, T., & Veneziale, S. (2023). Machine learning the dimension of a Fano variety. Nature Communications, 14, Article 5526. https://doi.org/10.1038/s41467-023-41157-1

Fano varieties are basic building blocks in geometry – they are ‘atomic pieces’ of mathematical shapes. Recent progress in the classification of Fano varieties involves analysing an invariant called the quantum period. This is a sequence of integers... Read More about Machine learning the dimension of a Fano variety.

Fundamental limitations to key distillation from Gaussian states with Gaussian operations (2023)
Journal Article
Lami, L., Mišta, L., & Adesso, G. (2023). Fundamental limitations to key distillation from Gaussian states with Gaussian operations. Physical Review Research, 5(3), Article 033153. https://doi.org/10.1103/PhysRevResearch.5.033153

We establish fundamental upper bounds on the amount of secret key that can be extracted from quantum Gaussian states by using only local Gaussian operations, local classical processing, and public communication. For one-way public communication, or w... Read More about Fundamental limitations to key distillation from Gaussian states with Gaussian operations.

Energetically efficient learning in neuronal networks (2023)
Journal Article
Pache, A., & van Rossum, M. C. (2023). Energetically efficient learning in neuronal networks. Current Opinion in Neurobiology, 83, Article 102779. https://doi.org/10.1016/j.conb.2023.102779

Human and animal experiments have shown that acquiring and storing information can require substantial amounts of metabolic energy. However, computational models of neural plasticity only seldom take this cost into account, and might thereby miss an... Read More about Energetically efficient learning in neuronal networks.

Strong convergence of an epidemic model with mixing groups (2023)
Journal Article
Ball, F., & Neal, P. (2024). Strong convergence of an epidemic model with mixing groups. Advances in Applied Probability, 56(2), 430-463. https://doi.org/10.1017/apr.2023.29

We consider an SIR (susceptible → infective → recovered) epidemic in a closed population of size n, in which infection spreads via mixing events, comprising individuals chosen uniformly at random from the population, which occur at the points of a Po... Read More about Strong convergence of an epidemic model with mixing groups.

On liftings of modular forms and Weil representations (2023)
Journal Article
STROMBERG, F. (2024). On liftings of modular forms and Weil representations. Forum Mathematicum, 36(1), 33-52. https://doi.org/10.1515/forum-2022-0353

We give an explicit construction of lifting maps from integral and half-integral modular forms to vector-valued modular forms for Weil representations associated with arbitrary isotropic subgroups and finite quadratic modules of even and odd signatur... Read More about On liftings of modular forms and Weil representations.

Towards the ultimate brain: Exploring scientific discovery with ChatGPT AI (2023)
Journal Article
Adesso, G. (2023). Towards the ultimate brain: Exploring scientific discovery with ChatGPT AI. AI Magazine, 44(3), 328-342. https://doi.org/10.1002/aaai.12113

This paper presents a novel approach to scientific discovery using an artificial intelligence (AI) environment known as ChatGPT, developed by OpenAI. This is the first paper entirely generated with outputs from ChatGPT. We demonstrate how ChatGPT can... Read More about Towards the ultimate brain: Exploring scientific discovery with ChatGPT AI.

Modelling calibration uncertainty in networks of environmental sensors (2023)
Journal Article
Smith, M. T., Ross, M., Ssematimba, J., Álvarez, M. A., Bainomugisha, E., & Wilkinson, R. (2023). Modelling calibration uncertainty in networks of environmental sensors. Journal of the Royal Statistical Society: Series C, 72(5), 1187-1209. https://doi.org/10.1093/jrsssc/qlad075

Networks of low-cost environmental sensors are becoming ubiquitous, but often suffer from poor accuracies and drift. Regular colocation with reference sensors allows recalibration but is complicated and expensive. Alternatively, the calibration can b... Read More about Modelling calibration uncertainty in networks of environmental sensors.