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Outputs (104)

Superdiffusive planar random walks with polynomial space–time drifts (2024)
Journal Article
da Costa, C., Menshikov, M., Shcherbakov, V., & Wade, A. (2024). Superdiffusive planar random walks with polynomial space–time drifts. Stochastic Processes and their Applications, 176, Article 104420. https://doi.org/10.1016/j.spa.2024.104420

We quantify superdiffusive transience for a two-dimensional random walk in which the vertical coordinate is a martingale and the horizontal coordinate has a positive drift that is a polynomial function of the individual coordinates... Read More about Superdiffusive planar random walks with polynomial space–time drifts.

Structured prior distributions for the covariance matrix in latent factor models (2024)
Journal Article
Heaps, S. E., & Jermyn, I. H. (2024). Structured prior distributions for the covariance matrix in latent factor models. Statistics and Computing, 34(4), Article 143. https://doi.org/10.1007/s11222-024-10454-0

Factor models are widely used for dimension reduction in the analysis of multivariate data. This is achieved through decomposition of a p×p covariance matrix into the sum of two components. Through a latent factor representation, they can be interpre... Read More about Structured prior distributions for the covariance matrix in latent factor models.

Training neural networks with universal adiabatic quantum computing (2024)
Journal Article
Abel, S., Criado, J. C., & Spannowsky, M. (2024). Training neural networks with universal adiabatic quantum computing. Frontiers in Artificial Intelligence, 7, Article 1368569. https://doi.org/10.3389/frai.2024.1368569

The training of neural networks (NNs) is a computationally intensive task requiring significant time and resources. This article presents a novel approach to NN training using adiabatic quantum computing (AQC), a paradigm that leverages the principle... Read More about Training neural networks with universal adiabatic quantum computing.

Using a prognostic medical device for early identification of pressure ulcers: protocol for study design. (2024)
Journal Article
Keltie, K., Parker, R., Dervin, H., Pagnamenta, F., Milne, J., Belilios, E., Latimer, L., Wason, J., Ogundimu, E., McParlin, C., & Sims, A. (2024). Using a prognostic medical device for early identification of pressure ulcers: protocol for study design. British Journal of Nursing, 33(12), S8-S18. https://doi.org/10.12968/bjon.2024.0158

Background: An objective, physiological measurement taken using a medical device may reduce the incidence of pressure ulcers through earlier detection of problems signs before visual signs appear. Research in this field is hampered by variations in... Read More about Using a prognostic medical device for early identification of pressure ulcers: protocol for study design..

Directed Spatial Permutations on Asymmetric Tori (2024)
Journal Article
Helmuth, T., & Hammond, A. (in press). Directed Spatial Permutations on Asymmetric Tori. Annals of Probability,

We investigate a model of random spatial permutations on two-dimensional tori, and establish that the joint distribution of large cycles is asymptotically given by the Poisson--Dirichlet distribution with parameter one. The asymmetry of the tori we c... Read More about Directed Spatial Permutations on Asymmetric Tori.

The Batchelor–Howells–Townsend spectrum: large velocity case (2024)
Journal Article
Jolly, M. S., & Wirosoetisno, D. (2024). The Batchelor–Howells–Townsend spectrum: large velocity case. Nonlinearity, 37(7), Article 075025. https://doi.org/10.1088/1361-6544/ad5265

We consider the behaviour of a passive tracer θ governed by ∂tθ+u⋅∇θ=Δθ+g in two space dimensions with prescribed smooth random incompressible velocity u(x, t) and source g(x). In 1959, Batchelor et al (J. Fluid Mech. 5 113) predicted that the tracer... Read More about The Batchelor–Howells–Townsend spectrum: large velocity case.

High-dimensional detection of Landscape Dynamics: a Landsat time series-based algorithm for forest disturbance mapping and beyond (2024)
Journal Article
Morresi, D., Maeng, H., Marzano, R., Lingua, E., Motta, R., & Garbarino, M. (2024). High-dimensional detection of Landscape Dynamics: a Landsat time series-based algorithm for forest disturbance mapping and beyond. GIScience and Remote Sensing, 61(1), Article 2365001. https://doi.org/10.1080/15481603.2024.2365001

Time series analysis of medium-resolution multispectral satellite imagery is critical to investigate forest disturbance dynamics at the landscape scale. In particular, the spatial, temporal, and radiometric consistency of Landsat time series data pro... Read More about High-dimensional detection of Landscape Dynamics: a Landsat time series-based algorithm for forest disturbance mapping and beyond.

A distribution-free method for change point detection in non-sparse high dimensional data (2024)
Journal Article
Drikvandi, R., & Modarres, R. (2024). A distribution-free method for change point detection in non-sparse high dimensional data. Journal of Computational and Graphical Statistics, https://doi.org/10.1080/10618600.2024.2365733

We propose a distribution-free distance-based method for high dimensional change points that can address challenging situations when the sample size is very small compared to the dimension as in the so-called HDLSS data or when non-sparse changes may... Read More about A distribution-free method for change point detection in non-sparse high dimensional data.

Basic metric geometry of the bottleneck distance (2024)
Journal Article
Che, M., Galaz-García, F., Guijarro, L., Membrillo Solis, I., & Valiunas, M. (2024). Basic metric geometry of the bottleneck distance. Proceedings of the American Mathematical Society, 152(8), 3575-3591. https://doi.org/10.1090/proc/16776

Given a metric pair (X, A), i.e. a metric space X and a distinguished closed set A ⊂ X, one may construct in a functorial way a pointed pseudometric space D∞(X, A) of persistence diagrams equipped with the bottleneck distance. We investigate the basi... Read More about Basic metric geometry of the bottleneck distance.