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Conference paperArcucci R, Casas CQ, Joshi A, et al., 2022,
Merging Real Images with Physics Simulations via Data Assimilation
, 27th International European Conference on Parallel and Distributed Computing (Euro-Par), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 255-266, ISSN: 0302-9743 -
Conference paperLim B, Allard M, Grillotti L, et al., 2022,
QDax: On the Benefits of Massive Parallelization for Quality-Diversity
, Genetic and Evolutionary Computation Conference (GECCO), Publisher: ASSOC COMPUTING MACHINERY, Pages: 128-131 -
Conference paperCursi F, Bai W, Kormushev P, 2021,
Kalibrot: a simple-to-use Matlab package for robot kinematic calibration
, Prague, Czech Republic, International Conference on Intelligent Robots and Systems (IROS) 2021, Pages: 8852-8859Robot modelling is an essential part to properlyunderstand how a robotic system moves and how to controlit. The kinematic model of a robot is usually obtained byusing Denavit-Hartenberg convention, which relies on a set ofparameters to describe the end-effector pose in a Cartesianspace. These parameters are assigned based on geometricalconsiderations of the robotic structure, however, the assignedvalues may be inaccurate. The purpose of robot kinematiccalibration is therefore to find optimal parameters whichimprove the accuracy of the robot model. In this work wepresent Kalibrot, an open source Matlab package for robotkinematic calibration. Kalibrot has been designed to simplifyrobot calibration and easily assess the calibration results. Besidecomputing the optimal parameters, Kalibrot provides a visualization layer showing the values of the calibrated parameters,what parameters can be identified, and the calibrated roboticstructure. The capabilities of the package are here shownthrough simulated and real world experiments.
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Conference paperCursi F, Kormushev P, 2021,
Pre-operative offline optimization of insertion point location for safe and accurate surgical task execution
, Prague, Czech Republic, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021), Publisher: IEEE, Pages: 4040-4047In robotically assisted surgical procedures thesurgical tool is usually inserted in the patient’s body througha small incision, which acts as a constraint for the motionof the robot, known as remote center of Motion (RCM). Thelocation of the insertion point on the patient’s body has hugeeffects on the performances of the surgical robot. In this workwe present an offline pre-operative framework to identify theoptimal insertion point location in order to guarantee accurateand safe surgical task execution. The approach is validatedusing a serial-link manipulator in conjunction with a surgicalrobotic tool to perform a tumor resection task, while avoidingnearby organs. Results show that the framework is capable ofidentifying the best insertion point ensuring high dexterity, hightracking accuracy, and safety in avoiding nearby organs.
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Conference paperLa Barbera V, Pardo F, Tassa Y, et al., 2021,
OstrichRL: a musculoskeletal ostrich simulation to study bio-mechanical locomotion
, NeurIPS 2021Muscle-actuated control is a research topic of interest spanning different fields, inparticular biomechanics, robotics and graphics. This type of control is particularlychallenging because models are often overactuated, and dynamics are delayed andnon-linear. It is however a very well tested and tuned actuation model that hasundergone millions of years of evolution and that involves interesting propertiesexploiting passive forces of muscle-tendon units and efficient energy storage andrelease. To facilitate research on muscle-actuated simulation, we release a 3Dmusculoskeletal simulation of an ostrich based on the MuJoCo simulator. Ostrichesare one of the fastest bipeds on earth and are therefore an excellent model forstudying muscle-actuated bipedal locomotion. The model is based on CT scans anddissections used to gather actual muscle data such as insertion sites, lengths andpennation angles. Along with this model, we also provide a set of reinforcementlearning tasks, including reference motion tracking and a reaching task with theneck. The reference motion data are based on motion capture clips of variousbehaviors which we pre-processed and adapted to our model. This paper describeshow the model was built and iteratively improved using the tasks. We evaluate theaccuracy of the muscle actuation patterns by comparing them to experimentallycollected electromyographic data from locomoting birds. We believe that this workcan be a useful bridge between the biomechanics, reinforcement learning, graphicsand robotics communities, by providing a fast and easy to use simulation.
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Journal articleLiu Z, Peach R, Lawrance E, et al., 2021,
Listening to mental health crisis needs at scale: using Natural Language Processing to understand and evaluate a mental health crisis text messaging service
, Frontiers in Digital Health, Vol: 3, Pages: 1-14, ISSN: 2673-253XThe current mental health crisis is a growing public health issue requiring a large-scale response that cannot be met with traditional services alone. Digital support tools are proliferating, yet most are not systematically evaluated, and we know little about their users and their needs. Shout is a free mental health text messaging service run by the charity Mental Health Innovations, which provides support for individuals in the UK experiencing mental or emotional distress and seeking help. Here we study a large data set of anonymised text message conversations and post-conversation surveys compiled through Shout. This data provides an opportunity to hear at scale from those experiencing distress; to better understand mental health needs for people not using traditional mental health services; and to evaluate the impact of a novel form of crisis support. We use natural language processing (NLP) to assess the adherence of volunteers to conversation techniques and formats, and to gain insight into demographic user groups and their behavioural expressions of distress. Our textual analyses achieve accurate classification of conversation stages (weighted accuracy = 88%), behaviours (1-hamming loss = 95%) and texter demographics (weighted accuracy = 96%), exemplifying how the application of NLP to frontline mental health data sets can aid with post-hoc analysis and evaluation of quality of service provision in digital mental health services.
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Conference paperSukpanichnant P, Rago A, Lertvittayakumjorn P, et al., 2021,
LRP-based argumentative explanations for neural networks
, XAI.it 2021 - Italian Workshop on Explainable Artificial Intelligence, Pages: 71-84, ISSN: 1613-0073In recent years, there have been many attempts to combine XAI with the field of symbolic AI in order to generate explanations for neural networks that are more interpretable and better align with human reasoning, with one prominent candidate for this synergy being the sub-field of computational argumentation. One method is to represent neural networks with quantitative bipolar argumentation frameworks (QBAFs) equipped with a particular semantics. The resulting QBAF can then be viewed as an explanation for the associated neural network. In this paper, we explore a novel LRP-based semantics under a new QBAF variant, namely neural QBAFs (nQBAFs). Since an nQBAF of a neural network is typically large, the nQBAF must be simplified before being used as an explanation. Our empirical evaluation indicates that the manner of this simplification is all important for the quality of the resulting explanation.
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Conference paperLiu Z, Barahona M, 2021,
Similarity measure for sparse time course data based on Gaussian processes
, Uncertainty in Artificial Intelligence 2021, Publisher: PMLR, Pages: 1332-1341We propose a similarity measure for sparsely sampled time course data in the form of a log-likelihood ratio of Gaussian processes (GP). The proposed GP similarity is similar to a Bayes factor and provides enhanced robustness to noise in sparse time series, such as those found in various biological settings, e.g., gene transcriptomics. We show that the GP measure is equivalent to the Euclidean distance when the noise variance in the GP is negligible compared to the noise variance of the signal. Our numerical experiments on both synthetic and real data show improved performance of the GP similarity when used in conjunction with two distance-based clustering methods.
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Conference paperKouvaros P, Kyono T, Leofante F, et al., 2021,
Formal analysis of neural network-based systems in the aircraft domain
, International Symposium on Formal Methods, Publisher: Springer International Publishing, Pages: 730-740, ISSN: 0302-9743Neural networks are being increasingly used for efficient decision making in the aircraft domain. Given the safety-critical nature of the applications involved, stringent safety requirements must be met by these networks. In this work we present a formal study of two neural network-based systems developed by Boeing. The Venus verifier is used to analyse the conditions under which these systems can operate safely, or generate counterexamples that show when safety cannot be guaranteed. Our results confirm the applicability of formal verification to the settings considered.
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Conference paperKotonya N, Spooner T, Magazzeni D, et al., 2021,
Graph reasoning with context-aware linearization for interpretable fact extraction and verification
, FEVER 2021, Publisher: Association for Computational Linguistics, Pages: 21-30This paper presents an end-to-end system for fact extraction and verification using textual and tabular evidence, the performance of which we demonstrate on the FEVEROUS dataset. We experiment with both a multi-task learning paradigm to jointly train a graph attention network for both the task of evidence extraction and veracity prediction, as well as a single objective graph model for solely learning veracity prediction and separate evidence extraction. In both instances, we employ a framework for per-cell linearization of tabular evidence, thus allowing us to treat evidence from tables as sequences. The templates we employ for linearizing tables capture the context as well as the content of table data. We furthermore provide a case study to show the interpretability our approach. Our best performing system achieves a FEVEROUS score of 0.23 and 53% label accuracy on the blind test data.
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Journal articleHay AD, Moore M, Taylor J, et al., 2021,
Immediate oral versus immediate topical versus delayed oral antibiotics for children with acute otitis media with discharge: the REST three-arm non-inferiority electronic platform-supported RCT Introduction
, HEALTH TECHNOLOGY ASSESSMENT, Vol: 25, Pages: 1-+, ISSN: 1366-5278 -
Conference paperAlbini E, Rago A, Baroni P, et al., 2021,
Influence-driven explanations for bayesian network classifiers
, PRICAI 2021, Publisher: Springer Verlag, Pages: 88-100, ISSN: 0302-9743We propose a novel approach to buildinginfluence-driven ex-planations(IDXs) for (discrete) Bayesian network classifiers (BCs). IDXsfeature two main advantages wrt other commonly adopted explanationmethods. First, IDXs may be generated using the (causal) influences between intermediate, in addition to merely input and output, variables within BCs, thus providing adeep, rather than shallow, account of theBCs’ behaviour. Second, IDXs are generated according to a configurable set of properties, specifying which influences between variables count to-wards explanations. Our approach is thusflexible and can be tailored to the requirements of particular contexts or users. Leveraging on this flexibility, we propose novel IDX instances as well as IDX instances cap-turing existing approaches. We demonstrate IDXs’ capability to explainvarious forms of BCs, and assess the advantages of our proposed IDX instances with both theoretical and empirical analyses.
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Journal articleFiorentino F, Prociuk D, Espinosa Gonzalez AB, et al., 2021,
An early warning risk prediction tool (RECAP-V1) for patients diagnosed with COVID-19: the protocol for a statistical analysis plan
, JMIR Research Protocols, Vol: 10, ISSN: 1929-0748Background:Since the start of the Covid-19 pandemic efforts have been made to develop early warning risk scores to help clinicians decide which patient is likely to deteriorate and require hospitalisation. The RECAP (Remote COVID Assessment in Primary Care) study investigates the predictive risk of hospitalisation, deterioration, and death of patients with confirmed COVID-19, based on a set of parameters chosen through a Delphi process done by clinicians. The study aims to use rich data collected remotely through the use of electronic data templates integrated in the electronic health systems of a number of general practices across the UK to construct accurate predictive models that will use pre-existing conditions and monitoring data of a patient’s clinical parameters such as blood oxygen saturation to make reliable predictions as to the patient’s risk of hospital admission, deterioration, and death.Objective:We outline the statistical methods to build the prediction model to be used in the prioritisation of patients in the primary care setting. The statistical analysis plan for the RECAP study includes as primary outcome the development and validation of the RECAP-V1 prediction model. Such prediction model will be adapted as a three-category risk score split into red (high risk), amber (medium risk), and green (low risk) for any patient with suspected covid-19. The model will predict risk of deterioration, hospitalisation, and death.Methods:After the data has been collected, we will assess the degree of missingness and use a combination of traditional data imputation using multiple imputation by chained equations, as well as more novel machine learning approaches to impute the missing data for the final analysis. For predictive model development we will use multiple logistic regressions to construct the model on a training dataset, as well as validating the model on an independent dataset. The model will also be applied for multiple different datasets
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Journal articleFiorentino F, Prociuk D, Espinosa Gonzalez AB, et al., 2021,
An Early Warning Risk Prediction Tool (RECAP-V1) for Patients Diagnosed With COVID-19: Protocol for a Statistical Analysis Plan
, JMIR Research Protocols, Vol: 10, Pages: e30083-e30083<jats:sec> <jats:title>Background</jats:title> <jats:p>Since the start of the COVID-19 pandemic, efforts have been made to develop early warning risk scores to help clinicians decide which patient is likely to deteriorate and require hospitalization. The RECAP (Remote COVID-19 Assessment in Primary Care) study investigates the predictive risk of hospitalization, deterioration, and death of patients with confirmed COVID-19, based on a set of parameters chosen through a Delphi process performed by clinicians. We aim to use rich data collected remotely through the use of electronic data templates integrated in the electronic health systems of several general practices across the United Kingdom to construct accurate predictive models. The models will be based on preexisting conditions and monitoring data of a patient’s clinical parameters (eg, blood oxygen saturation) to make reliable predictions as to the patient’s risk of hospital admission, deterioration, and death.</jats:p> </jats:sec> <jats:sec> <jats:title>Objective</jats:title> <jats:p>This statistical analysis plan outlines the statistical methods to build the prediction model to be used in the prioritization of patients in the primary care setting. The statistical analysis plan for the RECAP study includes the development and validation of the RECAP-V1 prediction model as a primary outcome. This prediction model will be adapted as a three-category risk score split into red (high risk), amber (medium risk), and green (low risk) for any patient with suspected COVID-19. The model will predict the risk of deterioration and hospitalization.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods</jats:title> <jats:p>After the data have been collected, we will assess the degree of missingness and use a combination
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Journal articleNurek M, Rayner C, Freyer A, et al., 2021,
Recommendations for the recognition, diagnosis, and management of long COVID: a Delphi study
, British Journal of General Practice, Vol: 71, Pages: E815-E825, ISSN: 0960-1643Background In the absence of research into therapies and care pathways for long COVID, guidance based on ‘emerging experience’ is needed.Aim To provide a rapid expert guide for GPs and long COVID clinical services.Design and setting A Delphi study was conducted with a panel of primary and secondary care doctors.Method Recommendations were generated relating to the investigation and management of long COVID. These were distributed online to a panel of UK doctors (any specialty) with an interest in, lived experience of, and/or experience treating long COVID. Over two rounds of Delphi testing, panellists indicated their agreement with each recommendation (using a five-point Likert scale) and provided comments. Recommendations eliciting a response of ‘strongly agree’, ‘agree’, or ‘neither agree nor disagree’ from 90% or more of responders were taken as showing consensus.Results Thirty-three clinicians representing 14 specialties reached consensus on 35 recommendations. Chiefly, GPs should consider long COVID in the presence of a wide range of presenting features (not limited to fatigue and breathlessness) and exclude differential diagnoses where appropriate. Detailed history and examination with baseline investigations should be conducted in primary care. Indications for further investigation and specific therapies (for myocarditis, postural tachycardia syndrome, mast cell disorder) include hypoxia/desaturation, chest pain, palpitations, and histamine-related symptoms. Rehabilitation should be individualised, with careful activity pacing (to avoid relapse) and multidisciplinary support.Conclusion Long COVID clinics should operate as part of an integrated care system, with GPs playing a key role in the multidisciplinary team. Holistic care pathways, investigation of specific complications, management of potential symptom clusters, and tailored rehabilitation are needed.
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Conference paperRago A, Cocarascu O, Bechlivanidis C, et al., 2020,
Argumentation as a framework for interactive explanations for recommendations
, KR 2020, 17th International Conference on Principles of Knowledge Representation and Reasoning, Publisher: IJCAI, Pages: 805-815, ISSN: 2334-1033As AI systems become ever more intertwined in our personallives, the way in which they explain themselves to and inter-act with humans is an increasingly critical research area. Theexplanation of recommendations is, thus a pivotal function-ality in a user’s experience of a recommender system (RS),providing the possibility of enhancing many of its desirablefeatures in addition to itseffectiveness(accuracy wrt users’preferences). For an RS that we prove empirically is effective,we show how argumentative abstractions underpinning rec-ommendations can provide the structural scaffolding for (dif-ferent types of) interactive explanations (IEs), i.e. explana-tions empowering interactions with users. We prove formallythat these IEs empower feedback mechanisms that guaranteethat recommendations will improve with time, hence render-ing the RSscrutable. Finally, we prove experimentally thatthe various forms of IE (tabular, textual and conversational)inducetrustin the recommendations and provide a high de-gree oftransparencyin the RS’s functionality.
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Journal articleSimoes Monteiro de Marvao A, McGurk K, Zheng S, et al., 2021,
Phenotypic expression and outcomes in individuals with rare genetic variants of hypertrophic cardiomyopathy
, Journal of the American College of Cardiology, Vol: 78, Pages: 1097-1110, ISSN: 0735-1097Background: Hypertrophic cardiomyopathy (HCM) is caused by rare variants in sarcomereencoding genes, but little is known about the clinical significance of these variants in thegeneral population.Objectives: To compare lifetime outcomes and cardiovascular phenotypes according to thepresence of rare variants in sarcomere-encoding genes amongst middle-aged adults.Methods: We analysed whole exome sequencing and cardiac magnetic resonance (CMR)imaging in UK Biobank participants stratified by sarcomere-encoding variant status.Results: The prevalence of rare variants (allele frequency <0.00004) in HCM-associatedsarcomere-encoding genes in 200,584 participants was 2.9% (n=5,712; 1 in 35), and theprevalence of variants pathogenic or likely pathogenic for HCM (SARC-HCM-P/LP) was0.25% (n=493, 1 in 407). SARC-HCM-P/LP variants were associated with increased risk ofdeath or major adverse cardiac events compared to controls (HR 1.69, 95% CI 1.38 to 2.07,p<0.001), mainly due to heart failure endpoints (HR 4.23, 95% CI 3.07 to 5.83, p<0.001). In21,322 participants with CMR, SARC-HCM-P/LP were associated with asymmetric increasein left ventricular maximum wall thickness (10.9±2.7 vs 9.4±1.6 mm, p<0.001) buthypertrophy (≥13mm) was only present in 18.4% (n=9/49, 95% CI 9 to 32%). SARC-HCMP/LP were still associated with heart failure after adjustment for wall thickness (HR 6.74,95% CI 2.43 to 18.7, p<0.001).Conclusions: In this population of middle-aged adults, SARC-HCM-P/LP variants have lowaggregate penetrance for overt HCM but are associated with increased risk of adversecardiovascular outcomes and an attenuated cardiomyopathic phenotype. Although absoluteevent rates are low, identification of these variants may enhance risk stratification beyondfamilial disease.
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Book chapterCocarascu O, Cyras K, Rago A, et al., 2021,
Mining property-driven graphical explanations for data-centric AI from argumentation frameworks
, Human-Like Machine Intelligence, Pages: 93-113- Cite
- Citations: 1
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Conference paperCyras K, Rago A, Emanuele A, et al., 2021,
Argumentative XAI: a survey
, The 30th International Joint Conference on Artificial Intelligence (IJCAI-21), Publisher: International Joint Conferences on Artificial Intelligence, Pages: 4392-4399Explainable AI (XAI) has been investigated for decades and, together with AI itself, has witnessed unprecedented growth in recent years. Among various approaches to XAI, argumentative models have been advocated in both the AI and social science literature, as their dialectical nature appears to match some basic desirable features of the explanation activity. In this survey we overview XAI approaches built using methods from the field of computational argumentation, leveraging its wide array of reasoning abstractions and explanation delivery methods. We overview the literature focusing on different types of explanation (intrinsic and post-hoc), different models with which argumentation-based explanations are deployed, different forms of delivery, and different argumentation frameworks they use. We also lay out a roadmap for future work.
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Conference paperZylberajch H, Lertvittayakumjorn P, Toni F, 2021,
HILDIF: interactive debugging of NLI models using influence functions
, 1st Workshop on Interactive Learning for Natural Language Processing (InterNLP), Publisher: ASSOC COMPUTATIONAL LINGUISTICS-ACL, Pages: 1-6Biases and artifacts in training data can cause unwelcome behavior in text classifiers (such as shallow pattern matching), leading to lack of generalizability. One solution to this problem is to include users in the loop and leverage their feedback to improve models. We propose a novel explanatory debugging pipeline called HILDIF, enabling humans to improve deep text classifiers using influence functions as an explanation method. We experiment on the Natural Language Inference (NLI) task, showing that HILDIF can effectively alleviate artifact problems in fine-tuned BERT models and result in increased model generalizability.
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