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Machine learning technology has the potential to dramatically optimise event generation and simulations. We continue to investigate the use of neural networks to approximate matrix elements for high-multiplicity scattering processes. We focus on the case of loop-induced diphoton production through gluon fusion, and develop a realistic simulation method that can be applied to hadron collider observables. Neural networks are trained using the one-loop amplitudes implemented in the NJet C++ library, and interfaced to the Sherpa Monte Carlo event generator, where we perform a detailed study for 2 → 3 and 2 → 4 scattering problems. We also consider how the trained networks perform when varying the kinematic cuts effecting the phase space and the reliability of the neural network simulations.
Joseph Aylett-Bullock; Simon Badger; Ryan Moodie. Optimising simulations for diphoton production at hadron colliders using amplitude neural networks. Journal of High Energy Physics 2021, 2021, 1 -30.
AMA StyleJoseph Aylett-Bullock, Simon Badger, Ryan Moodie. Optimising simulations for diphoton production at hadron colliders using amplitude neural networks. Journal of High Energy Physics. 2021; 2021 (8):1-30.
Chicago/Turabian StyleJoseph Aylett-Bullock; Simon Badger; Ryan Moodie. 2021. "Optimising simulations for diphoton production at hadron colliders using amplitude neural networks." Journal of High Energy Physics 2021, no. 8: 1-30.
We introduce J une , an open-source framework for the detailed simulation of epidemics on the basis of social interactions in a virtual population constructed from geographically granular census data, reflecting age, sex, ethnicity and socio-economic indicators. Interactions between individuals are modelled in groups of various sizes and properties, such as households, schools and workplaces, and other social activities using social mixing matrices. J une provides a suite of flexible parametrizations that describe infectious diseases, how they are transmitted and affect contaminated individuals. In this paper, we apply J une to the specific case of modelling the spread of COVID-19 in England. We discuss the quality of initial model outputs which reproduce reported hospital admission and mortality statistics at national and regional levels as well as by age strata.
Joseph Aylett-Bullock†; Carolina Cuesta-Lazaro†; Arnau Quera-Bofarull†; Miguel Icaza-Lizaola‡; Aidan Sedgewick‡; Henry Truong‡; Aoife Curran; Edward Elliott; Tristan Caulfield; Kevin Fong; Ian Vernon; Julian Williams; Richard Bower; Frank Krauss. J une : open-source individual-based epidemiology simulation. Royal Society Open Science 2021, 8, 1 .
AMA StyleJoseph Aylett-Bullock†, Carolina Cuesta-Lazaro†, Arnau Quera-Bofarull†, Miguel Icaza-Lizaola‡, Aidan Sedgewick‡, Henry Truong‡, Aoife Curran, Edward Elliott, Tristan Caulfield, Kevin Fong, Ian Vernon, Julian Williams, Richard Bower, Frank Krauss. J une : open-source individual-based epidemiology simulation. Royal Society Open Science. 2021; 8 (7):1.
Chicago/Turabian StyleJoseph Aylett-Bullock†; Carolina Cuesta-Lazaro†; Arnau Quera-Bofarull†; Miguel Icaza-Lizaola‡; Aidan Sedgewick‡; Henry Truong‡; Aoife Curran; Edward Elliott; Tristan Caulfield; Kevin Fong; Ian Vernon; Julian Williams; Richard Bower; Frank Krauss. 2021. "J une : open-source individual-based epidemiology simulation." Royal Society Open Science 8, no. 7: 1.
The spread of infectious diseases such as COVID-19 presents many challenges to healthcare systems and infrastructures across the world, exacerbating inequalities and leaving the world’s most vulnerable populations most affected. Given their density and available infrastructure, refugee and internally displaced person (IDP) settlements can be particularly susceptible to disease spread. Non-pharmaceutical public health interventions can be used to mitigate transmission, and modeling efforts can provide crucial insights on the potential effectiveness of such interventions to help inform decision making processes. In this paper we present an agent-based modeling approach to simulating the spread of disease in refugee and IDP settlements. The model, based on the JUNE open-source framework, is informed by data on geography, demographics, comorbidities, physical infrastructure and other parameters obtained from real-world observations and previous literature. Furthermore, we present a visual analytics tool which allows decision makers to distill insights by comparing the results of different simulations and scenarios. Through simulating their effects on the epidemiological development of COVID-19, we evaluate several public health interventions ranging from increasing mask wearing compliance to the reopening of learning institutions. The development and testing of this approach focuses on the Cox’s Bazar refugee settlement in Bangladesh, although our model is designed to be generalizable to other informal settings.
Joseph Bullock; Carolina Cuesta-Lazaro; Arnau Quera-Bofarull; Anjali Katta; Katherine Hoffmann Pham; Benjamin Hoover; Hendrik Strobelt; Rebeca Moreno Jimenez; Aidan Sedgewick; Egmond Samir Evers; David Kennedy; Sandra Harlass; Allen Gidraf Kahindo Maina; Ahmad Hussien; Miguel Luengo-Oroz. Operational response simulation tool for epidemics within refugee and IDP settlements. 2021, 1 .
AMA StyleJoseph Bullock, Carolina Cuesta-Lazaro, Arnau Quera-Bofarull, Anjali Katta, Katherine Hoffmann Pham, Benjamin Hoover, Hendrik Strobelt, Rebeca Moreno Jimenez, Aidan Sedgewick, Egmond Samir Evers, David Kennedy, Sandra Harlass, Allen Gidraf Kahindo Maina, Ahmad Hussien, Miguel Luengo-Oroz. Operational response simulation tool for epidemics within refugee and IDP settlements. . 2021; ():1.
Chicago/Turabian StyleJoseph Bullock; Carolina Cuesta-Lazaro; Arnau Quera-Bofarull; Anjali Katta; Katherine Hoffmann Pham; Benjamin Hoover; Hendrik Strobelt; Rebeca Moreno Jimenez; Aidan Sedgewick; Egmond Samir Evers; David Kennedy; Sandra Harlass; Allen Gidraf Kahindo Maina; Ahmad Hussien; Miguel Luengo-Oroz. 2021. "Operational response simulation tool for epidemics within refugee and IDP settlements." , no. : 1.
Rapid response to natural hazards, such as floods, is essential to mitigate loss of life and the reduction of suffering. For emergency response teams, access to timely and accurate data is essential. Satellite imagery offers a rich source of information which can be analysed to help determine regions affected by a disaster. Much remote sensing flood analysis is semi-automated, with time consuming manual components requiring hours to complete. In this study, we present a fully automated approach to the rapid flood mapping currently carried out by many non-governmental, national and international organisations. We design a Convolutional Neural Network (CNN) based method which isolates the flooded pixels in freely available Copernicus Sentinel-1 Synthetic Aperture Radar (SAR) imagery, requiring no optical bands and minimal pre-processing. We test a variety of CNN architectures and train our models on flood masks generated using a combination of classical semi-automated techniques and extensive manual cleaning and visual inspection. Our methodology reduces the time required to develop a flood map by 80%, while achieving strong performance over a wide range of locations and environmental conditions. Given the open-source data and the minimal image cleaning required, this methodology can also be integrated into end-to-end pipelines for more timely and continuous flood monitoring.
Edoardo Nemni; Joseph Bullock; Samir Belabbes; Lars Bromley. Fully Convolutional Neural Network for Rapid Flood Segmentation in Synthetic Aperture Radar Imagery. Remote Sensing 2020, 12, 2532 .
AMA StyleEdoardo Nemni, Joseph Bullock, Samir Belabbes, Lars Bromley. Fully Convolutional Neural Network for Rapid Flood Segmentation in Synthetic Aperture Radar Imagery. Remote Sensing. 2020; 12 (16):2532.
Chicago/Turabian StyleEdoardo Nemni; Joseph Bullock; Samir Belabbes; Lars Bromley. 2020. "Fully Convolutional Neural Network for Rapid Flood Segmentation in Synthetic Aperture Radar Imagery." Remote Sensing 12, no. 16: 2532.
Precision theoretical predictions for high multiplicity scattering rely on the evaluation of increasingly complicated scattering amplitudes which come with an extremely high CPU cost. For state-of-the-art processes this can cause technical bottlenecks in the production of fully differential distributions. In this article we explore the possibility of using neural networks to approximate multi-variable scattering amplitudes and provide efficient inputs for Monte Carlo integration. We focus on QCD corrections to e+e−→ jets up to one-loop and up to five jets. We demonstrate reliable interpolation when a series of networks are trained to amplitudes that have been divided into sectors defined by their infrared singularity structure. Complete simulations for one-loop distributions show speed improvements of at least an order of magnitude over a standard approach.
Simon Badger; Joseph Bullock. Using neural networks for efficient evaluation of high multiplicity scattering amplitudes. Journal of High Energy Physics 2020, 2020, 1 -26.
AMA StyleSimon Badger, Joseph Bullock. Using neural networks for efficient evaluation of high multiplicity scattering amplitudes. Journal of High Energy Physics. 2020; 2020 (6):1-26.
Chicago/Turabian StyleSimon Badger; Joseph Bullock. 2020. "Using neural networks for efficient evaluation of high multiplicity scattering amplitudes." Journal of High Energy Physics 2020, no. 6: 1-26.
Miguel Luengo-Oroz; Katherine Hoffmann Pham; Joseph Bullock; Robert Kirkpatrick; Alexandra Luccioni; Sasha Rubel; Cedric Wachholz; Moez Chakchouk; Phillippa Biggs; Tim Nguyen; Tina Purnat; Bernardo Mariano. Artificial intelligence cooperation to support the global response to COVID-19. Nature Machine Intelligence 2020, 2, 295 -297.
AMA StyleMiguel Luengo-Oroz, Katherine Hoffmann Pham, Joseph Bullock, Robert Kirkpatrick, Alexandra Luccioni, Sasha Rubel, Cedric Wachholz, Moez Chakchouk, Phillippa Biggs, Tim Nguyen, Tina Purnat, Bernardo Mariano. Artificial intelligence cooperation to support the global response to COVID-19. Nature Machine Intelligence. 2020; 2 (6):295-297.
Chicago/Turabian StyleMiguel Luengo-Oroz; Katherine Hoffmann Pham; Joseph Bullock; Robert Kirkpatrick; Alexandra Luccioni; Sasha Rubel; Cedric Wachholz; Moez Chakchouk; Phillippa Biggs; Tim Nguyen; Tina Purnat; Bernardo Mariano. 2020. "Artificial intelligence cooperation to support the global response to COVID-19." Nature Machine Intelligence 2, no. 6: 295-297.
Humanitarian response to natural disasters and conflicts can be assisted by satellite image analysis. In a humanitarian context, very specific satellite image analysis tasks must be done accurately and in a timely manner to provide operational support. We present PulseSatellite, a collaborative satellite image analysis tool which leverages neural network models that can be retrained on-the fly and adapted to specific humanitarian contexts and geographies. We present two case studies, in mapping shelters and floods respectively, that illustrate the capabilities of PulseSatellite.
Tomaz Logar; Joseph Bullock; Edoardo Nemni; Lars Bromley; John A. Quinn; Miguel Luengo-Oroz. PulseSatellite: A Tool Using Human-AI Feedback Loops for Satellite Image Analysis in Humanitarian Contexts. Proceedings of the AAAI Conference on Artificial Intelligence 2020, 34, 13628 -13629.
AMA StyleTomaz Logar, Joseph Bullock, Edoardo Nemni, Lars Bromley, John A. Quinn, Miguel Luengo-Oroz. PulseSatellite: A Tool Using Human-AI Feedback Loops for Satellite Image Analysis in Humanitarian Contexts. Proceedings of the AAAI Conference on Artificial Intelligence. 2020; 34 (9):13628-13629.
Chicago/Turabian StyleTomaz Logar; Joseph Bullock; Edoardo Nemni; Lars Bromley; John A. Quinn; Miguel Luengo-Oroz. 2020. "PulseSatellite: A Tool Using Human-AI Feedback Loops for Satellite Image Analysis in Humanitarian Contexts." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 9: 13628-13629.
X-Ray image enhancement, along with many other medical image processing applications, requires the segmentation of images into bone, soft tissue, and open beam regions. We apply a machine learning approach to this problem, presenting an end-to-end solution which results in robust and efficient inference. Since medical institutions often do not have the resources to process and label the large quantity of X-Ray images usually needed for neural network training, we design an end-to-end solution for small datasets, while achieving state-of-the-art results. Our implementation produces an overall accuracy of 92%, F1 score of 0.92, and an AUC of 0.98, surpassing classical image processing techniques, such as clustering and entropy based methods, while improving upon the output of existing neural networks used for segmentation in non-medical contexts. The code used for this project is available online.1
Joseph Bullock; Carolina Cuesta-Lazaro; Arnau Quera-Bofarull. XNet: a convolutional neural network (CNN) implementation for medical x-ray image segmentation suitable for small datasets. Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging 2019, 10953, 109531Z .
AMA StyleJoseph Bullock, Carolina Cuesta-Lazaro, Arnau Quera-Bofarull. XNet: a convolutional neural network (CNN) implementation for medical x-ray image segmentation suitable for small datasets. Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging. 2019; 10953 ():109531Z.
Chicago/Turabian StyleJoseph Bullock; Carolina Cuesta-Lazaro; Arnau Quera-Bofarull. 2019. "XNet: a convolutional neural network (CNN) implementation for medical x-ray image segmentation suitable for small datasets." Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging 10953, no. : 109531Z.