Outputs (112)

Quantization of derived cotangent stacks and gauge theory on directed graphs (2023)
Journal Article
Benini, M., Pridham, J. P., & Schenkel, A. (2023). Quantization of derived cotangent stacks and gauge theory on directed graphs. Advances in Theoretical and Mathematical Physics, 27(5), 1275-1332. https://doi.org/10.4310/atmp.2023.v27.n5.a1

We study the quantization of the canonical unshifted Poisson structure on the derived cotangent stack T ∗ [X/G] of a quotient stack, where X is a smooth affine scheme with an action of a (reductive) smooth affine group scheme G. This is achieved thro... Read More about Quantization of derived cotangent stacks and gauge theory on directed graphs.

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.

Polytopes and machine learning (2023)
Journal Article
Bao, J., He, Y.-H., 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.

Machine learning detects terminal singularities (2023)
Presentation / Conference Contribution
Kasprzyk, A. M., Coates, T., & Veneziale, S. (2023, December). Machine learning detects terminal singularities. Presented at 37th Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, USA

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 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.

A velocity-based moving mesh virtual element method (2023)
Journal Article
Wells, H., Hubbard, M., & Cangiani, A. (2024). A velocity-based moving mesh virtual element method. Computers and Mathematics with Applications, 155, 110-125. https://doi.org/10.1016/j.camwa.2023.12.005

We present a velocity-based moving mesh virtual element method for the numerical solution of PDEs involving boundaries which are free to move. The virtual element method is used for computing both the mesh velocity and a conservative Arbitrary Lagran... Read More about A velocity-based moving mesh virtual element method.

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., Dodgson, A., Freeman, R., Hope, R., Walker, A. S., Hopkins, S., & 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 Contribution
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.