arXiv subjects

Andrew Williams

Publications and source records attributed to Andrew Williams.

At least 19 recordsLinked to original sources

arXiv2025

STORMY : A Real-time Triggering Framework using Yamagawa Solar Spectrograph for Active Solar Emission Observations with the MWA

Some of the most interesting insights into solar physics and space weather come from studying radio emissions associated with solar activity, which remain inherently unpredictable. Hence, a real-time triggering system is needed for solar observations with the versatile new-generation radio telescopes to efficiently capture these episodes of solar activity with the precious and limited solar observing time. We have developed such a system, Solar Triggered Observations of Radio bursts using MWA and Yamagawa (STROMY) for the Murchison Widefield Array (MWA), the precursor for the low frequency telescope of upcoming Square Kilometre Array Observatory (SKAO). It is based on near-real-time data from the Yamagawa solar spectrograph, located at a similar longitude to the MWA. We have devised, implemented, and tested algorithms to perform an effective denoising of the data to identify signatures of solar activity in the Yamagawa data in near real-time. End-to-end tests of triggered observations have been successfully carried out at the MWA. STORMY is operational at the MWA for the routine solar observations, a timely development in the view of the ongoing solar maximum. We present this new observing framework and discuss how it can enable efficient capturing of event-rich solar data with existing instruments, like the LOw Frequency ARray (LOFAR), Owens Valley Radio Observatory - Long Wavelength Array (OVRO-LWA) etc., and pave the way for triggered observing with the SKAO, especially the SKA-Low.

astro-ph.SR↗

arXiv2025

Standards in the Preparation of Biomedical Research Metadata: A Bridge2AI Perspective

AI-readiness describes the degree to which data may be optimally and ethically used for subsequent AI and Machine Learning (AI/ML) methods, where those methods may involve some combination of model training, data classification, and ethical, explainable prediction. The Bridge2AI consortium has defined the particular criteria a biomedical dataset may possess to render it AI-ready: in brief, a dataset's readiness is related to its FAIRness, provenance, degree of characterization, explainability, sustainability, and computability, in addition to its accompaniment with documentation about ethical data practices. To ensure AI-readiness and to clarify data structure and relationships within Bridge2AI's Grand Challenges (GCs), particular types of metadata are necessary. The GCs within the Bridge2AI initiative include four data-generating projects focusing on generating AI/ML-ready datasets to tackle complex biomedical and behavioral research problems. These projects develop standardized, multimodal data, tools, and training resources to support AI integration, while addressing ethical data practices. Examples include using voice as a biomarker, building interpretable genomic tools, modeling disease trajectories with diverse multimodal data, and mapping cellular and molecular health indicators across the human body. This report assesses the state of metadata creation and standardization in the Bridge2AI GCs, provides guidelines where required, and identifies gaps and areas for improvement across the program. New projects, including those outside the Bridge2AI consortium, would benefit from what we have learned about creating metadata as part of efforts to promote AI readiness.

q-bio.OT↗

arXiv2024

Call to Protect the Dark and Quiet Sky from Harmful Interference by Satellite Constellations

The growing number of satellite constellations in low Earth orbit (LEO) enhances global communications and Earth observation, and support of space commerce is a high priority of many governments. At the same time, the proliferation of satellites in LEO has negative effects on astronomical observations and research, and the preservation of the dark and quiet sky. These satellite constellations reflect sunlight onto optical telescopes, and their radio emission impacts radio observatories, jeopardising our access to essential scientific discoveries through astronomy. The changing visual appearance of the sky also impacts our cultural heritage and environment. Both ground-based observatories and space-based telescopes in LEO are affected, and there are no places on Earth that can escape the effects of satellite constellations given their global nature. The minimally disturbed dark and radio-quiet sky is crucial for conducting fundamental research in astronomy and important public services such as planetary defence, technology development, and high-precision geolocation. Some aspects of satellite deployment and operation are regulated by States and intergovernmental organisations. While regulatory agencies in some States have started to require operators to coordinate with their national astronomy agencies over impacts, mitigation of the impact of space objects on astronomical activities is not sufficiently regulated. To address this issue, the CPS urges States and the international community to take steps to protect the dark and quiet sky as specified in this paper.

astro-ph.IM↗

arXiv2024

Sustainable Skies and the Earth-Space Environment

The rapid launch of hundreds of thousands of satellites into Low Earth Orbit will significantly alter our view of the sky and raise concerns about the sustainability of Earth's orbital space. A new framework for sustainable space development must balance technological advancement, protection of space environments, and our capacity to explore the Universe.

astro-ph.IM↗

arXiv2023

A Near-Field Treatment of Aperture Synthesis Techniques using the Murchison Widefield Array

Typical radio interferometer observations are performed assuming the source of radiation to be in the far-field of the instrument, resulting in a two-dimensional Fourier relationship between the observed visibilities in the aperture plane and the sky brightness distribution (over a small field of view). When near-field objects are present in an observation, the standard approach applies far-field delays during correlation, resulting in loss of signal coherence for the signal from the near-field object. In this paper, we demonstrate near-field aperture synthesis techniques using a Murchison Widefield Array observation of the International Space Station (ISS), as it appears as a bright near-field object. We perform visibility phase corrections to restore coherence across the array for the near-field object (however not restoring coherence losses due to time and frequency averaging at the correlator). We illustrate the impact of the near-field corrections in the aperture plane and the sky plane. The aperture plane curves to match the curvature of the near-field wavefront, and in the sky plane near-field corrections manifest as fringe rotations at different rates as we bring the focal point of the array from infinity to the desired near-field distance. We also demonstrate the inverse scenario of inferring the line-of-sight range of the ISS by inverting the apparent curvature of the wavefront seen by the aperture. We conclude the paper by briefly discussing the limitations of the methods developed and the near-field science cases where our approach can be exploited.

astro-ph.IM↗

arXiv2023

Preserving your skies since 1988 -- Committee on Radio Astronomy Frequencies (CRAF) -- Periodic Review 2011-2021

The Committee on Radio Astronomy Frequencies (CRAF) is an Expert Committee of the European Science Foundation. It aims to provide a cost-effective single voice on frequency protection issues for European radio astronomy observatories and research institutes, achieving a significantly greater impact than that achievable by individual national institutions. By working together, European observatories and institutes can profit from synergy effects, cover many more topics, and learn from each other. CRAF was founded in 1988 and has since then been engaged with the International Telecommunication Union (ITU), in particular its Radiocommunication Sector (ITU-R), and the European Conference of Postal and Telecommunications Administrations (CEPT) and its European Communications Committee (ECC). This is the self-evaluation report prepared by CRAF for its periodic review of the years 2011-2021.

astro-ph.IM↗

arXiv2023

AI For Global Climate Cooperation 2023 Competition Proceedings

The international community must collaborate to mitigate climate change and sustain economic growth. However, collaboration is hard to achieve, partly because no global authority can ensure compliance with international climate agreements. Combining AI with climate-economic simulations offers a promising solution to design international frameworks, including negotiation protocols and climate agreements, that promote and incentivize collaboration. In addition, these frameworks should also have policy goals fulfillment, and sustained commitment, taking into account climate-economic dynamics and strategic behaviors. These challenges require an interdisciplinary approach across machine learning, economics, climate science, law, policy, ethics, and other fields. Towards this objective, we organized AI for Global Climate Cooperation, a Mila competition in which teams submitted proposals and analyses of international frameworks, based on (modifications of) RICE-N, an AI-driven integrated assessment model (IAM). In particular, RICE-N supports modeling regional decision-making using AI agents. Furthermore, the IAM then models the climate-economic impact of those decisions into the future. Whereas the first track focused only on performance metrics, the proposals submitted to the second track were evaluated both quantitatively and qualitatively. The quantitative evaluation focused on a combination of (i) the degree of mitigation of global temperature rise and (ii) the increase in economic productivity. On the other hand, an interdisciplinary panel of human experts in law, policy, sociology, economics and environmental science, evaluated the solutions qualitatively. In particular, the panel considered the effectiveness, simplicity, feasibility, ethics, and notions of climate justice of the protocols. In the third track, the participants were asked to critique and improve RICE-N.

cs.AI↗

arXiv2022

Ontologizing Health Systems Data at Scale: Making Translational Discovery a Reality

Background: Common data models solve many challenges of standardizing electronic health record (EHR) data, but are unable to semantically integrate all the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. Objective: We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. Results: Using OMOP2OBO, we produced mappings for 92,367 conditions, 8611 drug ingredients, and 10,673 measurement results, which covered 68-99% of concepts used in clinical practice when examined across 24 hospitals. When used to phenotype rare disease patients, the mappings helped systematically identify undiagnosed patients who might benefit from genetic testing. Conclusions: By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping.

cs.DB↗

arXiv2022

AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N

Comprehensive global cooperation is essential to limit global temperature increases while continuing economic development, e.g., reducing severe inequality or achieving long-term economic growth. Achieving long-term cooperation on climate change mitigation with n strategic agents poses a complex game-theoretic problem. For example, agents may negotiate and reach climate agreements, but there is no central authority to enforce adherence to those agreements. Hence, it is critical to design negotiation and agreement frameworks that foster cooperation, allow all agents to meet their individual policy objectives, and incentivize long-term adherence. This is an interdisciplinary challenge that calls for collaboration between researchers in machine learning, economics, climate science, law, policy, ethics, and other fields. In particular, we argue that machine learning is a critical tool to address the complexity of this domain. To facilitate this research, here we introduce RICE-N, a multi-region integrated assessment model that simulates the global climate and economy, and which can be used to design and evaluate the strategic outcomes for different negotiation and agreement frameworks. We also describe how to use multi-agent reinforcement learning to train rational agents using RICE-N. This framework underpinsAI for Global Climate Cooperation, a working group collaboration and competition on climate negotiation and agreement design. Here, we invite the scientific community to design and evaluate their solutions using RICE-N, machine learning, economic intuition, and other domain knowledge. More information can be found on www.ai4climatecoop.org.

cs.LG↗

arXiv2022

(Private)-Retroactive Carbon Pricing [(P)ReCaP]: A Market-based Approach for Climate Finance and Risk Assessment

Insufficient Social Cost of Carbon (SCC) estimation methods and short-term decision-making horizons have hindered the ability of carbon emitters to properly correct for the negative externalities of climate change, as well as the capacity of nations to balance economic and climate policy. To overcome these limitations, we introduce Retrospective Social Cost of Carbon Updating (ReSCCU), a novel mechanism that corrects for these limitations as empirically measured evidence is collected. To implement ReSCCU in the context of carbon taxation, we propose Retroactive Carbon Pricing (ReCaP), a market mechanism in which polluters offload the payment of ReSCCU adjustments to insurers. To alleviate systematic risks and minimize government involvement, we introduce the Private ReCaP (PReCaP) prediction market, which could see real-world implementation based on the engagement of a few high net-worth individuals or independent institutions.

cs.CY↗

arXiv2021

An Open Natural Language Processing Development Framework for EHR-based Clinical Research: A case demonstration using the National COVID Cohort Collaborative (N3C)

While we pay attention to the latest advances in clinical natural language processing (NLP), we can notice some resistance in the clinical and translational research community to adopt NLP models due to limited transparency, interpretability, and usability. In this study, we proposed an open natural language processing development framework. We evaluated it through the implementation of NLP algorithms for the National COVID Cohort Collaborative (N3C). Based on the interests in information extraction from COVID-19 related clinical notes, our work includes 1) an open data annotation process using COVID-19 signs and symptoms as the use case, 2) a community-driven ruleset composing platform, and 3) a synthetic text data generation workflow to generate texts for information extraction tasks without involving human subjects. The corpora were derived from texts from three different institutions (Mayo Clinic, University of Kentucky, University of Minnesota). The gold standard annotations were tested with a single institution's (Mayo) ruleset. This resulted in performances of 0.876, 0.706, and 0.694 in F-scores for Mayo, Minnesota, and Kentucky test datasets, respectively. The study as a consortium effort of the N3C NLP subgroup demonstrates the feasibility of creating a federated NLP algorithm development and benchmarking platform to enhance multi-institution clinical NLP study and adoption. Although we use COVID-19 as a use case in this effort, our framework is general enough to be applied to other domains of interest in clinical NLP.

cs.CL↗

arXiv2021

Model calibration using ESEm v1.0.0 -- an open, scalable Earth System Emulator

Large computer models are ubiquitous in the earth sciences. These models often have tens or hundreds of tuneable parameters and can take thousands of core-hours to run to completion while generating terabytes of output. It is becoming common practice to develop emulators as fast approximations, or surrogates, of these models in order to explore the relationships between these inputs and outputs, understand uncertainties and generate large ensembles datasets. While the purpose of these surrogates may differ, their development is often very similar. Here we introduce ESEm: an open-source tool providing a general workflow for emulating and validating a wide variety of models and outputs. It includes efficient routines for sampling these emulators for the purpose of uncertainty quantification and model calibration. It is built on well-established, high-performance libraries to ensure robustness, extensibility and scalability. We demonstrate the flexibility of ESEm through three case-studies using ESEm to reduce parametric uncertainty in a general circulation model, explore precipitation sensitivity in a cloud resolving model and scenario uncertainty in the CMIP6 multi-model ensemble.

physics.ao-ph↗

arXiv2021

A Report to ESO Council on the Impact of Satellite Constellations

Up to 100,000 satellites could be launched into Low Earth Orbit (LEO) in the coming decade. Assuming the two most advanced companies' plans are realised, close to 80,000 satellites will be present at a variety of altitudes between 328 - 1,325 km. At Paranal, more than 5,000 satellites will be over the horizon at any given time. Of these, depending on the hour of night and season, a few hundred to several thousand will be illuminated by the sun and potentially detectable. Satellites show a very strong concentration towards the local horizon, with over 50% of the satellites below 20 degrees elevation. This report informs ESO's Council of the impacts on ESO facilities, mitigation measures that ESO could adopt in the future, and the various community efforts in which ESO is involved.

astro-ph.IM↗

arXiv2021

Analysing the impact of satellite constellations and ESO's role in supporting the astronomy community

In the coming decade, up to 100 000 satellites in large constellations could be launched into low Earth orbit. The satellites will introduce a variety of negative impacts on astronomy observatories and science, which vary from negligible to very disruptive depending on the type of instrument, the position of the science target, and the nature of the constellation. Since the launch of the first batch of SpaceX's Starlink constellation in 2019, the astronomy community has made substantial efforts to analyse the problem and to engage with satellite operators and government agencies. This article presents a short summary of the simulations of impacts on ESO's optical and infrared facilities and ALMA, as well as the conducted observational campaigns to assess the brightness of satellites. It also discusses several activities to identify policy solutions at the international and national level.

astro-ph.IM↗

arXiv2020

COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing

The rapid global spread of COVID-19 has led to an unprecedented demand for effective methods to mitigate the spread of the disease, and various digital contact tracing (DCT) methods have emerged as a component of the solution. In order to make informed public health choices, there is a need for tools which allow evaluation and comparison of DCT methods. We introduce an agent-based compartmental simulator we call COVI-AgentSim, integrating detailed consideration of virology, disease progression, social contact networks, and mobility patterns, based on parameters derived from empirical research. We verify by comparing to real data that COVI-AgentSim is able to reproduce realistic COVID-19 spread dynamics, and perform a sensitivity analysis to verify that the relative performance of contact tracing methods are consistent across a range of settings. We use COVI-AgentSim to perform cost-benefit analyses comparing no DCT to: 1) standard binary contact tracing (BCT) that assigns binary recommendations based on binary test results; and 2) a rule-based method for feature-based contact tracing (FCT) that assigns a graded level of recommendation based on diverse individual features. We find all DCT methods consistently reduce the spread of the disease, and that the advantage of FCT over BCT is maintained over a wide range of adoption rates. Feature-based methods of contact tracing avert more disability-adjusted life years (DALYs) per socioeconomic cost (measured by productive hours lost). Our results suggest any DCT method can help save lives, support re-opening of economies, and prevent second-wave outbreaks, and that FCT methods are a promising direction for enriching BCT using self-reported symptoms, yielding earlier warning signals and a significantly reduced spread of the virus per socioeconomic cost.

cs.CY↗

arXiv2020

Predicting Infectiousness for Proactive Contact Tracing

The COVID-19 pandemic has spread rapidly worldwide, overwhelming manual contact tracing in many countries and resulting in widespread lockdowns for emergency containment. Large-scale digital contact tracing (DCT) has emerged as a potential solution to resume economic and social activity while minimizing spread of the virus. Various DCT methods have been proposed, each making trade-offs between privacy, mobility restrictions, and public health. The most common approach, binary contact tracing (BCT), models infection as a binary event, informed only by an individual's test results, with corresponding binary recommendations that either all or none of the individual's contacts quarantine. BCT ignores the inherent uncertainty in contacts and the infection process, which could be used to tailor messaging to high-risk individuals, and prompt proactive testing or earlier warnings. It also does not make use of observations such as symptoms or pre-existing medical conditions, which could be used to make more accurate infectiousness predictions. In this paper, we use a recently-proposed COVID-19 epidemiological simulator to develop and test methods that can be deployed to a smartphone to locally and proactively predict an individual's infectiousness (risk of infecting others) based on their contact history and other information, while respecting strong privacy constraints. Predictions are used to provide personalized recommendations to the individual via an app, as well as to send anonymized messages to the individual's contacts, who use this information to better predict their own infectiousness, an approach we call proactive contact tracing (PCT). We find a deep-learning based PCT method which improves over BCT for equivalent average mobility, suggesting PCT could help in safe re-opening and second-wave prevention.

cs.LG↗

arXiv2020

Mechanics of Topologically Interlocked Material Systems under Point Load: Archimedean and Laves Tiling

Topologically interlocked material systems are two-dimensional assemblies of unit elements from which no element can be removed from the assembly without disassembly of the entire system. Consequently, such tile assemblies are able to carry transverse mechanical loads. Archimedean and Laves tilings are investigated as templates for the material system architecture. It is demonstrated under point loads that the architecture significantly affects the force-deflection response. Stiffness, load carrying capacity and toughness varied by a factor of at least three from the system with the poorest performance to the system with the best performance. Across all architectures stiffness, strength and toughness are found to be strongly and linearly correlated. Architecture characterizing parameters and their relationship to the mechanical behavior are investigated. It is shown that the measure of the smallest tile area in an assembly provides the best predictor of mechanical behavior. With small tiles present in the assembly the contact force network structure is well developed and the internal load path is channeled through these stiffest components of the assembly.

cond-mat.mtrl-sci↗

arXiv2019

Using negative-latency gravitational wave alerts to detect prompt radio bursts from binary neutron star mergers with the Murchison Widefield Array

We examine how fast radio burst (FRB)-like signals predicted to be generated during the merger of a binary neutron star (BNS) may be detected in low-frequency radio observations triggered by the aLIGO/Virgo gravitational wave detectors. The rapidity, directional accuracy, and sensitivity of follow-up observations with the Murchison Widefield Array (MWA) are considered. We show that with current methodology, the rapidity criteria fails for triggered MWA observations above 136 MHz for BNS mergers within the aLIGO/Virgo horizon, for which little dispersive delay is expected. A calculation of the expected reduction in response time by triggering on `negative latency' alerts from aLIGO/Virgo observations of gravitational waves generated by the BNS inspiral is presented. This allows for observations up to 300 MHz where the radio signal is expected to be stronger. To compensate for the poor positional accuracy expected from these alerts, we propose a new MWA observational mode that is capable of viewing one quarter of the sky. We show the sensitivity of this mode is sufficient to detect an FRB-like burst from an event similar to GW170817 if it occurred during the ongoing aLIGO/Virgo third science run (O3).

astro-ph.HE↗