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  • JOURNAL ARTICLE
    Kuntz J, Thomas P, Stan G-B, Barahona Met al., 2019,

    The exit time finite state projection scheme: bounding exit distributions and occupation measures of continuous-time Markov chains

    , SIAM Journal on Scientific Computing, Vol: 41, Pages: A748-A769, ISSN: 1064-8275

    We introduce the exit time finite state projection (ETFSP) scheme, a truncation- based method that yields approximations to the exit distribution and occupation measure associated with the time of exit from a domain (i.e., the time of first passage to the complement of the domain) of time-homogeneous continuous-time Markov chains. We prove that: (i) the computed approximations bound the measures from below; (ii) the total variation distances between the approximations and the measures decrease monotonically as states are added to the truncation; and (iii) the scheme converges, in the sense that, as the truncation tends to the entire state space, the total variation distances tend to zero. Furthermore, we give a computable bound on the total variation distance between the exit distribution and its approximation, and we delineate the cases in which the bound is sharp. We also revisit the related finite state projection scheme and give a comprehensive account of its theoretical properties. We demonstrate the use of the ETFSP scheme by applying it to two biological examples: the computation of the first passage time associated with the expression of a gene, and the fixation times of competing species subject to demographic noise.

  • JOURNAL ARTICLE
    Zhong Q, Fan X, Luo X, Toni Fet al., 2019,

    An explainable multi-attribute decision model based on argumentation

    , EXPERT SYSTEMS WITH APPLICATIONS, Vol: 117, Pages: 42-61, ISSN: 0957-4174
  • JOURNAL ARTICLE
    Bello GA, Dawes TJW, Duan J, Biffi C, de Marvao A, Howard LSGE, Gibbs JSR, Wilkins MR, Cook SA, Rueckert D, O'Regan DPet al., 2019,

    Deep learning cardiac motion analysis for human survival prediction.

    , Nat Mach Intell, Vol: 1, Pages: 95-104, ISSN: 2522-5839

    Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p = .0012) for our model C=0.75 (95% CI: 0.70 - 0.79) than the human benchmark of C=0.59 (95% CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival.

  • JOURNAL ARTICLE
    Baroni P, Rago A, Toni F, 2019,

    From fine-grained properties to broad principles for gradual argumentation: A principled spectrum

    , INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, Vol: 105, Pages: 252-286, ISSN: 0888-613X
  • JOURNAL ARTICLE
    Kormushev P, Ugurlu B, Caldwell DG, Tsagarakis NGet al., 2019,

    Learning to exploit passive compliance for energy-efficient gait generation on a compliant humanoid

    , AUTONOMOUS ROBOTS, Vol: 43, Pages: 79-95, ISSN: 0929-5593
  • JOURNAL ARTICLE
    Clarke JM, Warren LR, Arora S, Barahona M, Darzi AWet al., 2018,

    Guiding interoperable electronic health records through patient-sharing networks

    , npj Digital Medicine, Vol: 1, ISSN: 2398-6352

    Effective sharing of clinical information between care providers is a critical component of a safe, efficient health system. National data-sharing systems may be costly, politically contentious and do not reflect local patterns of care delivery. This study examines hospital attendances in England from 2013 to 2015 to identify instances of patient sharing between hospitals. Of 19.6 million patients receiving care from 155 hospital care providers, 130 million presentations were identified. On 14.7 million occasions (12%), patients attended a different hospital to the one they attended on their previous interaction. A network of hospitals was constructed based on the frequency of patient sharing between hospitals which was partitioned using the Louvain algorithm into ten distinct data-sharing communities, improving the continuity of data sharing in such instances from 0 to 65–95%. Locally implemented data-sharing communities of hospitals may achieve effective accessibility of clinical information without a large-scale national interoperable information system.

  • JOURNAL ARTICLE
    Cocarascu O, Toni F, 2018,

    Combining Deep Learning and Argumentative Reasoning for the Analysis of Social Media Textual Content Using Small Data Sets

    , COMPUTATIONAL LINGUISTICS, Vol: 44, Pages: 833-858, ISSN: 0891-2017
  • CONFERENCE PAPER
    Wang K, Shah A, Kormushev P, 2018,

    SLIDER: A Bipedal Robot with Knee-less Legs and Vertical Hip Sliding Motion

    , 21st International Conference on Climbing and Walking Robots and Support Technologies for Mobile Machines (CLAWAR 2018)
  • CONFERENCE PAPER
    Čyras K, Letsios D, Misener R, Toni Fet al.,

    Argumentation for Explainable Scheduling (Full Paper with Proofs)

    Mathematical optimization offers highly-effective tools for finding solutionsfor problems with well-defined goals, notably scheduling. However, optimizationsolvers are often unexplainable black boxes whose solutions are inaccessible tousers and which users cannot interact with. We define a novel paradigm usingargumentation to empower the interaction between optimization solvers andusers, supported by tractable explanations which certify or refute solutions. Asolution can be from a solver or of interest to a user (in the context of'what-if' scenarios). Specifically, we define argumentative and naturallanguage explanations for why a schedule is (not) feasible, (not) efficient or(not) satisfying fixed user decisions, based on models of the fundamentalmakespan scheduling problem in terms of abstract argumentation frameworks(AFs). We define three types of AFs, whose stable extensions are in one-to-onecorrespondence with schedules that are feasible, efficient and satisfying fixeddecisions, respectively. We extract the argumentative explanations from theseAFs and the natural language explanations from the argumentative ones.

  • CONFERENCE PAPER
    Russo A, Law M, Broda K,

    Representing and learning grammars in answer set programming

    , AAAI-19: Thirty-third AAAI Conference on Artificial Intelligence, Publisher: Association for the Advancement of Artificial Intelligence

    In this paper we introduce an extension of context-free gram-mars calledanswer set grammars(ASGs). These grammarsallow annotations on production rules, written in the lan-guage of Answer Set Programming (ASP), which can expresscontext-sensitive constraints. We investigate the complexityof various classes of ASG with respect to two decision prob-lems: deciding whether a given string belongs to the languageof an ASG and deciding whether the language of an ASG isnon-empty. Specifically, we show that the complexity of thesedecision problems can be lowered by restricting the subset ofthe ASP language used in the annotations. To aid the applica-bility of these grammars to computational problems that re-quire context-sensitive parsers for partially known languages,we propose a learning task for inducing the annotations of anASG. We characterise the complexity of this task and presentan algorithm for solving it. An evaluation of a (prototype)implementation is also discussed

  • CONFERENCE PAPER
    Russo A, Law M, Broda K,

    AAAI 2019, Proceedings pf the 33rd AAAI Conference on Artificial Intelligence

    , AAAI-19: Thirty-Third AAAI Conference on Artificial intelligence
  • CONFERENCE PAPER
    Saputra RP, Kormushev P, 2018,

    Casualty Detection from 3D Point Cloud Data for Autonomous Ground Mobile Rescue Robots

    © 2018 IEEE. One of the most important features of mobile rescue robots is the ability to autonomously detect casualties, i.e. human bodies, which are usually lying on the ground. This paper proposes a novel method for autonomously detecting casualties lying on the ground using obtained 3D point-cloud data from an on-board sensor, such as an RGB-D camera or a 3D LIDAR, on a mobile rescue robot. In this method, the obtained 3D point-cloud data is projected onto the detected ground plane, i.e. floor, within the point cloud. Then, this projected point cloud is converted into a grid-map that is used afterwards as an input for the algorithm to detect human body shapes. The proposed method is evaluated by performing detections of a human dummy, placed in different random positions and orientations, using an on-board RGB-D camera on a mobile rescue robot called ResQbot. To evaluate the robustness of the casualty detection method to different camera angles, the orientation of the camera is set to different angles. The experimental results show that using the point-cloud data from the on-board RGB-D camera, the proposed method successfully detects the casualty in all tested body positions and orientations relative to the on-board camera, as well as in all tested camera angles.

  • CONFERENCE PAPER
    Wilson J, Hutter F, Deisenroth MP,

    Maximizing acquisition functions for Bayesian optimization

    , Advances in Neural Information Processing Systems (NIPS) 2018, Publisher: Massachusetts Institute of Technology Press, ISSN: 1049-5258

    Bayesian optimization is a sample-efficient approach to global optimization that relies on theoretically motivated value heuristics (acquisition functions) to guide its search process. Fully maximizing acquisition functions produces the Bayes' decision rule, but this ideal is difficult to achieve since these functions are frequently non-trivial to optimize. This statement is especially true when evaluating queries in parallel, where acquisition functions are routinely non-convex, high-dimensional, and intractable. We first show that acquisition functions estimated via Monte Carlo integration are consistently amenable to gradient-based optimization. Subsequently, we identify a common family of acquisition functions, including EI and UCB, whose characteristics not only facilitate but justify use of greedy approaches for their maximization.

  • JOURNAL ARTICLE
    Schulz C, Toni F, 2018,

    On the responsibility for undecisiveness in preferred and stable labellings in abstract argumentation

    , ARTIFICIAL INTELLIGENCE, Vol: 262, Pages: 301-335, ISSN: 0004-3702
  • CONFERENCE PAPER
    Sæmundsson S, Hofmann K, Deisenroth MP, 2018,

    Meta reinforcement learning with latent variable Gaussian processes

    , Uncertainty in Artificial Intelligence (UAI) 2018, Publisher: Association for Uncertainty in Artificial Intelligence (AUAI)

    Learning from small data sets is critical inmany practical applications where data col-lection is time consuming or expensive, e.g.,robotics, animal experiments or drug design.Meta learning is one way to increase the dataefficiency of learning algorithms by general-izing learned concepts from a set of trainingtasks to unseen, but related, tasks. Often, thisrelationship between tasks is hard coded or re-lies in some other way on human expertise.In this paper, we frame meta learning as a hi-erarchical latent variable model and infer therelationship between tasks automatically fromdata. We apply our framework in a model-based reinforcement learning setting and showthat our meta-learning model effectively gen-eralizes to novel tasks by identifying how newtasks relate to prior ones from minimal data.This results in up to a60%reduction in theaverage interaction time needed to solve taskscompared to strong baselines.

  • JOURNAL ARTICLE
    Zheng JX-S, Pawar S, Goodman DFM, 2018,

    Graph Drawing by Stochastic Gradient Descent.

    , IEEE Trans Vis Comput Graph

    A popular method of force-directed graph drawing is multidimensional scaling using graph-theoretic distances as input. We present an algorithm to minimize its energy function, known as stress, by using stochastic gradient descent (SGD) to move a single pair of vertices at a time. Our results show that SGD can reach lower stress levels faster and more consistently than majorization, without needing help from a good initialization. We then show how the unique properties of SGD make it easier to produce constrained layouts than previous approaches. We also show how SGD can be directly applied within the sparse stress approximation of Ortmann et al. [1], making the algorithm scalable up to large graphs.

  • CONFERENCE PAPER
    Cocarascu O, Cyras K, Toni F, 2018,

    Explanatory predictions with artificial neural networks and argumentation

    , Workshop on Explainable Artificial Intelligence (XAI)

    Data-centric AI has proven successful in severaldomains, but its outputs are often hard to explain.We present an architecture combining ArtificialNeural Networks (ANNs) for feature selection andan instance of Abstract Argumentation (AA) forreasoning to provide effective predictions, explain-able both dialectically and logically. In particular,we train an autoencoder to rank features in input ex-amples, and select highest-ranked features to gen-erate an AA framework that can be used for mak-ing and explaining predictions as well as mappedonto logical rules, which can equivalently be usedfor making predictions and for explaining.Weshow empirically that our method significantly out-performs ANNs and a decision-tree-based methodfrom which logical rules can also be extracted.

  • CONFERENCE PAPER
    Pardo F, Tavakoli A, Levdik V, Kormushev Pet al., 2018,

    Time limits in reinforcement learning

    , International Conference on Machine Learning, Pages: 4042-4051

    In reinforcement learning, it is common to let anagent interact for a fixed amount of time with itsenvironment before resetting it and repeating theprocess in a series of episodes. The task that theagent has to learn can either be to maximize itsperformance over (i) that fixed period, or (ii) anindefinite period where time limits are only usedduring training to diversify experience. In thispaper, we provide a formal account for how timelimits could effectively be handled in each of thetwo cases and explain why not doing so can causestate-aliasing and invalidation of experience re-play, leading to suboptimal policies and traininginstability. In case (i), we argue that the termi-nations due to time limits are in fact part of theenvironment, and thus a notion of the remainingtime should be included as part of the agent’s in-put to avoid violation of the Markov property. Incase (ii), the time limits are not part of the envi-ronment and are only used to facilitate learning.We argue that this insight should be incorporatedby bootstrapping from the value of the state atthe end of each partial episode. For both cases,we illustrate empirically the significance of ourconsiderations in improving the performance andstability of existing reinforcement learning algo-rithms, showing state-of-the-art results on severalcontrol tasks.

  • CONFERENCE PAPER
    Altuncu MT, Mayer E, Yaliraki SN, Barahona Met al., 2018,

    From Text to Topics in Healthcare Records: An Unsupervised Graph Partitioning Methodology

    , 2018 KDD Conference Proceedings - MLMH: Machine Learning for Medicine and Healthcare

    Electronic Healthcare Records contain large volumes of unstructured data,including extensive free text. Yet this source of detailed information oftenremains under-used because of a lack of methodologies to extract interpretablecontent in a timely manner. Here we apply network-theoretical tools to analysefree text in Hospital Patient Incident reports from the National HealthService, to find clusters of documents with similar content in an unsupervisedmanner at different levels of resolution. We combine deep neural networkparagraph vector text-embedding with multiscale Markov Stability communitydetection applied to a sparsified similarity graph of document vectors, andshowcase the approach on incident reports from Imperial College Healthcare NHSTrust, London. The multiscale community structure reveals different levels ofmeaning in the topics of the dataset, as shown by descriptive terms extractedfrom the clusters of records. We also compare a posteriori against hand-codedcategories assigned by healthcare personnel, and show that our approachoutperforms LDA-based models. Our content clusters exhibit good correspondencewith two levels of hand-coded categories, yet they also provide further medicaldetail in certain areas and reveal complementary descriptors of incidentsbeyond the external classification taxonomy.

  • JOURNAL ARTICLE
    Muggleton S, Dai W-Z, Sammut C, Tamaddoni-Nezhad A, Wen J, Zhou Z-Het al., 2018,

    Meta-Interpretive Learning from noisy images

    , MACHINE LEARNING, Vol: 107, Pages: 1097-1118, ISSN: 0885-6125

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