Understanding Optimisation Processes with Biologically-Inspired Visualisations

Evolutionary algorithms (EAs) constitute a branch of artificial intelligence utilised to evolve solutions to solve optimisation problems abound in industry and research. EAs often generate many solutions and visualisation has been a primary strategy to display EA solutions, given that visualisation is a multi-domain well-evaluated medium to comprehend extensive data. The endeavour of visualising solutions is inherent with challenges resulting from high dimensional phenomenons and the large number of solutions to display. Recently, scholars have produced methods to mitigate some of these known issues when illustrating solutions. However, one key consideration is that displaying the final subset of solutions exclusively (rather than the whole population) discards most of the informativeness of the search, creating inadequate insight into the black-box EA. There is an unequivocal knowledge gap and requirement for methods which can visualise the whole population of solutions from an optimiser and subjugate the high-dimensional problems and scaling issues to create interpretability of the EA search process. Furthermore, a requirement for explainability in evolutionary computing has been demanded by the evolutionary computing community, which could take the form of visualisations, to support EA comprehension much like the support explainable artificial intelligence has brought to artificial intelligence. In this thesis, we report novel visualisation methods that can be used to visualise large and high-dimensional optimiser populations with the aim of creating greater interpretability during a search. We consider the nascent intersection of visualisation and explainability in evolutionary computing. The potential high informativeness of a visualisation method from an early chapter of this work forms an effective platform to develop an explainability visualisation method, namely the population dynamics plot, to attempt to inject explainability into the inner workings of the search process. We further support the visualisation of populations using machine learning to construct models which can capture the characteristics of an EA search and develop intelligent visualisations which use artificial intelligence to potentially enhance and support visualisation for a more informative search process. The methods developed in this thesis are evaluated both quantitatively and qualitatively. We use multi-feature benchmark problems to show the method’s ability to reveal specific problem characteristics such as disconnected fronts, local optima and bias, as well as potentially creating a better understanding of the problem landscape and optimiser search for evaluating and comparing algorithm performance (we show the visualisation method to be more insightful than conventional metrics like hypervolume alone). One of the most insightful methods developed in this thesis can produce a visualisation requiring less than 1% of the time and memory necessary to produce a visualisation of the same objective space solutions using existing methods. This allows for greater scalability and the use in short compile time applications such as online visualisations. Predicated by an existing visualisation method in this thesis, we then develop and apply an explainability method to a real-world problem and evaluate it to show the method to be highly effective at explaining the search via solutions in the objective spaces, solution lineage and solution variation operators to compactly comprehend, evaluate and communicate the search of an optimiser, although we note the explainability properties are only evaluated against the author’s ability and could be evaluated further in future work with a usability study. The work is then supported by the development of intelligent visualisation models that may allow one to predict solutions in optima (importantly local optima) in unseen problems by using a machine learning model. The results are effective, with some models able to predict and visualise solution optima with a balanced F1 accuracy metric of 96%. The results of this thesis provide a suite of visualisations which aims to provide greater informativeness of the search and scalability than previously existing literature. The work develops one of the first explainability methods aiming to create greater insight into the search space, solution lineage and reproductive operators. The work applies machine learning to potentially enhance EA understanding via visualisation. These models could also be used for a number of applications outside visualisation. Ultimately, the work provides novel methods for all EA stakeholders which aims to support understanding, evaluation and communication of EA processes with visualisation

An automated machine learning approach to predict brain age from cortical anatomical measures

The use of machine learning (ML) algorithms has significantly increased in neuroscience. However, from the vast extent of possible ML algorithms, which one is the optimal model to predict the target variable? What are the hyperparameters for such a model? Given the plethora of possible answers to these questions, in the last years, automated ML (autoML) has been gaining attention. Here, we apply an autoML library called Tree-based Pipeline Optimisation Tool (TPOT) which uses a tree-based representation of ML pipelines and conducts a genetic programming-based approach to find the model and its hyperparameters that more closely predicts the subject's true age. To explore autoML and evaluate its efficacy within neuroimaging data sets, we chose a problem that has been the focus of previous extensive study: brain age prediction. Without any prior knowledge, TPOT was able to scan through the model space and create pipelines that outperformed the state-of-the-art accuracy for Freesurfer-based models using only thickness and volume information for anatomical structure. In particular, we compared the performance of TPOT (mean absolute error [MAE]: 4.612 ± .124 years) and a relevance vector regression (MAE 5.474 ± .140 years). TPOT also suggested interesting combinations of models that do not match the current most used models for brain prediction but generalise well to unseen data. AutoML showed promising results as a data-driven approach to find optimal models for neuroimaging applications

Global Precipitation Measurement Cold Season Precipitation Experiment (GCPEx): For Measurement Sake Let it Snow

As a component of the Earth's hydrologic cycle, and especially at higher latitudes,falling snow creates snow pack accumulation that in turn provides a large proportion of the fresh water resources required by many communities throughout the world. To assess the relationships between remotely sensed snow measurements with in situ measurements, a winter field project, termed the Global Precipitation Measurement (GPM) mission Cold Season Precipitation Experiment (GCPEx), was carried out in the winter of 2011-2012 in Ontario, Canada. Its goal was to provide information on the precipitation microphysics and processes associated with cold season precipitation to support GPM snowfall retrieval algorithms that make use of a dual-frequency precipitation radar and a passive microwave imager on board the GPM core satellite,and radiometers on constellation member satellites. Multi-parameter methods are required to be able to relate changes in the microphysical character of the snow to measureable parameters from which precipitation detection and estimation can be based. The data collection strategy was coordinated, stacked, high-altitude and in-situ cloud aircraft missions with three research aircraft sampling within a broader surface network of five ground sites taking in-situ and volumetric observations. During the field campaign 25 events were identified and classified according to their varied precipitation type, synoptic context, and precipitation amount. Herein, the GCPEx fieldcampaign is described and three illustrative cases detailed

Development and Validation of an OMERACT MRI Whole-Body Score for Inflammation in Peripheral Joints and Entheses in Inflammatory Arthritis (MRI-WIPE)

Objective: To develop a whole-body MRI-scoring system for peripheral arthritis and enthesitis. Methods: After consensus on definitions/locations of MRI pathologies, four multi-reader exercises were performed. Eighty-three joints were scored 0-3 separately for synovitis and osteitis, thirty-three entheses 0-3 separately for soft tissue inflammation and osteitis. Results: In the last exercise, reliability was moderate-good for musculoskeletal radiologists and rheumatologists with previously demonstrated good scoring proficiency. Median pairwise single-measure/average-measure ICCs were 0.67/0.80 for status scores and 0.69/0.82 for change scores; kappas ranged 0.35-0.77. Conclusion: WBMRI scoring of peripheral arthritis and enthesitis is reliable which encourages further testing and refinement in clinical trials

Can a Dusty Warm Absorber Model Reproduce the Soft X-ray Spectra of MCG-6-30-15 and Mrk 766?

XMM-Newton RGS spectra of MCG-6-30-15 and Mrk 766 exhibit complex discrete structure, which was interpreted in a paper by Branduardi-Raymont et al. (2001) as evidence for the existence of relativistically broadened Lyman alpha emission from carbon, nitrogen, and oxygen, produced in the inner-most regions of an accretion disk around a Kerr black hole. This suggestion was subsequently criticized in a paper by Lee et al. (2001), who argued that for MCG-6-30-15, the Chandra HETG spectrum, which is partially overlapping the RGS in spectral coverage, is adequately fit by a dusty warm absorber model, with no relativistic line emission. We present a reanalysis of the original RGS data sets in terms of the Lee et al. (2001) model, and demonstrate that spectral models consisting of a smooth continuum with ionized and dust absorption alone cannot reproduce the RGS spectra of both objects. The original relativistic line model with warm absorption proposed by Branduardi-Raymont et al. (2001) provides a superior fit to the RGS data, both in the overall shape of the spectrum and in the discrete absorption lines. Limits on the amount of X-ray absorption by dust particles are discussed. We also discuss a possible theoretical interpretation for the putative relativistic Lyman alpha line emission in terms of the photoionized surface layers of the inner regions of an accretion disk.Comment: Replaced with accepted version. To appear in ApJ; tentatively scheduled for the v596 Oct. 10, 2003 issu

US Cosmic Visions: New Ideas in Dark Matter 2017: Community Report

This white paper summarizes the workshop "U.S. Cosmic Visions: New Ideas in Dark Matter" held at University of Maryland on March 23-25, 2017.Comment: 102 pages + reference

The Long-Baseline Neutrino Experiment: Exploring Fundamental Symmetries of the Universe

The preponderance of matter over antimatter in the early Universe, the dynamics of the supernova bursts that produced the heavy elements necessary for life and whether protons eventually decay --- these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our Universe, its current state and its eventual fate. The Long-Baseline Neutrino Experiment (LBNE) represents an extensively developed plan for a world-class experiment dedicated to addressing these questions. LBNE is conceived around three central components: (1) a new, high-intensity neutrino source generated from a megawatt-class proton accelerator at Fermi National Accelerator Laboratory, (2) a near neutrino detector just downstream of the source, and (3) a massive liquid argon time-projection chamber deployed as a far detector deep underground at the Sanford Underground Research Facility. This facility, located at the site of the former Homestake Mine in Lead, South Dakota, is approximately 1,300 km from the neutrino source at Fermilab -- a distance (baseline) that delivers optimal sensitivity to neutrino charge-parity symmetry violation and mass ordering effects. This ambitious yet cost-effective design incorporates scalability and flexibility and can accommodate a variety of upgrades and contributions. With its exceptional combination of experimental configuration, technical capabilities, and potential for transformative discoveries, LBNE promises to be a vital facility for the field of particle physics worldwide, providing physicists from around the globe with opportunities to collaborate in a twenty to thirty year program of exciting science. In this document we provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess.Comment: Major update of previous version. This is the reference document for LBNE science program and current status. Chapters 1, 3, and 9 provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess. 288 pages, 116 figure

Brain structural correlates of insomnia severity in 1053 individuals with major depressive disorder : results from the ENIGMA MDD Working Group

It has been difficult to find robust brain structural correlates of the overall severity of major depressive disorder (MDD). We hypothesized that specific symptoms may better reveal correlates and investigated this for the severity of insomnia, both a key symptom and a modifiable major risk factor of MDD. Cortical thickness, surface area and subcortical volumes were assessed from T1-weighted brain magnetic resonance imaging (MRI) scans of 1053 MDD patients (age range 13-79 years) from 15 cohorts within the ENIGMA MDD Working Group. Insomnia severity was measured by summing the insomnia items of the Hamilton Depression Rating Scale (HDRS). Symptom specificity was evaluated with correlates of overall depression severity. Disease specificity was evaluated in two independent samples comprising 2108 healthy controls, and in 260 clinical controls with bipolar disorder. Results showed that MDD patients with more severe insomnia had a smaller cortical surface area, mostly driven by the right insula, left inferior frontal gyrus pars triangularis, left frontal pole, right superior parietal cortex, right medial orbitofrontal cortex, and right supramarginal gyrus. Associations were specific for insomnia severity, and were not found for overall depression severity. Associations were also specific to MDD; healthy controls and clinical controls showed differential insomnia severity association profiles. The findings indicate that MDD patients with more severe insomnia show smaller surfaces in several frontoparietal cortical areas. While explained variance remains small, symptom-specific associations could bring us closer to clues on underlying biological phenomena of MDD

Surface indicators are correlated with soil multifunctionality in global drylands

Multiple ecosystem functions need to be considered simultaneously to manage and protect the several ecosystem services that are essential to people and their environments. Despite this, cost effective, tangible, relatively simple and globally relevant methodologies to monitor in situ soil multifunctionality, that is, the provision of multiple ecosystem functions by soils, have not been tested at the global scale. We combined correlation analysis and structural equation modelling to explore whether we could find easily measured, field-based indicators of soil multifunctionality (measured using functions linked to the cycling and storage of soil carbon, nitrogen and phosphorus). To do this, we gathered soil data from 120 dryland ecosystems from five continents. Two soil surface attributes measured in situ (litter incorporation and surface aggregate stability) were the most strongly associated with soil multifunctionality, even after accounting for geographic location and other drivers such as climate, woody cover, soil pH and soil electric conductivity. The positive relationships between surface stability and litter incorporation on soil multifunctionality were greater beneath the canopy of perennial vegetation than in adjacent, open areas devoid of vascular plants. The positive associations between surface aggregate stability and soil functions increased with increasing mean annual temperature. Synthesis and applications. Our findings demonstrate that a reduced suite of easily measured in situ soil surface attributes can be used as potential indicators of soil multifunctionality in drylands world-wide. These attributes, which relate to plant litter (origin, incorporation, cover), and surface stability, are relatively cheap and easy to assess with minimal training, allowing operators to sample many sites across widely varying climatic areas and soil types. The correlations of these variables are comparable to the influence of climate or soil, and would allow cost-effective monitoring of soil multifunctionality under changing land-use and environmental conditions. This would provide important information for evaluating the ecological impacts of land degradation, desertification and climate change in drylands world-wide.Fil: Eldridge, David J.. University of New South Wales; AustraliaFil: Delgado Baquerizo, Manuel. Universidad Rey Juan Carlos; EspañaFil: Quero, José L.. Universidad de Córdoba; EspañaFil: Ochoa, Victoria. Universidad Rey Juan Carlos; España. Universidad de Alicante; EspañaFil: Gozalo, Beatriz. Universidad Rey Juan Carlos; España. Universidad de Alicante; EspañaFil: García Palacios, Pablo. Universidad Rey Juan Carlos; EspañaFil: Escolar, Cristina. Universidad Rey Juan Carlos; EspañaFil: García Gómez, Miguel. Universidad Politécnica de Madrid; EspañaFil: Prina, Aníbal. Universidad Nacional de La Pampa; ArgentinaFil: Bowker, Mathew A.. Northern Arizona University; Estados UnidosFil: Bran, Donaldo Eduardo. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche; ArgentinaFil: Castro, Ignacio. Universidad Experimental Simón Rodríguez; VenezuelaFil: Cea, Alex. Universidad de La Serena; ChileFil: Derak, Mchich. No especifíca;Fil: Espinosa, Carlos I.. Universidad Técnica Particular de Loja; EcuadorFil: Florentino, Adriana. Universidad Central de Venezuela; VenezuelaFil: Gaitán, Juan José. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales. Instituto de Suelos; Argentina. Universidad Nacional de Luján. Departamento de Tecnología; ArgentinaFil: Gatica, Mario Gabriel. Universidad Nacional de San Juan. Facultad de Ciencias Exactas Físicas y Naturales. Departamento de Biología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Centro de Investigaciones de la Geosfera y Biosfera. Universidad Nacional de San Juan. Facultad de Ciencias Exactas Físicas y Naturales. Centro de Investigaciones de la Geosfera y Biosfera; ArgentinaFil: Gómez González, Susana. Universidad de Cádiz; EspañaFil: Ghiloufi, Wahida. Université de Sfax; TúnezFil: Gutierrez, Julio R.. Universidad de La Serena; ChileFil: Guzman, Elizabeth. Universidad Técnica Particular de Loja; EcuadorFil: Hernández, Rosa M.. Universidad Experimental Simón Rodríguez; VenezuelaFil: Hughes, Frederic M.. Universidade Estadual de Feira de Santana; BrasilFil: Muiño, Walter. Universidad Nacional de La Pampa; ArgentinaFil: Monerris, Jorge. No especifíca;Fil: Ospina, Abelardo. Universidad Central de Venezuela; VenezuelaFil: Ramírez, David A.. International Potato Centre; PerúFil: Ribas Fernandez, Yanina Antonia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Centro de Investigaciones de la Geosfera y Biosfera. Universidad Nacional de San Juan. Facultad de Ciencias Exactas Físicas y Naturales. Centro de Investigaciones de la Geosfera y Biosfera; ArgentinaFil: Romão, Roberto L.. Universidade Estadual de Feira de Santana; BrasilFil: Torres Díaz, Cristian. Universidad del Bio Bio; ChileFil: Koen, Terrance B.. No especifíca;Fil: Maestre, Fernando T.. Universidad Rey Juan Carlos; España. Universidad de Alicante; Españ