arXiv subjects

Joseph Taylor

Publications and source records attributed to Joseph Taylor.

8 recordsLinked to original sources

arXiv2026

Fuzzy Spectroscopy of Bound States in Massive Quantum Field Theories

Mesons and glueballs are paradigmatic bound states of confining quantum field theories (QFTs), but their nonperturbative spectroscopy in the continuum remains challenging beyond one spatial dimension. Here we perform such spectroscopy for the Ising QFT using a recently developed regularization based on noncommutative ``fuzzy'' geometry. On a thin fuzzy torus, we reproduce the universal low-lying $\mathbb E_8$ meson masses of the magnetically perturbed $(1{+}1)\mathrm{D}$ Ising QFT. By increasing the torus aspect ratio, we continuously track the second-lightest $\mathbb E_8$ meson as the system effectively crosses over from 1D to 2D. On the 2D fuzzy torus and sphere, we find a subthreshold scalar level and above-threshold response features consistent with previous estimates of glueball masses. The same excitations are revealed away from equilibrium using quench dynamics. Our results establish fuzzy geometries as nonperturbative spectroscopic probes of massive QFTs, including their bound-state evolution through dimensional crossover.

cond-mat.stat-mech↗

arXiv2026

Embedding-Based Federated Learning with Runtime Governance for Iron Deficiency Prediction

Recent reviews find that the vast majority of published healthcare federated learning (FL) studies never reach real-world deployment. We developed an embedding-based FL pipeline for iron deficiency prediction from routine full blood count (FBC) data and deployed it across real institutional environments at Amsterdam University Medical Centre (AUMC) and NHS Blood and Transplant (NHSBT), two clinical environments that differ markedly in iron deficiency prevalence, ferritin distribution, and subject populations. A frozen domain-specific haematology foundation model, DeepCBC, performs site-local representation extraction, restricting federated training to a compact downstream classifier and substantially reducing recurrent communication relative to full-encoder federation. The two clinical datasets are structurally not independent and identically distributed (non-IID), with heterogeneity arising from distinct population differences rather than sampling artefacts. Runtime governance is enforced by FLA$^3$, a healthcare-oriented FL platform providing study-scoped execution, policy-based authorisation, and signed audit logging. Standard sample-size-weighted aggregation (FedAvg) reduced the area under the receiver operating characteristic curve (ROC-AUC) at both sites relative to local-only training, as the global update was biased towards the larger AUMC distribution. FedMAP, a personalised aggregation method, raised ROC-AUC from 0.9470 to 0.9594 at AUMC and from 0.8558 to 0.8671 at NHSBT relative to local-only training, achieving the highest macro ROC-AUC of 0.9133 and the best macro balanced accuracy overall. These results support personalised aggregation in clinical federations where client sample size and task relevance diverge substantially.

cs.LG↗

arXiv2026

Building Privacy-and-Security-Focused Federated Learning Infrastructure for Global Multi-Centre Healthcare Research

Collaborative healthcare research across multiple institutions increasingly requires diverse clinical datasets, but cross-border data sharing is strictly constrained by privacy regulations. Federated learning (FL) enables model training while keeping data local; however, many existing frameworks remain proof-of-concept and do not adequately address governance risks such as unauthorised participation, misuse, and lack of accountability. In particular, enforceable mechanisms for authentication, authorisation, and accounting (AAA) are often missing, limiting real-world clinical deployment. This paper presents FLA$^3$ (Federated Learning with Authentication, Authorisation, and Accounting), a governance-aware federated learning platform that operationalises regulatory obligations through runtime policy enforcement. FLA$^3$ integrates eXtensible Access Control Markup Language (XACML) compliant attribute-based access control (ABAC), cryptographic accounting, and study-scoped federation directly into the federated learning orchestration layer to enforce institutional sovereignty and protocol adherence. We evaluate FLA$^3$ through two complementary studies. First, we demonstrate operational feasibility by deploying the platform infrastructure across five BloodCounts! Consortium institutions in four countries: United Kingdom, Netherlands, India, and The Gambia. Second, we assess clinical utility using simulated federation of full blood count (FBC) data from 54,446 samples from 35,315 subjects across 25 centres in the INTERVAL study. Results show that FLA$^3$ achieves predictive performance comparable to centralised training while strictly enforcing governance constraints. These results show that enforceable governance can function as a first-class privacy-preserving control, improving trustworthiness for scalable artificial intelligence (AI) in cross-jurisdictional healthcare deployments.

cs.CR↗

arXiv2025

Conformal scalar field theory from Ising tricriticality on the fuzzy sphere

Free theories are landmarks in the landscape of quantum field theories: their exact solvability serves as a pillar for perturbative constructions of interacting theories. Fuzzy sphere regularization, which combines quantum Hall physics with state-operator correspondence, has recently been proposed as a promising framework for simulating three-dimensional conformal field theories (CFTs), but so far it has not provided access to free theories. We overcome this limitation by designing a bilayer quantum Hall system that hosts an Ising tricritical point -- a nontrivial fixed point where first-order and second-order transitions meet -- which flows to the conformally coupled scalar theory in the infrared. The critical energy spectrum and operator structure match those at the Gaussian fixed point, providing nonperturbative evidence for the emergence of a free scalar CFT. Our results expand the landscape of CFTs realizable on the fuzzy sphere and demonstrate that even free bosonic theories -- previously inaccessible -- can emerge from interacting electrons in this framework.

cond-mat.str-el↗

arXiv2024

VAIR: Visuo-Acoustic Implicit Representations for Low-Cost, Multi-Modal Transparent Surface Reconstruction in Indoor Scenes

Mobile robots operating indoors must be prepared to navigate challenging scenes that contain transparent surfaces. This paper proposes a novel method for the fusion of acoustic and visual sensing modalities through implicit neural representations to enable dense reconstruction of transparent surfaces in indoor scenes. We propose a novel model that leverages generative latent optimization to learn an implicit representation of indoor scenes consisting of transparent surfaces. We demonstrate that we can query the implicit representation to enable volumetric rendering in image space or 3D geometry reconstruction (point clouds or mesh) with transparent surface prediction. We evaluate our method's effectiveness qualitatively and quantitatively on a new dataset collected using a custom, low-cost sensing platform featuring RGB-D cameras and ultrasonic sensors. Our method exhibits significant improvement over state-of-the-art for transparent surface reconstruction.

cs.CV↗

arXiv2024

Deep Generative Classification of Blood Cell Morphology

Accurate classification of haematological cells is critical for diagnosing blood disorders, but presents significant challenges for machine automation owing to the complexity of cell morphology, heterogeneities of biological, pathological, and imaging characteristics, and the imbalance of cell type frequencies. We introduce CytoDiffusion, a diffusion-based classifier that effectively models blood cell morphology, combining accurate classification with robust anomaly detection, resistance to distributional shifts, interpretability, data efficiency, and superhuman uncertainty quantification. Our approach outperforms state-of-the-art discriminative models in anomaly detection (AUC 0.990 vs. 0.918), resistance to domain shifts (85.85% vs. 74.38% balanced accuracy), and performance in low-data regimes (95.88% vs. 94.95% balanced accuracy). Notably, our model generates synthetic blood cell images that are nearly indistinguishable from real images, as demonstrated by an authenticity test in which expert haematologists achieved only 52.3% accuracy (95% CI: [50.5%, 54.2%]) in distinguishing real from generated images. Furthermore, we enhance model explainability through the generation of directly interpretable counterfactual heatmaps. Our comprehensive evaluation framework, encompassing these multiple performance dimensions, establishes a new benchmark for medical image analysis in haematology, ultimately enabling improved diagnostic accuracy in clinical settings. Our code is available at https://github.com/CambridgeCIA/CytoDiffusion.

cs.CV↗

arXiv2024

Microscopic Model for Fractional Quantum Hall Nematics

Geometric fluctuations of the density mode in a fractional quantum Hall (FQH) state can give rise to a nematic FQH phase, a topological state with a spontaneously broken rotational symmetry. While experiments on FQH states in the second Landau level have reported signatures of putative FQH nematics in anisotropic transport, a realistic model for this state has been lacking. We show that the standard model of particles in the lowest Landau level interacting via the Coulomb potential realizes the FQH nematic transition, which is reached by a progressive reduction of the strength of the shortest-range Haldane pseudopotential. Using exact diagonalization and variational wave functions, we demonstrate that the FQH nematic transition occurs when the system's neutral gap closes in the long-wavelength limit while the charge gap remains open. We confirm the symmetry-breaking nature of the transition by demonstrating the existence of a "circular moat" potential in the manifold of states with broken rotational symmetry, while its geometric character is revealed through the strong fluctuations of the nematic susceptibility and Hall viscosity.

cond-mat.str-el↗

arXiv2022

How to Teach a Teacher: Challenges and Opportunities in Physics Teacher Education in Germany and the USA

Preparing future physics teachers for the demanding nature of their profession is an important and complex endeavor. Teacher education systems must provide a structure for the coherent professional development of prospective teachers. Worldwide, physics teacher education is organized in different ways, but have to face similar challenges, like the relation between academic studies and practical preparation. To meet these challenges, it is worth taking look at different teacher education systems. In this chapter, we compare physics teacher education in two countries, representing two different educational traditions: Germany and the USA. Comparing different aspects of physics teacher education (standards, organization and institutionalization, content of teacher education, quality assurance), we describe both systems in their current state and why they are organized in the way they are. In doing so, we identify surprising commonalities but also different opportunities for both systems to learn from each other.

physics.ed-ph↗