arXiv subjects

David Butler

Publications and source records attributed to David Butler.

At least 19 recordsLinked to original sources

arXiv2026

The design of selection experiments using a model-based approach

Plant breeding programs use data obtained from multi-environment selection experiments to produce improved varieties with the ultimate aim of maintaining high levels of genetic gain. Selection accuracy can be improved with the use of advanced statistical analytical methods that use informative and parsimonious variance models for the set of genotype by environment interaction effects, include information on genetic relatedness and appropriately accommodate non-genetic sources of variation within the framework of a single step estimation and prediction algorithm. Maximal gains from using these advanced techniques are more likely to be achieved if the designs used match the aims of the selection experiment and make full use of the available resources. In this paper we present an approach for constructing designs for selection experiments which are optimal or near optimal against a robust and sensible linear mixed model. This model reflects the models used for analysis. The approach is flexible and introduces an additional step to accommodate efficient resource allocation of replication status to genotypes, which is undertaken prior to the allocation of plots to genotypes. A motivating example is used to illustrate the approach, two illustrative examples are presented one each for single and multiple environment selection experiments and several in-silico simulation studies are used to demonstrate the advantages of these approaches.

stat.ME↗

arXiv2025

Real-Time Transmission of Uncompressed High-Definition Video Via A VCSEL-Based Optical Wireless Link With Ultra-Low Latency

Real-time transmission of high-resolution video signals in an uncompressed and unencrypted format requires an ultra-reliable and low-latency communications (URLLC) medium with high bandwidth to maintain the quality of experience (QoE) for users. We put forward the design and experimental demonstration of a high-performance laser-based optical wireless communication (OWC) system that enables high-definition (HD) video transmission with submillisecond latencies. The serial digital interface (SDI) output of a camera is used to transmit the live video stream over an optical wireless link by directly modulating the SDI signal on the intensity of a 940 nm vertical cavity surface emitting laser (VCSEL). The proposed SDI over light fidelity (LiFi) system corroborates error-free transmission of full HD (FHD) and 4K ultra-high-definition (UHD) resolutions at data rates of 2.97 Gb/s and 5.94 Gb/s, respectively, with a measured end-to-end latency of under 35 ns. Since SDI standards support various video formats and VCSELs are high-bandwidth and low-power devices, this presents a scalable and inexpensive solution for wireless connectivity between professional broadcast equipment using off-the-shelf SDI components.

eess.SP↗

arXiv2025

Risk Estimation of Knee Osteoarthritis Progression via Predictive Multi-task Modelling from Efficient Diffusion Model using X-ray Images

Medical imaging plays a crucial role in assessing knee osteoarthritis (OA) risk by enabling early detection and disease monitoring. Recent machine learning methods have improved risk estimation (i.e., predicting the likelihood of disease progression) and predictive modelling (i.e., the forecasting of future outcomes based on current data) using medical images, but clinical adoption remains limited due to their lack of interpretability. Existing approaches that generate future images for risk estimation are complex and impractical. Additionally, previous methods fail to localize anatomical knee landmarks, limiting interpretability. We address these gaps with a new interpretable machine learning method to estimate the risk of knee OA progression via multi-task predictive modelling that classifies future knee OA severity and predicts anatomical knee landmarks from efficiently generated high-quality future images. Such image generation is achieved by leveraging a diffusion model in a class-conditioned latent space to forecast disease progression, offering a visual representation of how particular health conditions may evolve. Applied to the Osteoarthritis Initiative dataset, our approach improves the state-of-the-art (SOTA) by 2\%, achieving an AUC of 0.71 in predicting knee OA progression while offering ~9% faster inference time.

cs.CV↗

arXiv2024

Template synthesis approach for radio emission from extensive air showers

We present a novel way to synthesise the radio emission from extensive air showers. It is a hybrid approach which uses a single microscopic Monte-Carlo simulation to generate the radio emission from a shower with a different longitudinal evolution, primary particle type and energy. The method employs semi-analytical relations which only depend on the shower parameters to transform the radio signal in the simulated antennas. We apply this method to vertical air showers with energies ranging from $10^{17}$ eV to $10^{19}$ eV and compare the results with CoREAS using two different metrics. In order to gauge the performance over our simulation set, we subsequently use every shower in the set as a template to synthesise the emission from the other showers. Depending on the scoring metric, template synthesis reconstructs the radio emission with an accuracy of 5 to 10%.

astro-ph.HE↗

arXiv2024

Human-AI Collaborative Multi-modal Multi-rater Learning for Endometriosis Diagnosis

Endometriosis, affecting about 10% of individuals assigned female at birth, is challenging to diagnose and manage. Diagnosis typically involves the identification of various signs of the disease using either laparoscopic surgery or the analysis of T1/T2 MRI images, with the latter being quicker and cheaper but less accurate. A key diagnostic sign of endometriosis is the obliteration of the Pouch of Douglas (POD). However, even experienced clinicians struggle with accurately classifying POD obliteration from MRI images, which complicates the training of reliable AI models. In this paper, we introduce the Human-AI Collaborative Multi-modal Multi-rater Learning (HAICOMM) methodology to address the challenge above. HAICOMM is the first method that explores three important aspects of this problem: 1) multi-rater learning to extract a cleaner label from the multiple "noisy" labels available per training sample; 2) multi-modal learning to leverage the presence of T1/T2 MRI images for training and testing; and 3) human-AI collaboration to build a system that leverages the predictions from clinicians and the AI model to provide more accurate classification than standalone clinicians and AI models. Presenting results on the multi-rater T1/T2 MRI endometriosis dataset that we collected to validate our methodology, the proposed HAICOMM model outperforms an ensemble of clinicians, noisy-label learning models, and multi-rater learning methods.

cs.CV↗

arXiv2023

Proof of principle for template synthesis approach for the radio emission from vertical extensive air showers

The radio detection technique of cosmic ray air showers has gained renewed interest in the last two decades. While the radio experiments are very cost-effective to deploy, the Monte-Carlo simulations required to analyse the data are computationally expensive. Here we present a proof of concept for a novel way to synthesise the radio emission from extensive air showers in simulations. It is a hybrid approach which uses a single microscopic Monte-Carlo simulation, called the origin shower, to generate the radio emission from a target shower with a different longitudinal evolution, primary particle type and energy. The method employs semi-analytical relations which only depend on the shower parameters to transform the radio signals in the simulated antennas. We apply this method to vertical air showers with energies ranging from $10^{17}$ eV to $10^{19}$ eV and compare the results with CoREAS simulations in two frequency bands, namely the broad [20, 500] MHz band and a more narrow one at [30, 80] MHz. We gauge the synthesis quality using the maximal amplitude and energy fluence contained in the signal. We observe that the quality depends primarily on the difference in $X_{\text{max}}$ between the origin and target shower. After applying a linear bias correction, we find that for a shift in $X_{\text{max}}$ of less than 150 $\text{g}/\text{cm}^2$ , template synthesis has a bias of less than 2% and a scatter up to 6%, both in amplitude, on the broad frequency range. On the restricted [30, 80] MHz range the bias is similar, but the spread on amplitude drops down to 3%. These fluctuations are on the same level as the intrinsic scatter we observe in Monte-Carlo ensembles. We therefore surmise the observed scatter in amplitude to originate from intrinsic shower fluctuations we do not explicitly account for in template synthesis.

astro-ph.HE↗

arXiv2023

Distilling Missing Modality Knowledge from Ultrasound for Endometriosis Diagnosis with Magnetic Resonance Images

Endometriosis is a common chronic gynecological disorder that has many characteristics, including the pouch of Douglas (POD) obliteration, which can be diagnosed using Transvaginal gynecological ultrasound (TVUS) scans and magnetic resonance imaging (MRI). TVUS and MRI are complementary non-invasive endometriosis diagnosis imaging techniques, but patients are usually not scanned using both modalities and, it is generally more challenging to detect POD obliteration from MRI than TVUS. To mitigate this classification imbalance, we propose in this paper a knowledge distillation training algorithm to improve the POD obliteration detection from MRI by leveraging the detection results from unpaired TVUS data. More specifically, our algorithm pre-trains a teacher model to detect POD obliteration from TVUS data, and it also pre-trains a student model with 3D masked auto-encoder using a large amount of unlabelled pelvic 3D MRI volumes. Next, we distill the knowledge from the teacher TVUS POD obliteration detector to train the student MRI model by minimizing a regression loss that approximates the output of the student to the teacher using unpaired TVUS and MRI data. Experimental results on our endometriosis dataset containing TVUS and MRI data demonstrate the effectiveness of our method to improve the POD detection accuracy from MRI.

eess.IV↗

arXiv2022

In Defense of Kalman Filtering for Polyp Tracking from Colonoscopy Videos

Real-time and robust automatic detection of polyps from colonoscopy videos are essential tasks to help improve the performance of doctors during this exam. The current focus of the field is on the development of accurate but inefficient detectors that will not enable a real-time application. We advocate that the field should instead focus on the development of simple and efficient detectors that an be combined with effective trackers to allow the implementation of real-time polyp detectors. In this paper, we propose a Kalman filtering tracker that can work together with powerful, but efficient detectors, enabling the implementation of real-time polyp detectors. In particular, we show that the combination of our Kalman filtering with the detector PP-YOLO shows state-of-the-art (SOTA) detection accuracy and real-time processing. More specifically, our approach has SOTA results on the CVC-ClinicDB dataset, with a recall of 0.740, precision of 0.869, $F_1$ score of 0.799, an average precision (AP) of 0.837, and can run in real time (i.e., 30 frames per second). We also evaluate our method on a subset of the Hyper-Kvasir annotated by our clinical collaborators, resulting in SOTA results, with a recall of 0.956, precision of 0.875, $F_1$ score of 0.914, AP of 0.952, and can run in real time.

cs.CV↗

arXiv2020

Beyond COVID-19: Network science and sustainable exit strategies

On May $28^{th}$ and $29^{th}$, a two day workshop was held virtually, facilitated by the Beyond Center at ASU and Moogsoft Inc. The aim was to bring together leading scientists with an interest in Network Science and Epidemiology to attempt to inform public policy in response to the COVID-19 pandemic. Epidemics are at their core a process that progresses dynamically upon a network, and are a key area of study in Network Science. In the course of the workshop a wide survey of the state of the subject was conducted. We summarize in this paper a series of perspectives of the subject, and where the authors believe fruitful areas for future research are to be found.

physics.soc-ph↗

arXiv2020

Differentially Private Health Tokens for Estimating COVID-19 Risk

In the fight against Covid-19, many governments and businesses are in the process of evaluating, trialling and even implementing so-called immunity passports. Also known as antibody or health certificates, there is a clear demand for any technology that could allow people to return to work and other crowded places without placing others at risk. One of the major criticisms of such systems is that they could be misused to unfairly discriminate against those without immunity, allowing the formation of an `immuno-privileged' class of people. In this work we are motivated to explore an alternative technical solution that is non-discriminatory by design. In particular we propose health tokens -- randomised health certificates which, using methods from differential privacy, allow individual test results to be randomised whilst still allowing useful aggregate risk estimates to be calculated. We show that health tokens could mitigate immunity-based discrimination whilst still presenting a viable mechanism for estimating the collective transmission risk posed by small groups of users. We evaluate the viability of our approach in the context of identity-free and identity-binding use cases and then consider a number of possible attacks. Our experimental results show that for groups of size 500 or more, the error associated with our method can be as low as 0.03 on average and thus the aggregated results can be useful in a number of identity-free contexts. Finally, we present the results of our open-source prototype which demonstrates the practicality of our solution.

cs.CR↗

arXiv2020

SecureABC: Secure AntiBody Certificates for COVID-19

COVID-19 has resulted in unprecedented social distancing policies being enforced worldwide. As governments seek to restore their economies, open workplaces and permit travel there is a demand for technologies that may alleviate the requirement for social distancing whilst also protecting healthcare services. In this work we explore the controversial technique of so-called immunity passports and present SecureABC: a decentralised, privacy-preserving protocol for issuing and verifying antibody certificates. We consider the implications of antibody certificate systems, develop a set of risk-minimising principles and a security framework for their evaluation, and show that these may be satisfied in practice. Finally, we also develop two additional protocols that minimise individual discrimination but which still allow for collective transmission risk to be estimated. We use these two protocols to illustrate the utility-privacy trade-offs of antibody certificates and their alternatives.

cs.CR↗

arXiv2020

TraceSecure: Towards Privacy Preserving Contact Tracing

Contact tracing is being widely employed to combat the spread of COVID-19. Many apps have been developed that allow for tracing to be done automatically based off location and interaction data generated by users. There are concerns, however, regarding the privacy and security of users data when using these apps. These concerns are paramount for users who contract the virus, as they are generally required to release all their data. Motivated by the need to protect users privacy we propose two solutions to this problem. Our first solution builds on current "message based" methods and our second leverages ideas from secret sharing and additively homomorphic encryption.

cs.CR↗

arXiv2019

Universality and template synthesis of cosmic ray air shower radio emission

Accurate prediction of the radio emission from cosmic ray air showers relies on computationally demanding Monte Carlo simulations such as CoREAS. We aim to expedite this process via a semi-analytical synthesis model while maintaining high accuracy by using simulated radio pulses as templates. We present our key concept for template processing focusing on the development of the particle cascade and its empirical effect on the locally produced radio signal. In this context the universality of the radio emission from small sections of an air shower also becomes important where most previous studies focus on integral quantities observable at far distances.

astro-ph.HE↗

arXiv2018

How to Simulate It in Isabelle: Towards Formal Proof for Secure Multi-Party Computation

In cryptography, secure Multi-Party Computation (MPC) protocols allow participants to compute a function jointly while keeping their inputs private. Recent breakthroughs are bringing MPC into practice, solving fundamental challenges for secure distributed computation. Just as with classic protocols for encryption and key exchange, precise guarantees are needed for MPC designs and implementations; any flaw will give attackers a chance to break privacy or correctness. In this paper we present the first (as far as we know) formalisation of some MPC security proofs. These proofs provide probabilistic guarantees in the computational model of security, but have a different character to machine proofs and proof tools implemented so far --- MPC proofs use a \emph{simulation} approach, in which security is established by showing indistinguishability between execution traces in the actual protocol execution and an ideal world where security is guaranteed by definition. We show that existing machinery for reasoning about probabilistic programs adapted to this setting, paving the way to precisely check a new class of cryptography arguments. We implement our proofs using the CryptHOL framework inside Isabelle/HOL.

cs.CR↗

arXiv2017

Towards a fast and precise forward model for air shower radio simulation

The radio detection method for cosmic rays relies on coherent emission from electrons and positrons which is beamed in a narrow cone along the shower axis. Currently the only mod- els to reproduce this emission with sufficient accuracy are Monte Carlo based simulations of the particle and radio emission physics, which require large investments of computation time. The work presented here focuses on condensing the simulation results into a semi-analytical model. This relies on building a framework based on theoretical predictions of radio emission, but instead of calculating the radio signal directly these models are used to map template simu- lations to the specifications of a given radio event. Our current approach slices the radio signal based on atmospheric depth of origin and weights these slices based on a shower parameter such as electron number or an effective dipole moment. One significant gain over the existing Monte Carlo codes lies in the fact this makes the depth of the shower maximum a direct input to the simulation where currently one has to pre-select showers based on their random number seed. Such a model has great potential for heavily simulation-based analysis methods, for example the LOFAR air shower reconstruction. These techniques are severely limited by the available computation time but have the lowest errors in real measurement applications.

astro-ph.HE↗

arXiv2017

R Package ASMap: Efficient Genetic Linkage Map Construction and Diagnosis

Although various forms of linkage map construction software are widely available, there is a distinct lack of packages for use in the R statistical computing environment. This article introduces the ASMap linkage map construction R package which contains functions that use the efficient MSTmap algorithm for clustering and optimally ordering large sets of markers. Additional to the construction functions, the package also contains a suite of tools to assist in the rapid diagnosis and repair of a constructed linkage map. The package functions can also be used for post linkage map construction techniques such as fine mapping or combining maps of the same population. To showcase the efficiency and functionality of ASMap, the complete linkage map construction process is demonstrated with a high density barley backcross marker data set.

stat.CO↗

arXiv2016

Ultimate precision in cosmic-ray radio detection --- the SKA

As of 2023, the low-frequency part of the Square Kilometre Array will go online in Australia. It will constitute the largest and most powerful low-frequency radio-astronomical observatory to date, and will facilitate a rich science programme in astronomy and astrophysics. With modest engineering changes, it will also be able to measure cosmic rays via the radio emission from extensive air showers. The extreme antenna density and the homogeneous coverage provided by more than 60,000 antennas within an area of one km$^2$ will push radio detection of cosmic rays in the energy range around 10$^{17}$ eV to ultimate precision, with superior capabilities in the reconstruction of arrival direction, energy, and an expected depth-of-shower-maximum resolution of 6~g/cm${^2}$.

astro-ph.IM↗

arXiv2005

Absolute Proper Motion of the Canis Major Dwarf Galaxy Candidate

We have measured the absolute proper motion of the candidate Canis Major dwarf galaxy (CMa) at $(l,b) = (240\arcdeg, -8\arcdeg)$. Likely main-sequence stars in CMa have been selected from a region in the color-magnitude diagram that has very little contamination from known Milky Way components. We obtain $\mu_{l} cos b = -1.47 \pm 0.37$ and $\mu_{b} = -1.07 \pm 0.38$ mas yr$^{-1}$, on the ICRS system via Hipparcos stars. Together with the radial velocity of 109 km s$^{-1}$, and the assumed distance of 8 kpc, these results imply a space motion of $(\Pi, \Theta, W) = (-5 \pm 12, 188 \pm 10, -49 \pm 15)$ km s$^{-1}$. While CMa has in-plane rotation similar to the mean of thick disk stars, it shows significant $(3\sigma)$ motion perpendicular to the disk, and differs even more $(7\sigma)$ from that expected for the Galactic warp. The $W$ velocity lends support to the argument that the CMa overdensity is part of a satellite galaxy remnant.

astro-ph↗