All Outputs (109)

Machine learning detects terminal singularities (2023)
Conference Proceeding
Kasprzyk, A. M., Coates, T., & Veneziale, S. (2023). 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.

Polytopes and machine learning (2023)
Journal Article
Bao, J., He, Y., Hirst, E., Hofscheier, J., Kasprzyk, A., & Majumder, S. (2023). Polytopes and machine learning. International Journal of Data Science in the Mathematical Sciences, 1(2), 181-211. https://doi.org/10.1142/S281093922350003X

We introduce machine learning methodology to the study of lattice polytopes. With supervised learning techniques, we predict standard properties such as volume, dual volume, reflexivity, etc, with accuracies up to 100%. We focus on 2d polygons and 3d... Read More about Polytopes and machine learning.

Planar diagrammatics of self-adjoint functors and recognizable tree series (2023)
Journal Article
Khovanov, M., & Laugwitz, R. (2023). Planar diagrammatics of self-adjoint functors and recognizable tree series. Pure and Applied Mathematics Quarterly, 19(5), 2409-2499. https://doi.org/10.4310/pamq.2023.v19.n5.a4

A pair of biadjoint functors between two categories produces a collection of elements in the centers of these categories, one for each isotopy class of nested circles in the plane. If the centers are equipped with a trace map into the ground field, t... Read More about Planar diagrammatics of self-adjoint functors and recognizable tree series.

The Rapid Rise of Generative AI: Assessing risks to safety and security (2023)
Report
Janjeva, A., Harris, A., Mercer, S., Kasprzyk, A., & Gausen, A. (2023). The Rapid Rise of Generative AI: Assessing risks to safety and security. Alan Turing Institute

This CETaS Research Report presents the findings from a major project exploring the implications of generative AI for national security. It is based on extensive engagement with more than 50 experts across government, academia, industry, and civil so... Read More about The Rapid Rise of Generative AI: Assessing risks to safety and security.

A caustic terminating at an inflection point (2023)
Journal Article
Ockendon, J. R., Ockendon, H., Tew, R. H., Hewett, D. P., & Gibbs, A. (2024). A caustic terminating at an inflection point. Wave Motion, 125, Article 103257. https://doi.org/10.1016/j.wavemoti.2023.103257

We present an asymptotic and numerical study of the evolution of an incoming wavefield which has a caustic close to a curve with an inflection point. Our results reveal the emergence of a wavefield which resembles that of a shadow boundary but has a... Read More about A caustic terminating at an inflection point.

Model-based evaluation of admission screening strategies for the detection and control of carbapenemase-producing Enterobacterales in the English hospital setting (2023)
Journal Article
Pople, D., Kypraios, T., Donker, T., Stoesser, N., Seale, A. C., George, R., …Robotham, J. (2023). Model-based evaluation of admission screening strategies for the detection and control of carbapenemase-producing Enterobacterales in the English hospital setting. BMC Medicine, 21(1), Article 492. https://doi.org/10.1186/s12916-023-03007-1

Background Globally, detections of carbapenemase-producing Enterobacterales (CPE) colonisations and infections are increasing. The spread of these highly resistant bacteria poses a serious threat to public health. However, understanding of CPE trans... Read More about Model-based evaluation of admission screening strategies for the detection and control of carbapenemase-producing Enterobacterales in the English hospital setting.

Flow Matching for Scalable Simulation-Based Inference (2023)
Presentation / Conference
Wildberger, J., Dax, M., Green, S., Buchholz, S., Macke, J., & Schölkopf, B. (2023, December). Flow Matching for Scalable Simulation-Based Inference. Poster presented at Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, USA

Neural posterior estimation methods based on discrete normalizing flows have become established tools for simulation-based inference (SBI), but scaling them to high-dimensional problems can be challenging. Building on recent advances in generative mo... Read More about Flow Matching for Scalable Simulation-Based Inference.

Towards Inferring Network Properties from Epidemic Data (2023)
Journal Article
Kiss, I. Z., Berthouze, L., & KhudaBukhsh, W. R. (2024). Towards Inferring Network Properties from Epidemic Data. Bulletin of Mathematical Biology, 86(1), Article 6. https://doi.org/10.1007/s11538-023-01235-3

Epidemic propagation on networks represents an important departure from traditional mass-action models. However, the high-dimensionality of the exact models poses a challenge to both mathematical analysis and parameter inference. By using mean-field... Read More about Towards Inferring Network Properties from Epidemic Data.

On a Dynamic Variant of the Iteratively Regularized Gauss–Newton Method with Sequential Data (2023)
Journal Article
Chada, N. K., Iglesias, M., Lu, S., & Werner, F. (2023). On a Dynamic Variant of the Iteratively Regularized Gauss–Newton Method with Sequential Data. SIAM Journal on Scientific Computing, 45(6), A3020-A3046. https://doi.org/10.1137/22m1512442

For numerous parameter and state estimation problems, assimilating new data as they become available can help produce accurate and fast inference of unknown quantities. While most existing algorithms for solving those kind of ill-posed inverse prob... Read More about On a Dynamic Variant of the Iteratively Regularized Gauss–Newton Method with Sequential Data.

Fluctuations in auxin levels depend upon synchronicity of cell divisions in a one-dimensional model of auxin transport (2023)
Journal Article
Bellows, S., Janes, G., Avitabile, D., King, J. R., Bishopp, A., & Farcot, E. (2023). Fluctuations in auxin levels depend upon synchronicity of cell divisions in a one-dimensional model of auxin transport. PLoS Computational Biology, 19(11), Article e1011646. https://doi.org/10.1371/journal.pcbi.1011646

Auxin is a well-studied plant hormone, the spatial distribution of which remains incompletely understood. Here, we investigate the effects of cell growth and divisions on the dynamics of auxin patterning, using a combination of mathematical modelling... Read More about Fluctuations in auxin levels depend upon synchronicity of cell divisions in a one-dimensional model of auxin transport.

Neural variance reduction for stochastic differential equations (2023)
Journal Article
Hinds, P., & Tretyakov, M. (2023). Neural variance reduction for stochastic differential equations. Journal of Computational Finance, 27(3), 1-41. https://doi.org/10.21314/JCF.2023.010

Variance reduction techniques are of crucial importance for the efficiency of Monte Carlo simulations in finance applications. We propose the use of neural SDEs, with control variates parameterized by neural networks, in order to learn approximately... Read More about Neural variance reduction for stochastic differential equations.

Seasonality as a driver of pH1N12009 influenza vaccination campaign impact (2023)
Journal Article
Bolton, K. J., McCaw, J. M., Dafilis, M. P., McVernon, J., & Heffernan, J. M. (2023). Seasonality as a driver of pH1N12009 influenza vaccination campaign impact. Epidemics, 45, Article 100730. https://doi.org/10.1016/j.epidem.2023.100730

Although the most recent respiratory virus pandemic was triggered by a Coronavirus, sustained and elevated prevalence of highly pathogenic avian influenza viruses able to infect mammalian hosts highlight the continued threat of pandemics of influenza... Read More about Seasonality as a driver of pH1N12009 influenza vaccination campaign impact.

Closed form expressions for the Green’s function of a quantum graph—a scattering approach (2023)
Journal Article
Lawrie, T., Gnutzmann, S., & Tanner, G. K. (2023). Closed form expressions for the Green’s function of a quantum graph—a scattering approach. Journal of Physics A: Mathematical and Theoretical, 56(47), Article 475202. https://doi.org/10.1088/1751-8121/ad03a5

In this work we present a three step procedure for generating a closed form expression of the Green’s function on both closed and open finite quantum graphs with general self-adjoint matching conditions. We first generalize and simplify the approach... Read More about Closed form expressions for the Green’s function of a quantum graph—a scattering approach.

Empirical Quantification of Predictive Uncertainty Due to Model Discrepancy by Training with an Ensemble of Experimental Designs: An Application to Ion Channel Kinetics (2023)
Journal Article
Shuttleworth, J. G., Lei, C. L., Whittaker, D. G., Windley, M. J., Hill, A. P., Preston, S. P., & Mirams, G. R. (2024). Empirical Quantification of Predictive Uncertainty Due to Model Discrepancy by Training with an Ensemble of Experimental Designs: An Application to Ion Channel Kinetics. Bulletin of Mathematical Biology, 86(1), Article 2. https://doi.org/10.1007/s11538-023-01224-6

When using mathematical models to make quantitative predictions for clinical or industrial use, it is important that predictions come with a reliable estimate of their accuracy (uncertainty quantification). Because models of complex biological system... Read More about Empirical Quantification of Predictive Uncertainty Due to Model Discrepancy by Training with an Ensemble of Experimental Designs: An Application to Ion Channel Kinetics.

The impact of household structure on disease-induced herd immunity (2023)
Journal Article
Ball, F., Critcher, L., Neal, P., & Sirl, D. (2023). The impact of household structure on disease-induced herd immunity. Journal of Mathematical Biology, 87(6), Article 83. https://doi.org/10.1007/s00285-023-02010-7

The disease-induced herd immunity level hD is the fraction of the population that must be infected by an epidemic to ensure that a new epidemic among the remaining susceptible population is not supercritical. For a homogeneously mixing population hD... Read More about The impact of household structure on disease-induced herd immunity.

Efficient and Scalable Inverse Kinematics for Continuum Robots (2023)
Journal Article
Wild, S., Zeng, T., Mohammad, A., Billingham, J., Axinte, D., & Dong, X. (2024). Efficient and Scalable Inverse Kinematics for Continuum Robots. IEEE Robotics and Automation Letters, 9(1), 375 - 381. https://doi.org/10.1109/lra.2023.3331291

With their flexible nature, continuum robots offer hyper-redundancy regarding their workspace; their backbone can take many shapes upon a single tip position and orientation. Deciphering which backbone shape to use under certain conditions is crucial... Read More about Efficient and Scalable Inverse Kinematics for Continuum Robots.

The impact of uncertainty in hERG binding mechanism on in silico predictions of drug-induced proarrhythmic risk (2023)
Journal Article
Lei, C. L., Whittaker, D. G., & Mirams, G. R. (2024). The impact of uncertainty in hERG binding mechanism on in silico predictions of drug-induced proarrhythmic risk. British Journal of Pharmacology, 181(7), 987-1004. https://doi.org/10.1111/bph.16250

Background and Purpose Drug-induced reduction of the rapid delayed rectifier potassium current carried by the human Ether-à-go-go-Related Gene (hERG) channel is associated with increased risk of arrhythmias. Recent updates to drug safety regulatory... Read More about The impact of uncertainty in hERG binding mechanism on in silico predictions of drug-induced proarrhythmic risk.

General upper bounds on fluctuations of trajectory observables (2023)
Journal Article
Bakewell-Smith, G., Girotti, F., Guţǎ, M., & Garrahan, J. P. (2023). General upper bounds on fluctuations of trajectory observables. Physical Review Letters, 131(19), Article 197101. https://doi.org/10.1103/PhysRevLett.131.197101

Thermodynamic uncertainty relations (TURs) are general lower bounds on the size of fluctuations of dynamical observables. They have important consequences, one being that the precision of estimation of a current is limited by the amount of entropy pr... Read More about General upper bounds on fluctuations of trajectory observables.

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.