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The Need for a Symbiotic Interface for a Digital Twin (2023)
Conference Proceeding
Palmer, C., Goh, Y. M., Hubbard, E., Grant, R., & Houghton, R. (2023). The Need for a Symbiotic Interface for a Digital Twin. In Leveraging transdisciplinary engineering in a changing and connected world : proceedings of the 30th ISTE international conference on transdisciplinary engineering (873 - 882). https://doi.org/10.3233/ATDE230685

Human interaction with a Digital Twin is an emerging concept for which there are no common definitions. This paper considers the various types of human interaction with Digital Twins. There is very little research considering human cognitive interact... Read More about The Need for a Symbiotic Interface for a Digital Twin.

Elementary effects for models with dimensional inputs of arbitrary type and range: Scaling and trajectory generation (2023)
Journal Article
Rutjens, R. J., Band, L. R., Jones, M. D., & Owen, M. R. (2023). Elementary effects for models with dimensional inputs of arbitrary type and range: Scaling and trajectory generation. PLoS ONE, 18(10), Article e0293344. https://doi.org/10.1371/journal.pone.0293344

The Elementary Effects method is a global sensitivity analysis approach for identifying (un)important parameters in a model. However, it has almost exclusively been used where inputs are dimensionless and take values on [0, 1]. Here, we consider mode... Read More about Elementary effects for models with dimensional inputs of arbitrary type and range: Scaling and trajectory generation.

Analogues of the Bol operator for half-integral weight weakly holomorphic modular forms (2023)
Journal Article
Diamantis, N., Lee, M., & Rolen, L. (2023). Analogues of the Bol operator for half-integral weight weakly holomorphic modular forms. Proceedings of the American Mathematical Society, 152, 37-51. https://doi.org/10.1090/proc/16435

We define an analogue of the Bol operator on spaces of weakly holomorphic modular forms of half-integral weight. We establish its main properties and relation with other objects.

Normal Families and Quasiregular Mappings (2023)
Journal Article
Fletcher, A. N., & Nicks, D. A. (2024). Normal Families and Quasiregular Mappings. Proceedings of the Edinburgh Mathematical Society, 67(1), 79-112. https://doi.org/10.1017/s0013091523000640

Beardon and Minda gave a characterization of normal families of holomorphic and meromorphic functions in terms of a locally uniform Lipschitz condition. Here, we generalize this viewpoint to families of mappings in higher dimensions that are locally... Read More about Normal Families and Quasiregular Mappings.

Modelling how plant cell-cycle progression leads to cell size regulation (2023)
Journal Article
Williamson, D., Tasker-Brown, W., Murray, J., Jones, A. R., & Band, L. R. (2023). Modelling how plant cell-cycle progression leads to cell size regulation. PLoS Computational Biology, 19(10), Article e1011503. https://doi.org/10.1371/journal.pcbi.1011503

Populations of cells typically maintain a consistent size, despite cell division rarely being precisely symmetrical. Therefore, cells must possess a mechanism of “size control”, whereby the cell volume at birth affects cell-cycle progression. While s... Read More about Modelling how plant cell-cycle progression leads to cell size regulation.

A Post-Quantum Associative Memory (2023)
Journal Article
Lami, L., Goldwater, D., & Adesso, G. (2023). A Post-Quantum Associative Memory. Journal of Physics A: Mathematical and Theoretical, 56(45), Article 455304. https://doi.org/10.1088/1751-8121/acfeb7

Associative memories are devices storing information that can be fully retrieved given partial disclosure of it. We examine a toy model of associative memory and the ultimate limitations it is subjected to within the framework of general probabilisti... Read More about A Post-Quantum Associative Memory.

Accelerating Bayesian inference for stochastic epidemic models using incidence data (2023)
Journal Article
Golightly, A., Wadkin, L. E., Whitaker, S. A., Baggaley, A. W., Parker, N. G., & Kypraios, T. (2023). Accelerating Bayesian inference for stochastic epidemic models using incidence data. Statistics and Computing, 33(6), Article 134. https://doi.org/10.1007/s11222-023-10311-6

We consider the case of performing Bayesian inference for stochastic epidemic compartment models, using incomplete time course data consisting of incidence counts that are either the number of new infections or removals in time intervals of fixed len... Read More about Accelerating Bayesian inference for stochastic epidemic models using incidence data.

Deep Learning of Transition Probability Densities for Stochastic Asset Models with Applications in Option Pricing (2023)
Journal Article
Su, H., Tretyakov, M. V., & Newton, D. P. (in press). Deep Learning of Transition Probability Densities for Stochastic Asset Models with Applications in Option Pricing. Management Science,

Transition probability density functions (TPDFs) are fundamental to computational finance, including option pricing and hedging. Advancing recent work in deep learning, we develop novel neural TPDF generators through solving backward Kolmogorov equat... Read More about Deep Learning of Transition Probability Densities for Stochastic Asset Models with Applications in Option Pricing.

Tumor radiogenomics in gliomas with Bayesian layered variable selection (2023)
Journal Article
Mohammed, S., Kurtek, S., Bharath, K., Rao, A., & Baladandayuthapani, V. (2023). Tumor radiogenomics in gliomas with Bayesian layered variable selection. Medical Image Analysis, 90, Article 102964. https://doi.org/10.1016/j.media.2023.102964

We propose a statistical framework to analyze radiological magnetic resonance imaging (MRI) and genomic data to identify the underlying radiogenomic associations in lower grade gliomas (LGG). We devise a novel imaging phenotype by dividing the tumor... Read More about Tumor radiogenomics in gliomas with Bayesian layered variable selection.

Probabilistic Learning of Treatment Trees in Cancer (2023)
Journal Article
Yao, T., Wu, Z., Bharath, K., Li, J., & Baladandayuthapani, V. (2023). Probabilistic Learning of Treatment Trees in Cancer. Annals of Applied Statistics, 17(3), 1884-1908. https://doi.org/10.1214/22-AOAS1696

Accurate identification of synergistic treatment combinations and their underlying biological mechanisms is critical across many disease domains, especially cancer. In translational oncology research, preclinical systems, such as patient-derived xeno... Read More about Probabilistic Learning of Treatment Trees in Cancer.

Machine learning detects terminal singularities (2023)
Conference Proceeding
Kasprzyk, A., Coates, T., & Veneziale, S. (in press). Machine learning detects terminal singularities. In Advances in Neural Information Processing Systems (NeurIPS 2023)

Algebraic varieties are the geometric shapes defined by systems of polynomial equations; they are ubiquitous across mathematics and science. Amongst these algebraic varieties are Q-Fano varieties: positively curved shapes which have Q-factorial termi... Read More about Machine learning detects terminal singularities.

The non-local Lotka-Volterra system with a top hat kernel - Part 1: dynamics and steady states with small diffusivity (2023)
Journal Article
Billingham, J., & Needham, D. J. (2023). The non-local Lotka-Volterra system with a top hat kernel - Part 1: dynamics and steady states with small diffusivity. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 479(2277), Article 20230381. https://doi.org/10.1098/rspa.2023.0381

We study the dynamics of the non-local Lotka-Volterra system ut=Duuxx+u(1-φ - u-αv), vt=Dvvxx+v(1-φ - v-βu), where a star denotes the spatial convolution and the kernel φ is a top hat function. We initially focus on the case of small, equal diffusivi... Read More about The non-local Lotka-Volterra system with a top hat kernel - Part 1: dynamics and steady states with small diffusivity.

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. (in press). Strong convergence of an epidemic model with mixing groups. Advances in Applied Probability, 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.