arXiv subjects

Thomas Bartlett

Publications and source records attributed to Thomas Bartlett.

3 recordsLinked to original sources

arXiv2022

Twist and Turn Squeezing in a Multi-Mode Bose-Einstein Condensate

Here we examine the generation of Twist and Turn (TNT) Squeezing in a large atom-number Bose-Einstein Condensate for the purposes of generating quantum-enhanced states for atom interferometry. Unlike previous analysis, we examine situations where the multi-mode dynamics is significant, and cannot be captured by a simple single-mode model. We find that in some regimes, with careful choice of the rotation parameter, we can still obtain squeezing much more rapidly than via one-axis twisting (OAT).

quant-ph↗

arXiv2021

Dimensions of Kleinian orbital sets

Given a non-empty bounded subset of hyperbolic space and a Kleinian group acting on that space, the orbital set is the orbit of the given set under the action of the group. We may view orbital sets as bounded (often fractal) subsets of Euclidean space. We prove that the upper box dimension of an orbital set is given by the maximum of three quantities: the upper box dimension of the given set; the Poincaré exponent of the Kleinian group; and the upper box dimension of the limit set of the Kleinian group. Since we do not make any assumptions about the Kleinian group, none of the terms in the maximum can be removed in general. We show by constructing an explicit example that the (hyperbolic) boundedness assumption on $C$ cannot be removed in general.

math.DS↗

arXiv2016

Parenclitic network analysis of methylation data for cancer identification

We make use of ideas from the theory of complex networks to implement a machine learning classification of human DNA methylation data, that carry signatures of cancer development. The data were obtained from patients with various kinds of cancers and represented as parenclictic networks, wherein nodes correspond to genes, and edges are weighted according to pairwise variation from control group subjects. We demonstrate that for the $10$ types of cancer under study, it is possible to obtain a high performance of binary classification between cancer-positive and negative samples based on network measures. Remarkably, an accuracy as high as $93-99\%$ is achieved with only $12$ network topology indices, in a dramatic reduction of complexity from the original $15295$ gene methylation levels. Moreover, it was found that the parenclictic networks are scale-free in cancer-negative subjects, and deviate from the power-law node degree distribution in cancer. The node centrality ranking and arising modular structure could provide insights into the systems biology of cancer.

q-bio.GN↗