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Journal articleZheng JX, Pawar S, Goodman DFM, 2021,
Further towards unambiguous edge bundling: Investigating power-confluentdrawings for network visualization
, IEEE Transactions on Visualization and Computer Graphics, Vol: 27, Pages: 2244-2249, ISSN: 1077-2626Bach et al. [1] recently presented an algorithm for constructing confluentdrawings, by leveraging power graph decomposition to generate an auxiliaryrouting graph. We identify two problems with their method and offer a singlesolution to solve both. We also classify the exact type of confluent drawingsthat the algorithm can produce as 'power-confluent', and prove that it is asubclass of the previously studied 'strict confluent' drawing. A descriptionand source code of our implementation is also provided, which additionallyincludes an improved method for power graph construction.
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Journal articleSaputra RP, Rakicevic N, Chappell D, et al., 2021,
Hierarchical decomposed-objective model predictive control for autonomous casualty extraction
, IEEE Access, Vol: 9, Pages: 39656-39679, ISSN: 2169-3536In recent years, several robots have been developed and deployed to perform casualty extraction tasks. However, the majority of these robots are overly complex, and require teleoperation via either a skilled operator or a specialised device, and often the operator must be present at the scene to navigate safely around the casualty. Instead, improving the autonomy of such robots can reduce the reliance on expert operators and potentially unstable communication systems, while still extracting the casualty in a safe manner. There are several stages in the casualty extraction procedure, from navigating to the location of the emergency, safely approaching and loading the casualty, to finally navigating back to the medical assistance location. In this paper, we propose a Hierarchical Decomposed-Objective based Model Predictive Control (HiDO-MPC) method for safely approaching and manoeuvring around the casualty. We implement this controller on ResQbot — a proof-of-concept mobile rescue robot we previously developed — capable of safely rescuing an injured person lying on the ground, i.e. performing the casualty extraction procedure. HiDO-MPC achieves the desired casualty extraction behaviour by decomposing the main objective into multiple sub-objectives with a hierarchical structure. At every time step, the controller evaluates this hierarchical decomposed objective and generates the optimal control decision. We have conducted a number of experiments both in simulation and using the real robot to evaluate the proposed method’s performance, and compare it with baseline approaches. The results demonstrate that the proposed control strategy gives significantly better results than baseline approaches in terms of accuracy, robustness, and execution time, when applied to casualty extraction scenarios.
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Journal articleD'Amore L, Murano A, Sorrentino L, et al., 2021,
Toward a multilevel scalable parallel Zielonka's algorithm for solving parity games
, CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, Vol: 33, ISSN: 1532-0626- Author Web Link
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- Citations: 2
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Journal articleKumar P, Kalaiarasan G, Porter AE, et al., 2021,
An overview of methods of fine and ultrafine particle collection for physicochemical characterisation and toxicity assessments.
, Science of the Total Environment, Vol: 756, Pages: 1-22, ISSN: 0048-9697Particulate matter (PM) is a crucial health risk factor for respiratory and cardiovascular diseases. The smaller size fractions, ≤2.5 μm (PM2.5; fine particles) and ≤0.1 μm (PM0.1; ultrafine particles), show the highest bioactivity but acquiring sufficient mass for in vitro and in vivo toxicological studies is challenging. We review the suitability of available instrumentation to collect the PM mass required for these assessments. Five different microenvironments representing the diverse exposure conditions in urban environments are considered in order to establish the typical PM concentrations present. The highest concentrations of PM2.5 and PM0.1 were found near traffic (i.e. roadsides and traffic intersections), followed by indoor environments, parks and behind roadside vegetation. We identify key factors to consider when selecting sampling instrumentation. These include PM concentration on-site (low concentrations increase sampling time), nature of sampling sites (e.g. indoors; noise and space will be an issue), equipment handling and power supply. Physicochemical characterisation requires micro- to milli-gram quantities of PM and it may increase according to the processing methods (e.g. digestion or sonication). Toxicological assessments of PM involve numerous mechanisms (e.g. inflammatory processes and oxidative stress) requiring significant amounts of PM to obtain accurate results. Optimising air sampling techniques are therefore important for the appropriate collection medium/filter which have innate physical properties and the potential to interact with samples. An evaluation of methods and instrumentation used for airborne virus collection concludes that samplers operating cyclone sampling techniques (using centrifugal forces) are effective in collecting airborne viruses. We highlight that predictive modelling can help to identify pollution hotspots in an urban environment for the efficient collection of PM mass. This review provides
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Conference paperCursi F, Modugno V, Kormushev P, 2021,
Model predictive control for a tendon-driven surgical robot with safety constraints in kinematics and dynamics
, Las Vegas, USA, International Conference on Intelligence Robots and Systems (IROS), Pages: 7653-7660In fields such as minimally invasive surgery, effective control strategies are needed to guarantee safety andaccuracy of the surgical task. Mechanical designs and actuationschemes have inevitable limitations such as backlash and jointlimits. Moreover, surgical robots need to operate in narrowpathways, which may give rise to additional environmentalconstraints. Therefore, the control strategies must be capableof satisfying the desired motion trajectories and the imposedconstraints. Model Predictive Control (MPC) has proven effective for this purpose, allowing to solve an optimal problem bytaking into consideration the evolution of the system states, costfunction, and constraints over time. The high nonlinearities intendon-driven systems, adopted in many surgical robots, are difficult to be modelled analytically. In this work, we use a modellearning approach for the dynamics of tendon-driven robots.The dynamic model is then employed to impose constraintson the torques of the robot under consideration and solve anoptimal constrained control problem for trajectory trackingby using MPC. To assess the capabilities of the proposedframework, both simulated and real world experiments havebeen conducted
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Conference paperJohns E, Garcia-Hernando G, Kim T-K, 2020,
Physics-based dexterous manipulations with estimated hand poses and residual reinforcement learning
, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, Publisher: IEEE, Pages: 9561-9568Dexterous manipulation of objects in virtual environments with our bare hands, by using only a depth sensor and a state-of-the-art 3D hand pose estimator (HPE), is challenging. While virtual environments are ruled by physics, e.g. object weights and surface frictions, the absence of force feedback makes the task challenging, as even slight inaccuracies on finger tips or contact points from HPE may make the interactions fail. Prior arts simply generate contact forces in the direction of the fingers' closures, when finger joints penetrate virtual objects. Although useful for simple grasping scenarios, they cannot be applied to dexterous manipulations such as inhand manipulation. Existing reinforcement learning (RL) and imitation learning (IL) approaches train agents that learn skills by using task-specific rewards, without considering any online user input. In this work, we propose to learn a model that maps noisy input hand poses to target virtual poses, which introduces the needed contacts to accomplish the tasks on a physics simulator. The agent is trained in a residual setting by using a model-free hybrid RL+IL approach. A 3D hand pose estimation reward is introduced leading to an improvement on HPE accuracy when the physics-guided corrected target poses are remapped to the input space. As the model corrects HPE errors by applying minor but crucial joint displacements for contacts, this helps to keep the generated motion visually close to the user input. Since HPE sequences performing successful virtual interactions do not exist, a data generation scheme to train and evaluate the system is proposed. We test our framework in two applications that use hand pose estimates for dexterous manipulations: hand-object interactions in VR and hand-object motion reconstruction in-the-wild. Experiments show that the proposed method outperforms various RL/IL baselines and the simple prior art of enforcing hand closure, both in task success and hand pose accuracy.
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Conference paperValassakis P, Ding Z, Johns E, 2021,
Crossing the gap: a deep dive into zero-shot sim-to-real transfer for dynamics
, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, Publisher: IEEEZero-shot sim-to-real transfer of tasks with complex dynamics is a highly challenging and unsolved problem. A number of solutions have been proposed in recent years, but we have found that many works do not present a thorough evaluation in the real world, or underplay the significant engineering effort and task-specific fine tuning that is required to achieve the published results. In this paper, we dive deeper into the sim-to-real transfer challenge, investigate why this issuch a difficult problem, and present objective evaluations of anumber of transfer methods across a range of real-world tasks.Surprisingly, we found that a method which simply injects random forces into the simulation performs just as well as more complex methods, such as those which randomise the simulator's dynamics parameters
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Journal articleKuntz J, Thomas P, Stan G-B, et al., 2021,
Stationary distributions of continuous-time Markov chains: a review of theory and truncation-based approximations
, SIAM Review, ISSN: 0036-1445Computing the stationary distributions of a continuous-time Markov chaininvolves solving a set of linear equations. In most cases of interest, thenumber of equations is infinite or too large, and cannot be solved analyticallyor numerically. Several approximation schemes overcome this issue by truncatingthe state space to a manageable size. In this review, we first give acomprehensive theoretical account of the stationary distributions and theirrelation to the long-term behaviour of the Markov chain, which is readilyaccessible to non-experts and free of irreducibility assumptions made instandard texts. We then review truncation-based approximation schemes payingparticular attention to their convergence and to the errors they introduce, andwe illustrate their performance with an example of a stochastic reactionnetwork of relevance in biology and chemistry. We conclude by elaborating oncomputational trade-offs associated with error control and some open questions.
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Journal articleQuilodrán-Casas C, Silva VS, Arcucci R, et al., 2021,
Digital twins based on bidirectional LSTM and GAN for modelling COVID-19
The outbreak of the coronavirus disease 2019 (COVID-19) has now spreadthroughout the globe infecting over 100 million people and causing the death ofover 2.2 million people. Thus, there is an urgent need to study the dynamics ofepidemiological models to gain a better understanding of how such diseasesspread. While epidemiological models can be computationally expensive, recentadvances in machine learning techniques have given rise to neural networks withthe ability to learn and predict complex dynamics at reduced computationalcosts. Here we introduce two digital twins of a SEIRS model applied to anidealised town. The SEIRS model has been modified to take account of spatialvariation and, where possible, the model parameters are based on official virusspreading data from the UK. We compare predictions from a data-correctedBidirectional Long Short-Term Memory network and a predictive GenerativeAdversarial Network. The predictions given by these two frameworks are accuratewhen compared to the original SEIRS model data. Additionally, these frameworksare data-agnostic and could be applied to towns, idealised or real, in the UKor in other countries. Also, more compartments could be included in the SEIRSmodel, in order to study more realistic epidemiological behaviour.
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Journal articleEspinosa-González AB, Delaney BC, Marti J, et al., 2021,
The role of the state in financing and regulating primary care in Europe: a taxonomy
, Health Policy, Vol: 125, Pages: 168-176, ISSN: 0168-8510Traditional health systems typologies were based on health system financing type, such as the well-known OECD typology. However, the number of dimensions captured in classifications increased to reflect health systems complexity. This study aims to develop a taxonomy of primary care (PC) systems based on the actors involved (state, societal and private) and mechanisms used in governance, financing and regulation, which conceptually represents the degree of decentralisation of functions. We use nonlinear canonical correlations analysis and agglomerative hierarchical clustering on data obtained from the European Observatory on Health Systems and Policy and informants from 24 WHO European Region countries. We obtain four clusters: 1) Bosnia Herzegovina, Czech Republic, Germany, Slovakia and Switzerland: corporatist and/or fragmented PC system, with state involvement in PC supply regulation, without gatekeeping; 2) Greece, Ireland, Israel, Malta, Sweden, and Ukraine: public and (re)centralised PC financing and regulation with private involvement, without gatekeeping; 3) Finland, Norway, Spain and United Kingdom: public financing and devolved regulation and organisation of PC, with gatekeeping; and 4) Bulgaria, Croatia, France, North Macedonia, Poland, Romania, Serbia, Slovenia and Turkey: public and deconcentrated with professional involvement in supply regulation, and gatekeeping. This taxonomy can serve as a framework for performance comparisons and a means to analyse the effect that different actors and levels of devolution or fragmentation of PC delivery may have in health outcomes.
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Conference paperAmendola M, Arcucci R, Mottet L, et al., 2021,
Data Assimilation in the Latent Space of a Convolutional Autoencoder
, Pages: 373-386, ISSN: 0302-9743Data Assimilation (DA) is a Bayesian inference that combines the state of a dynamical system with real data collected by instruments at a given time. The goal of DA is to improve the accuracy of the dynamic system making its result as real as possible. One of the most popular technique for DA is the Kalman Filter (KF). When the dynamic system refers to a real world application, the representation of the state of a physical system usually leads to a big data problem. For these problems, KF results computationally too expensive and mandates to use of reduced order modeling techniques. In this paper we proposed a new methodology we called Latent Assimilation (LA). It consists in performing the KF in the latent space obtained by an Autoencoder with non-linear encoder functions and non-linear decoder functions. In the latent space, the dynamic system is represented by a surrogate model built by a Recurrent Neural Network. In particular, an Long Short Term Memory (LSTM) network is used to train a function which emulates the dynamic system in the latent space. The data from the dynamic model and the real data coming from the instruments are both processed through the Autoencoder. We apply the methodology to a real test case and we show that the LA has a good performance both in accuracy and in efficiency.
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Conference paperAfzali J, Casas CQ, Arcucci R, 2021,
Latent GAN: Using a Latent Space-Based GAN for Rapid Forecasting of CFD Models
, Pages: 360-372, ISSN: 0302-9743The focus of this study is to simulate realistic fluid flow, through Machine Learning techniques that could be utilised in real-time forecasting of urban air pollution. We propose a novel Latent GAN architecture which looks at combining an AutoEncoder with a Generative Adversarial Network to predict fluid flow at the proceeding timestep of a given input, whilst keeping computational costs low. This architecture is applied to tracer flows and velocity fields around an urban city. We present a pair of AutoEncoders capable of dimensionality reduction of 3 orders of magnitude. Further, we present a pair of Generator models capable of performing real-time forecasting of tracer flows and velocity fields. We demonstrate that the models, as well as the latent spaces generated, learn and retain meaningful physical features of the domain. Despite the domain of this project being that of computational fluid dynamics, the Latent GAN architecture is designed to be generalisable such that it can be applied to other dynamical systems.
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Journal articleLertvittayakumjorn P, Toni F, 2021,
Explanation-based human debugging of nlp models: a survey
, Transactions of the Association for Computational Linguistics, Vol: 9, Pages: 1508-1528, ISSN: 2307-387XDebugging a machine learning model is hard since the bug usually involves the training data and the learning process. This becomes even harder for an opaque deep learning model if we have no clue about how the model actually works. In this survey, we review papers that exploit explanations to enable humans to give feedback and debug NLP models. We call this problem explanation-based human debugging (EBHD). In particular, we categorize and discuss existing work along three dimensions of EBHD (the bug context, the workflow, and the experimental setting), compile findings on how EBHD components affect the feedback providers, and highlight open problems that could be future research directions.
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Journal articleNurek M, Rayner C, Freyer A, et al., 2021,
Recommendations for the Recognition, Diagnosis, and Management of Patients with Post COVID-19 Condition ('Long COVID'): A Delphi Study
, SSRN Electronic Journal -
Journal articleArcucci R, Zhu J, Hu S, et al., 2021,
Deep Data Assimilation: Integrating Deep Learning with Data Assimilation
, APPLIED SCIENCES-BASEL, Vol: 11- Author Web Link
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- Citations: 34
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Conference paperPaulino-Passos G, Toni F, 2021,
Monotonicity and Noise-Tolerance in Case-Based Reasoning with Abstract Argumentation
, Pages: 508-518 -
Conference paperLauren S, Belardinelli F, Toni F, 2021,
Aggregating Bipolar Opinions
, 20th International Conference on Autonomous Agents and Multiagent Systems -
Conference paperRakicevic N, Cully A, Kormushev P, 2020,
Policy manifold search for improving diversity-based neuroevolution
, Publisher: arXivDiversity-based approaches have recently gained popularity as an alternativeparadigm to performance-based policy search. A popular approach from thisfamily, Quality-Diversity (QD), maintains a collection of high-performingpolicies separated in the diversity-metric space, defined based on policies'rollout behaviours. When policies are parameterised as neural networks, i.e.Neuroevolution, QD tends to not scale well with parameter space dimensionality.Our hypothesis is that there exists a low-dimensional manifold embedded in thepolicy parameter space, containing a high density of diverse and feasiblepolicies. We propose a novel approach to diversity-based policy search viaNeuroevolution, that leverages learned latent representations of the policyparameters which capture the local structure of the data. Our approachiteratively collects policies according to the QD framework, in order to (i)build a collection of diverse policies, (ii) use it to learn a latentrepresentation of the policy parameters, (iii) perform policy search in thelearned latent space. We use the Jacobian of the inverse transformation(i.e.reconstruction function) to guide the search in the latent space. Thisensures that the generated samples remain in the high-density regions of theoriginal space, after reconstruction. We evaluate our contributions on threecontinuous control tasks in simulated environments, and compare todiversity-based baselines. The findings suggest that our approach yields a moreefficient and robust policy search process.
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Conference paperKotonya N, Toni F, 2020,
Explainable Automated Fact-Checking: A Survey
, Barcelona. Spain, 28th International Conference on Computational Linguistics (COLING 2020), Publisher: International Committee on Computational Linguistics, Pages: 5430-5443A number of exciting advances have been made in automated fact-checkingthanks to increasingly larger datasets and more powerful systems, leading toimprovements in the complexity of claims which can be accurately fact-checked.However, despite these advances, there are still desirable functionalitiesmissing from the fact-checking pipeline. In this survey, we focus on theexplanation functionality -- that is fact-checking systems providing reasonsfor their predictions. We summarize existing methods for explaining thepredictions of fact-checking systems and we explore trends in this topic.Further, we consider what makes for good explanations in this specific domainthrough a comparative analysis of existing fact-checking explanations againstsome desirable properties. Finally, we propose further research directions forgenerating fact-checking explanations, and describe how these may lead toimprovements in the research area.v
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Journal articleMack J, Arcucci R, Molina-Solana M, et al., 2020,
Attention-based Convolutional Autoencoders for 3D-Variational Data Assimilation
, COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, Vol: 372, ISSN: 0045-7825
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