Outputs (44)

Quantifying the difference between phylogenetic diversity and diversity indices (2024)
Journal Article
Bordewich, M., & Semple, C. (2024). Quantifying the difference between phylogenetic diversity and diversity indices. Journal of Mathematical Biology, 88(4), Article 40. https://doi.org/10.1007/s00285-024-02059-y

Phylogenetic diversity is a popular measure for quantifying the biodiversity of a collection Y of species, while phylogenetic diversity indices provide a way to apportion phylogenetic diversity to individual species. Typically, for some specific dive... Read More about Quantifying the difference between phylogenetic diversity and diversity indices.

Evaluating Gaussian Grasp Maps for Generative Grasping Models (2022)
Presentation / Conference Contribution
Prew, W., Breckon, T., Bordewich, M., & Beierholm, U. (2022, July). Evaluating Gaussian Grasp Maps for Generative Grasping Models. Presented at Proc. Int. Joint Conf. Neural Networks, Padova, Italy

Generalising robotic grasping to previously unseen objects is a key task in general robotic manipulation. The current method for training many antipodal generative grasping models rely on a binary ground truth grasp map generated from the centre thir... Read More about Evaluating Gaussian Grasp Maps for Generative Grasping Models.

On the Complexity of Optimising Variants of Phylogenetic Diversity on Phylogenetic Networks (2022)
Journal Article
Bordewich, M., Semple, C., & Wicke, K. (2022). On the Complexity of Optimising Variants of Phylogenetic Diversity on Phylogenetic Networks. Theoretical Computer Science, 917, 66-80. https://doi.org/10.1016/j.tcs.2022.03.012

Phylogenetic Diversity (PD) is a prominent quantitative measure of the biodiversity of a collection of present-day species (taxa). This measure is based on the evolutionary distance among the species in the collection. Loosely speaking, if T is a roo... Read More about On the Complexity of Optimising Variants of Phylogenetic Diversity on Phylogenetic Networks.

On the Maximum Agreement Subtree Conjecture for Balanced Trees (2022)
Journal Article
Bordewich, M., Linz, S., Owen, M., St. John, K., Semple, C., & Wicke, K. (2022). On the Maximum Agreement Subtree Conjecture for Balanced Trees. SIAM Journal on Discrete Mathematics, 36(1), 336-354. https://doi.org/10.1137/20m1379678

We give a counterexample to the conjecture of Martin and Thatte that two balanced rooted binary leaf-labelled trees on n leaves have a maximum agreement subtree (MAST) of size at least n 1 2 . In particular, we show that for any c > 0, there exist tw... Read More about On the Maximum Agreement Subtree Conjecture for Balanced Trees.

Autoencoders Without Reconstruction for Textural Anomaly Detection (2021)
Presentation / Conference Contribution
Adey, P., Akcay, S., Bordewich, M., & Breckon, T. (2021, July). Autoencoders Without Reconstruction for Textural Anomaly Detection. Presented at 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China

Automatic anomaly detection in natural textures is a key component within quality control for a range of high-speed, high-yield manufacturing industries that rely on camera-based visual inspection techniques. Targeting anomaly detection through the u... Read More about Autoencoders Without Reconstruction for Textural Anomaly Detection.

Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss (2021)
Presentation / Conference Contribution
Prew, W., Breckon, T., Bordewich, M., & Beierholm, U. (2021, January). Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss. Presented at 25th International Conference on Pattern Recognition (ICPR 2020), Milan, Italy

In this paper we introduce two methods of improving real-time object grasping performance from monocular colour images in an end-to-end CNN architecture. The first is the addition of an auxiliary task during model training (multi-task learning). Our... Read More about Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss.

A universal tree-based network with the minimum number of reticulations (2018)
Journal Article
Bordewich, M., & Semple, C. (2018). A universal tree-based network with the minimum number of reticulations. Discrete Applied Mathematics, 250, 357-362. https://doi.org/10.1016/j.dam.2018.05.010

A tree-based network N on X is universal if every rooted binary phylogenetic X-tree is a base tree for N. Hayamizu and, independently, Zhang constructively showed that, for all positive integers n, there exists an universal tree-based network on n le... Read More about A universal tree-based network with the minimum number of reticulations.

Recovering normal networks from shortest inter-taxa distance information (2018)
Journal Article
Bordewich, M., Huber, K. T., Moulton, V., & Semple, C. (2018). Recovering normal networks from shortest inter-taxa distance information. Journal of Mathematical Biology, 77(3), 571-594. https://doi.org/10.1007/s00285-018-1218-x

Phylogenetic networks are a type of leaf-labelled, acyclic, directed graph used by biologists to represent the evolutionary history of species whose past includes reticulation events. A phylogenetic network is tree–child if each non-leaf vertex is th... Read More about Recovering normal networks from shortest inter-taxa distance information.

On the information content of discrete phylogenetic characters (2017)
Journal Article
Bordewich, M., Deutschmann, I. M., Fischer, M., Kasbohm, E., Semple, C., & Steel, M. (2018). On the information content of discrete phylogenetic characters. Journal of Mathematical Biology, 77(3), 527-544. https://doi.org/10.1007/s00285-017-1198-2

Phylogenetic inference aims to reconstruct the evolutionary relationships of different species based on genetic (or other) data. Discrete characters are a particular type of data, which contain information on how the species should be grouped togethe... Read More about On the information content of discrete phylogenetic characters.

Constructing Tree-Child Networks from Distance Matrices (2017)
Journal Article
Bordewich, M., Semple, C., & Tokac, N. (2017). Constructing Tree-Child Networks from Distance Matrices. Algorithmica, 80(8), 2240-2259. https://doi.org/10.1007/s00453-017-0320-6

A tree-child network is a phylogenetic network with the property that each non-leaf vertex is the parent of a tree vertex or a leaf. In this paper, we show that a tree-child network on taxa (leaf) set X with an outgroup and a positive real-valued wei... Read More about Constructing Tree-Child Networks from Distance Matrices.