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Conference paperRago A, Toni F, 2017,
Quantitative Argumentation Debates with Votes for Opinion Polling
, PRIMA, Publisher: Springer, Pages: 369-385, ISSN: 0302-9743Opinion polls are used in a variety of settings to assess the opinions of a population, but they mostly conceal the reasoning behind these opinions. Argumentation, as understood in AI, can be used to evaluate opinions in dialectical exchanges, transparently articulating the reasoning behind the opinions. We give a method integrating argumentation within opinion polling to empower voters to add new statements that render their opinions in the polls individually rational while at the same time justifying them. We then show how these poll results can be amalgamated to give a collectively rational set of voters in an argumentation framework. Our method relies upon Quantitative Argumentation Debate for Voting (QuAD-V) frameworks, which extend QuAD frameworks (a form of bipolar argumentation frameworks in which arguments have an intrinsic strength) with votes expressing individuals’ opinions on arguments.
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Journal articleZheng JX, Pawar S, Goodman DFM, 2017,
Graph Drawing by Stochastic Gradient Descent
A popular method of force-directed graph drawing is multidimensional scalingusing graph-theoretic distances as input. We present an algorithm to minimizeits 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 SGDcan reach lower stress levels faster and more consistently than majorization,without needing help from a good initialization. We then show how the uniqueproperties of SGD make it easier to produce constrained layouts than previousapproaches. We also show how SGD can be directly applied within the sparsestress approximation of Ortmann et al. [1], making the algorithm scalable up tolarge graphs.
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Conference paperBao Z, Cyras K, Toni F, 2017,
ABAplus: Attack Reversal in Abstract and Structured Argumentation with Preferences
, PRIMA 2017: The 20th International Conference on Principles and Practice of Multi-Agent Systems, Publisher: Springer Verlag, ISSN: 0302-9743We present ABAplus, a system that implements reasoningwith the argumentation formalism ABA+. ABA+ is a structured argumentationformalism that extends Assumption-Based Argumentation(ABA) with preferences and accounts for preferences via attack reversal.ABA+ also admits as instance Preference-based Argumentation whichaccounts for preferences by reversing attacks in abstract argumentation(AA). ABAplus readily implements attack reversal in both AA and ABAstylestructured argumentation. ABAplus affords computation, visualisationand comparison of extensions under five argumentation semantics.It is available both as a stand-alone system and as a web application.
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Conference paperOlofsson S, Mehrian M, Geris L, et al., 2017,
Bayesian multi-objective optimisation of neotissue growth in a perfusion bioreactor set-up
, European Symposium on Computer Aided Process Engineering (ESCAPE 27), Publisher: ElsevierWe consider optimising bone neotissue growth in a 3D scaffold during dynamic perfusionbioreactor culture. The goal is to choose design variables by optimising two conflictingobjectives: (i) maximising neotissue growth and (ii) minimising operating cost. Our con-tribution is a novel extension of Bayesian multi-objective optimisation to the case of oneblack-box (neotissue growth) and one analytical (operating cost) objective function, thathelps determine, within a reasonable amount of time, what design variables best managethe trade-off between neotissue growth and operating cost. Our method is tested againstand outperforms the most common approach in literature, genetic algorithms, and showsits important real-world applicability to problems that combine black-box models witheasy-to-quantify objectives like cost.
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Journal articleHerrero P, Bondia J, Oliver N, et al., 2017,
A coordinated control strategy for insulin and glucagon delivery in type 1 diabetes
, Computer Methods in Biomechanics and Biomedical Engineering, Vol: 20, Pages: 1474-1482, ISSN: 1025-5842Type 1 diabetes is an autoimmune condition characterised by a pancreatic insulin secretion deficit, resulting in high blood glucose concentrations, which can lead to micro- and macrovascular complications. Type 1 diabetes also leads to impaired glucagon production by the pancreatic α-cells, which acts as a counter-regulatory hormone to insulin. A closed-loop system for automatic insulin and glucagon delivery, also referred to as an artificial pancreas, has the potential to reduce the self-management burden of type 1 diabetes and reduce the risk of hypo- and hyperglycemia. To date, bihormonal closed-loop systems for glucagon and insulin delivery have been based on two independent controllers. However, in physiology, the secretion of insulin and glucagon in the body is closely interconnected by paracrine and endocrine associations. In this work, we present a novel biologically-inspired glucose control strategy that accounts for such coordination. An in silico study using an FDA-accepted type 1 simulator was performed to evaluate the proposed coordinated control strategy compared to its non-coordinated counterpart, as well as an insulin-only version of the controller. The proposed coordinated strategy achieves a reduction of hyperglycemia without increasing hypoglycemia, when compared to its non-coordinated counterpart.
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Conference paperCocarascu O, Toni F, 2017,
Identifying attack and support argumentative relations using deep learning
, 2017 Conference on Empirical Methods in Natural Language Processing, Publisher: Association for Computational Linguistics, Pages: 1374-1379We propose a deep learning architecture tocapture argumentative relations ofattackandsupportfrom one piece of text to an-other, of the kind that naturally occur ina debate. The architecture uses two (uni-directional or bidirectional) Long Short-Term Memory networks and (trained ornon-trained) word embeddings, and al-lows to considerably improve upon exist-ing techniques that use syntactic featuresand supervised classifiers for the sameform of (relation-based) argument mining.
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Journal articleBiffi C, Simoes Monteiro de Marvao A, Attard M, et al., 2017,
Three-dimensional Cardiovascular Imaging-Genetics: A Mass Univariate Framework
, Bioinformatics, ISSN: 1367-4803Motivation: Left ventricular (LV) hypertrophy is a strong predictor of cardiovascular outcomes, but its genetic regulation remains largely unexplained. Conventional phenotyping relies on manual calculation of LV mass and wall thickness, but advanced cardiac image analysis presents an opportunity for highthroughput mapping of genotype-phenotype associations in three dimensions (3D).Results: High-resolution cardiac magnetic resonance images were automatically segmented in 1,124 healthy volunteers to create a 3D shape model of the heart. Mass univariate regression was used to plot a 3D effect-size map for the association between wall thickness and a set of predictors at each vertex in the mesh. The vertices where a significant effect exists were determined by applying threshold-free cluster enhancement to boost areas of signal with spatial contiguity. Experiments on simulated phenotypic signals and SNP replication show that this approach offers a substantial gain in statistical power for cardiac genotype-phenotype associations while providing good control of the false discovery rate. This framework models the effects of genetic variation throughout the heart and can be automatically applied to large population cohorts.Availability: The proposed approach has been coded in an R package freely available at https://doi.org/10.5281/zenodo.834610 together with the clinical data used in this work.
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Conference paperChamberlain BP, Cardoso A, Liu CHB, et al., 2017,
Customer lifetime value pediction using embeddings
, International Conference on Knowledge Discovery and Data Mining, Publisher: ACM, Pages: 1753-1762We describe the Customer LifeTime Value (CLTV) prediction system deployed at ASOS.com, a global online fashion retailer. CLTV prediction is an important problem in e-commerce where an accurate estimate of future value allows retailers to effectively allocate marketing spend, identify and nurture high value customers and mitigate exposure to losses. The system at ASOS provides daily estimates of the future value of every customer and is one of the cornerstones of the personalised shopping experience. The state of the art in this domain uses large numbers of handcrafted features and ensemble regressors to forecast value, predict churn and evaluate customer loyalty. Recently, domains including language, vision and speech have shown dramatic advances by replacing handcrafted features with features that are learned automatically from data. We detail the system deployed at ASOS and show that learning feature representations is a promising extension to the state of the art in CLTV modelling. We propose a novel way to generate embeddings of customers, which addresses the issue of the ever changing product catalogue and obtain a significant improvement over an exhaustive set of handcrafted features.
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Journal articleHerrero P, Bondia J, Adewuyi O, et al., 2017,
Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator - in silico evaluation under intra- day variability
, Computer Methods and Programs in Biomedicine, Vol: 146, Pages: 125-131, ISSN: 0169-2607Background and ObjectiveCurrent prototypes of closed-loop systems for glucose control in type 1 diabetes mellitus, also referred to as artificial pancreas systems, require a pre-meal insulin bolus to compensate for delays in subcutaneous insulin absorption in order to avoid initial post-prandial hyperglycemia. Computing such a meal bolus is a challenging task due to the high intra-subject variability of insulin requirements. Most closed-loop systems compute this pre-meal insulin dose by a standard bolus calculation, as is commonly found in insulin pumps. However, the performance of these calculators is limited due to a lack of adaptiveness in front of dynamic changes in insulin requirements. Despite some initial attempts to include adaptation within these calculators, challenges remain.MethodsIn this paper we present a new technique to automatically adapt the meal-priming bolus within an artificial pancreas. The technique consists of using a novel adaptive bolus calculator based on Case-Based Reasoning and Run-To-Run control, within a closed-loop controller. Coordination between the adaptive bolus calculator and the controller was required to achieve the desired performance. For testing purposes, the clinically validated Imperial College Artificial Pancreas controller was employed. The proposed system was evaluated against itself but without bolus adaptation. The UVa-Padova T1DM v3.2 system was used to carry out a three-month in silico study on 11 adult and 11 adolescent virtual subjects taking into account inter-and intra-subject variability of insulin requirements and uncertainty on carbohydrate intake.ResultsOverall, the closed-loop controller enhanced by an adaptive bolus calculator improves glycemic control when compared to its non-adaptive counterpart. In particular, the following statistically significant improvements were found (non-adaptive vs. adaptive). Adults: mean glucose 142.2 ± 9.4 vs. 131.8 ± 4.2 mg/dl; perce
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Journal articleEleftheriadis S, Rudovic O, Deisenroth MP, et al., 2017,
Gaussian process domain experts for modeling of facial affect
, IEEE Transactions on Image Processing, Vol: 26, Pages: 4697-4711, ISSN: 1941-0042Most of existing models for facial behavior analysis rely on generic classifiers, which fail to generalize well to previously unseen data. This is because of inherent differences in source (training) and target (test) data, mainly caused by variation in subjects’ facial morphology, camera views, and so on. All of these account for different contexts in which target and source data are recorded, and thus, may adversely affect the performance of the models learned solely from source data. In this paper, we exploit the notion of domain adaptation and propose a data efficient approach to adapt already learned classifiers to new unseen contexts. Specifically, we build upon the probabilistic framework of Gaussian processes (GPs), and introduce domain-specific GP experts (e.g., for each subject). The model adaptation is facilitated in a probabilistic fashion, by conditioning the target expert on the predictions from multiple source experts. We further exploit the predictive variance of each expert to define an optimal weighting during inference. We evaluate the proposed model on three publicly available data sets for multi-class (MultiPIE) and multi-label (DISFA, FERA2015) facial expression analysis by performing adaptation of two contextual factors: “where” (view) and “who” (subject). In our experiments, the proposed approach consistently outperforms: 1) both source and target classifiers, while using a small number of target examples during the adaptation and 2) related state-of-the-art approaches for supervised domain adaptation.
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Conference paperHerrero Vinas P, Pesl P, Reddy M, et al., 2017,
Atomatic adjustment of Basal insulin infusion rates in type 1 diabetes using run-to-run control and case-based reasoning
, Artificial Intelligence in MedicinePeople with type 1 diabetes mellitus rely on a basal-bolus insulinregimen to roughly emulate how a non-diabetic person’s body delivers insulin.Adjusting such regime is a challenging process usually conducted by an expertclinical. Despite several guidelines exist for such purpose, they are usuallyimpractical and fall short in achieving optimal glycemic outcomes. Therefore,there is a need for more automated and efficient strategies to adjust such regime.This paper presents, and in silico validates, a novel technique to automaticallyadapt the basal insulin profile of a person with person with type 1 diabetes. Thepresented technique, which is based on Run-to-Run control and Case-BasedReasoning, overcomes some of the limitations of previously proposedapproaches and has been proved to be robust in front of realistic intra-dayvariability. Over a period of 5 weeks on 10 virtual adult subjects, a significantreduction on the percentage of time in hyperglycemia (<70mg/dl) (from 14.3±5.6to 1.6±1.7, p< 0.01), without a significant increase on the percentage of time inhypoglycemia (>180mg/dl) (from 10.2±5.9 to 1.6±1.7, p=0.1), was achieved.
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Journal articleKupcsik A, Deisenroth MP, Peters J, et al., 2017,
Model-based contextual policy search for data-efficient generalization of robot skills
, Artificial Intelligence, Vol: 247, Pages: 415-439, ISSN: 0004-3702© 2014 Elsevier B.V. In robotics, lower-level controllers are typically used to make the robot solve a specific task in a fixed context. For example, the lower-level controller can encode a hitting movement while the context defines the target coordinates to hit. However, in many learning problems the context may change between task executions. To adapt the policy to a new context, we utilize a hierarchical approach by learning an upper-level policy that generalizes the lower-level controllers to new contexts. A common approach to learn such upper-level policies is to use policy search. However, the majority of current contextual policy search approaches are model-free and require a high number of interactions with the robot and its environment. Model-based approaches are known to significantly reduce the amount of robot experiments, however, current model-based techniques cannot be applied straightforwardly to the problem of learning contextual upper-level policies. They rely on specific parametrizations of the policy and the reward function, which are often unrealistic in the contextual policy search formulation. In this paper, we propose a novel model-based contextual policy search algorithm that is able to generalize lower-level controllers, and is data-efficient. Our approach is based on learned probabilistic forward models and information theoretic policy search. Unlike current algorithms, our method does not require any assumption on the parametrization of the policy or the reward function. We show on complex simulated robotic tasks and in a real robot experiment that the proposed learning framework speeds up the learning process by up to two orders of magnitude in comparison to existing methods, while learning high quality policies.
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Journal articleDawes T, Simoes monteiro de marvao A, Shi W, et al., 2017,
Machine learning of three-dimensional right ventricular motion enables outcome prediction in pulmonary hypertension: a cardiac MR imaging study
, Radiology, Vol: 283, Pages: 381-390, ISSN: 1527-1315Purpose: To determine if patient survival and mechanisms of right ventricular (RV) failure in pulmonary hypertension (PH) could be predicted using supervised machine learning of three dimensional patterns of systolic cardiac motion. Materials and methods: The study was approved by a research ethics committee and participants gave written informed consent. 256 patients (143 females, mean age 63 ± 17) with newly diagnosed PH underwent cardiac MR imaging, right heart catheterization (RHC) and six minute walk testing (6MWT) with a median follow up of 4.0 years. Semi automated segmentation of short axis cine images was used to create a three dimensional model of right ventricular motion. Supervised principal components analysis identified patterns of systolic motion which were most strongly predictive of survival. Survival prediction was assessed by the difference in median survival time and the area under the curve (AUC) using time dependent receiver operator characteristic for one year survival. Results: At the end of follow up 33% (93/256) died and one underwent lung transplantation. Poor outcome was predicted by a loss of effective contraction in the septum and freewall coupled with reduced basal longitudinal motion. When added to conventional imaging, hemodynamic, functional and clinical markers, three dimensional cardiac motion improved survival prediction (area under the curve 0.73 vs 0.60, p<0.001) and provided greater differentiation by difference in median survival time between high and low risk groups (13.8 vs 10.7 years, p<0.001). Conclusion:Three dimensional motion modeling with machine learning approaches reveal the adaptations in function that occur early in right heart failure and independently predict outcomes in newly diagnosed PH patients.
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Journal articleRankothge W, Le F, Russo A, et al., 2017,
Optimizing resource allocation for virtualized network functions in a cloud center using genetic algorithms
, IEEE Transactions on Network and Service Management, Vol: 14, Pages: 343-356, ISSN: 1932-4537With the introduction of network function virtualization technology, migrating entire enterprise data centers into the cloud has become a possibility. However, for a cloud service provider (CSP) to offer such services, several research problems still need to be addressed. In previous work, we have introduced a platform, called network function center (NFC), to study research issues related to virtualized network functions (VNFs). In an NFC, we assume VNFs to be implemented on virtual machines that can be deployed in any server in the CSP network. We have proposed a resource allocation algorithm for VNFs based on genetic algorithms (GAs). In this paper, we present a comprehensive analysis of two resource allocation algorithms based on GA for: 1) the initial placement of VNFs and 2) the scaling of VNFs to support traffic changes. We compare the performance of the proposed algorithms with a traditional integer linear programming resource allocation technique. We then combine data from previous empirical analyses to generate realistic VNF chains and traffic patterns, and evaluate the resource allocation decision making algorithms. We assume different architectures for the data center, implement different fitness functions with GA, and compare their performance when scaling over the time.
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Journal articleSchulz C, Toni F, 2017,
Labellings for assumption-based and abstract argumentation
, International Journal of Approximate Reasoning, Vol: 84, Pages: 110-149, ISSN: 1873-4731The semantics of Assumption-Based Argumentation (ABA) frameworks are traditionally characterised as assumption extensions, i.e. sets of accepted assumptions. Assumption labellings are an alternative way to express the semantics of flat ABA frameworks, where one of the labels in, out, or undec is assigned to each assumption. They are beneficial for applications where it is important to distinguish not only between accepted and non-accepted assumptions, but further divide the non-accepted assumptions into those which are clearly rejected and those which are neither accepted nor rejected and thus undecided. We prove one-to-one correspondences between assumption labellings and extensions for the admissible, grounded, complete, preferred, ideal, semi-stable and stable semantics. We also show how the definition of assumption labellings for flat ABA frameworks can be extended to assumption labellings for any (flat and non-flat) ABA framework, enabling reasoning with a wider range of scenarios. Since flat ABA frameworks are structured instances of Abstract Argumentation (AA) frameworks, we furthermore investigate the relation between assumption labellings for flat ABA frameworks and argument labellings for AA frameworks. Building upon prior work on complete assumption and argument labellings, we prove one-to-one correspondences between grounded, preferred, ideal, and stable assumption and argument labellings, and a one-to-many correspondence between admissible assumption and argument labellings. Inspired by the notion of admissible assumption labellings we introduce committed admissible argument labellings for AA frameworks, which correspond more closely to admissible assumption labellings of ABA frameworks than admissible argument labellings do.
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Journal articleErasmus JC, Bruche S, Pizarro L, et al., 2017,
Corrigendum: Defining functional interactions during biogenesis of epithelial junctions
, Nature Communications, Vol: 8, Pages: 14195-14195, ISSN: 2041-1723The original version of this Article (https://doi.org/10.1038/ncomms13542) contained an error in the spelling of the author Tommaso Poggioli, which was incorrectly given as Tommaso Pogglioli. This has now been corrected in both the PDF and HTML versions of the Article.
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Conference paperDragiev S, Russo A, Broda K, et al., 2017,
An abductive-inductive algorithm for probabilistic inductive logic programming
, 26th International Conference on Inductive Logic Programming, Pages: 20-26, ISSN: 1613-0073The integration of abduction and induction has lead to a variety of non-monotonic ILP systems. XHAIL is one of these systems, in which abduction is used to compute hypotheses that subsume Kernel Sets. On the other hand, Peircebayes is a recently proposed logic-based probabilistic programming approach that combines abduction with parameter learning to learn distributions of most likely explanations. In this paper, we propose an approach for integrating probabilistic inference with ILP. The basic idea is to redefine the inductive task of XHAIL as a statistical abduction, and to use Peircebayes to learn probability distribution of hypotheses. An initial evaluation of the proposed algorithm is given using synthetic data.
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Journal articlePesl P, Herrero P, Reddy M, et al., 2017,
Case-Based Reasoning for Insulin Bolus Advice.
, J Diabetes Sci Technol, Vol: 11, Pages: 37-42BACKGROUND: Insulin bolus calculators assist people with Type 1 diabetes (T1D) to calculate the amount of insulin required for meals to achieve optimal glucose levels but lack adaptability and personalization. We have proposed enhancing bolus calculators by the means of case-based reasoning (CBR), an established problem-solving methodology, by individualizing and optimizing insulin therapy for various meal situations. CBR learns from experiences of past similar meals, which are described in cases through a set of parameters (eg, time of meal, alcohol, exercise). This work discusses the selection, representation and effect of case parameters used for a CBR-based Advanced Bolus Calculator for Diabetes (ABC4D). METHODS: We analyzed the usage and effect of selected parameters during a pilot study (n = 10), where participants used ABC4D for 6 weeks. Retrospectively, we evaluated the effect of glucose rate of change before the meal on the glycemic excursion. Feedback from study participants about the choice of parameters was obtained through a nonvalidated questionnaire. RESULTS: Exercise and alcohol were the most frequently used parameters, which was congruent with the feedback from study participants, who found these parameters most useful. Furthermore, cases including either exercise or alcohol as parameter showed a trend in reduction of insulin at the end of the study. A significant difference ( P < .01) was found in glycemic outcomes for meals where glucose rate of change was rising compared to stable rate of change. CONCLUSIONS: Results from the 6-week study indicate the potential benefit of including parameters exercise, alcohol and glucose-rate of change for insulin dosing decision support.
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Conference paperChamberlain BP, Humby C, Deisenroth MP, 2017,
Probabilistic Inference of Twitter Users' Age Based on What They Follow.
, Publisher: Springer, Pages: 191-203 -
Journal articleBroda KB, Law M, Russo A, 2016,
Iterative Learning of Answer Set Programs with Context Dependent Examples
, Theory and Practice of Logic Programming, Vol: 16, Pages: 834-848, ISSN: 1475-3081In recent years, several frameworks and systems have been proposed that extend InductiveLogic Programming (ILP) to the Answer Set Programming (ASP) paradigm. In ILP, examplesmust all be explained by a hypothesis together with a given background knowledge. In existingsystems, the background knowledge is the same for all examples; however, examples may becontext-dependent. This means that some examples should be explained in the context ofsome information, whereas others should be explained in different contexts. In this paper, wecapture this notion and present a context-dependent extension of the Learning from OrderedAnswer Sets framework. In this extension, contexts can be used to further structure thebackground knowledge. We then propose a new iterative algorithm, ILASP2i, which exploitsthis feature to scale up the existing ILASP2 system to learning tasks with large numbersof examples. We demonstrate the gain in scalability by applying both algorithms to variouslearning tasks. Our results show that, compared to ILASP2, the newly proposed ILASP2isystem can be two orders of magnitude faster and use two orders of magnitude less memory,whilst preserving the same average accuracy
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