arXiv subjects

Sean McMahon

Publications and source records attributed to Sean McMahon.

7 recordsLinked to original sources

arXiv2026

Mineral False Positives in the Search for Exoplanet Surface Biosignatures

In the search for life in the cosmos, biopigments on exoplanet surfaces are a critical target. Such pigments have been detected in Earth's spectrum (by the Galileo spacecraft and in Earthshine) via the "vegetation" or "photosynthesis red edge" (VRE or PRE), a sharp, step-like increase in reflectance with increasing wavelength at ~700 nm. Future space telescopes like the Habitable Worlds Observatory (HWO) are designed to obtain disk-integrated spectra of Earth-like exoplanets in the visible-to-near-infrared to identify such features. However, there has been no systematic analysis of the occurrence of similar reflectance edges among minerals of non-biological origin. Here, we use existing databases of mineral reflectance spectra to explore the risk that minerals may present false positives in the search for biopigments on exoplanets. We find that several sulfide and tectosilicate minerals, as well as the prebiotically important cyanide salt, potassium ferrocyanide, have PRE-like features. We characterize these features in order to assess how they may be distinguished from biopigments. We conclude that the future evaluation of the biogenicity of PRE-like features in exoplanet reflectance spectra can be informed by the atmospheric context, but may require an assessment of the prior probability of non-biological and biological hypotheses about the surface materials of exoplanets.

astro-ph.EP↗

arXiv2026

A Multimodal and Explainable Machine Learning Approach to Diagnosing Multi-Class Ejection Fraction from Electrocardiograms

Left ventricular ejection fraction (LVEF) assessment depends on echocardiography, limiting access in primary care and resource-constrained settings. We developed a multimodal machine-learning framework that combines engineered 12-lead ECG timeseries features with structured EHR variables to classify LVEF into four clinically used strata: normal (>50%), mildly reduced (40-50%), moderately reduced (30-40%), and severely reduced (<30%). To support model explainability, we identified the most influential ECG and EHR features via SHAP attributions. Using retrospective data from Hartford HealthCare, we trained XGBoost models on 36,784 ECG-echocardiogram pairs from 30,952 outpatients and evaluated temporal generalizability on 19,966 ECGs from a subsequent period. The multimodal model achieved one-vs-rest AUROCs of 0.95 (severe), 0.92 (moderate), 0.82 (mild), and 0.91 (normal), outperforming ECG-only and EHR-only baselines, and maintained performance under temporal validation. This work supports ECG-based, multimodal LVEF stratification as a practical screening and triage aid to prioritize confirmatory imaging where resources are limited.

cs.LG↗

arXiv2026

Self-assembled versus biological pattern formation in geology

Both abiotic self-organization and biological mechanisms have been put forward as the origin of a number of geological patterns. It is important to comprehend the formation mechanisms of such structures both to understand geological self-organization and in order to differentiate them from biological patterns -- fossils and bio-influenced structures -- seen in geological systems. Being able to distinguish the traces of biological activity from geological self-organization is fundamental both for understanding the origin of life on Earth and for the search for life beyond Earth.

nlin.PS↗

arXiv2024

Self-Organized Pattern Formation in Geological Soft Matter

Geological materials are often seen as the antithesis of soft; rocks are hard. However, during the formation of minerals and rocks, all the systems we shall discuss, indeed geological materials in general, pass through a stage where they are soft. This occurs either because they form at a high temperature - igneous or metamorphic rock - or because they form at a lower temperature but in the presence of water - sedimentary rock. For this reason it is useful to introduce soft-matter concepts into the geological domain. There is a universality in the diverse instances of geological patterns that may be appreciated by looking at the common aspect in their formation of having passed through a stage as soft matter.

physics.geo-ph↗

arXiv2020

Tuning structural relaxations, mechanical properties, and degradation timescale of PLLA during hydrolytic degradation by blending with PLCL-PEG

Poly-L-lactide (PLLA) is a popular choice for medical devices due to its bioresorbability and superior mechanical properties compared with other polymers. However, although PLLA has been investigated for use in bioresorbable cardiovascular stents, it presents application-specific limitations which hamper device therapies. These include low toughness and strength compared with metals used for this purpose, and slow degradation. Blending PLLA with novel polyethylene glycol functionalised poly(L-lactide-co-$\varepsilon$-caprolactone) (PLCL-PEG) materials has been investigated here to tailor the mechanical properties and degradation behaviour of PLLA. This exciting approach provides a foundation for a next generation of bioresorbable materials whose properties can be rapidly tuned. The degradation of PLLA was significantly accelerated by addition of PLCL-PEG. After 30 days of degradation, several structural changes were observed in the polymer blends, which were dependent on the level of PLCL-PEG addition. Blends with low PLCL-PEG content displayed enthalpy relaxation, resulting in embrittlement, while blends with high PLCL-PEG content displayed crystallisation, due to enhanced chain mobility brought on by chain scission, also causing embrittlement. Moderate PLCL-PEG additions (10% PLCL(70:30)-PEG and 20 - 30% PLCL(80:20)-PEG) stabilised the structure, reducing the extent of enthalpy relaxation and crystallisation and thus retaining ductility. Compositional optimisation identified a sweet spot for this blend strategy, whereby the ductility was enhanced while maintaining strength. Our results indicate that blending PLLA with PLCL-PEG provides an effective method of tuning the degradation timescale and mechanical properties of PLLA, and provides important new insight into the mechanisms of structural relaxations that occur during degradation, and strategies for regulating these.

cond-mat.mtrl-sci↗

arXiv2017

Multi-Modal Trip Hazard Affordance Detection On Construction Sites

Trip hazards are a significant contributor to accidents on construction and manufacturing sites, where over a third of Australian workplace injuries occur [1]. Current safety inspections are labour intensive and limited by human fallibility,making automation of trip hazard detection appealing from both a safety and economic perspective. Trip hazards present an interesting challenge to modern learning techniques because they are defined as much by affordance as by object type; for example wires on a table are not a trip hazard, but can be if lying on the ground. To address these challenges, we conduct a comprehensive investigation into the performance characteristics of 11 different colour and depth fusion approaches, including 4 fusion and one non fusion approach; using colour and two types of depth images. Trained and tested on over 600 labelled trip hazards over 4 floors and 2000m$\mathrm{^{2}}$ in an active construction site,this approach was able to differentiate between identical objects in different physical configurations (see Figure 1). Outperforming a colour-only detector, our multi-modal trip detector fuses colour and depth information to achieve a 4% absolute improvement in F1-score. These investigative results and the extensive publicly available dataset moves us one step closer to assistive or fully automated safety inspection systems on construction sites.

cs.RO↗

arXiv2015

Place Categorization and Semantic Mapping on a Mobile Robot

In this paper we focus on the challenging problem of place categorization and semantic mapping on a robot without environment-specific training. Motivated by their ongoing success in various visual recognition tasks, we build our system upon a state-of-the-art convolutional network. We overcome its closed-set limitations by complementing the network with a series of one-vs-all classifiers that can learn to recognize new semantic classes online. Prior domain knowledge is incorporated by embedding the classification system into a Bayesian filter framework that also ensures temporal coherence. We evaluate the classification accuracy of the system on a robot that maps a variety of places on our campus in real-time. We show how semantic information can boost robotic object detection performance and how the semantic map can be used to modulate the robot's behaviour during navigation tasks. The system is made available to the community as a ROS module.

cs.RO↗