12,595 research outputs found

    Efficient instance and hypothesis space revision in Meta-Interpretive Learning

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    Inductive Logic Programming (ILP) is a form of Machine Learning. The goal of ILP is to induce hypotheses, as logic programs, that generalise training examples. ILP is characterised by a high expressivity, generalisation ability and interpretability. Meta-Interpretive Learning (MIL) is a state-of-the-art sub-field of ILP. However, current MIL approaches have limited efficiency: the sample and learning complexity respectively are polynomial and exponential in the number of clauses. My thesis is that improvements over the sample and learning complexity can be achieved in MIL through instance and hypothesis space revision. Specifically, we investigate 1) methods that revise the instance space, 2) methods that revise the hypothesis space and 3) methods that revise both the instance and the hypothesis spaces for achieving more efficient MIL. First, we introduce a method for building training sets with active learning in Bayesian MIL. Instances are selected maximising the entropy. We demonstrate this method can reduce the sample complexity and supports efficient learning of agent strategies. Second, we introduce a new method for revising the MIL hypothesis space with predicate invention. Our method generates predicates bottom-up from the background knowledge related to the training examples. We demonstrate this method is complete and can reduce the learning and sample complexity. Finally, we introduce a new MIL system called MIGO for learning optimal two-player game strategies. MIGO learns from playing: its training sets are built from the sequence of actions it chooses. Moreover, MIGO revises its hypothesis space with Dependent Learning: it first solves simpler tasks and can reuse any learned solution for solving more complex tasks. We demonstrate MIGO significantly outperforms both classical and deep reinforcement learning. The methods presented in this thesis open exciting perspectives for efficiently learning theories with MIL in a wide range of applications including robotics, modelling of agent strategies and game playing.Open Acces

    Mathematical Modeling of Biological Systems

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    Mathematical modeling is a powerful approach supporting the investigation of open problems in natural sciences, in particular physics, biology and medicine. Applied mathematics allows to translate the available information about real-world phenomena into mathematical objects and concepts. Mathematical models are useful descriptive tools that allow to gather the salient aspects of complex biological systems along with their fundamental governing laws, by elucidating the system behavior in time and space, also evidencing symmetry, or symmetry breaking, in geometry and morphology. Additionally, mathematical models are useful predictive tools able to reliably forecast the future system evolution or its response to specific inputs. More importantly, concerning biomedical systems, such models can even become prescriptive tools, allowing effective, sometimes optimal, intervention strategies for the treatment and control of pathological states to be planned. The application of mathematical physics, nonlinear analysis, systems and control theory to the study of biological and medical systems results in the formulation of new challenging problems for the scientific community. This Special Issue includes innovative contributions of experienced researchers in the field of mathematical modelling applied to biology and medicine

    Recent Advances in Single-Particle Tracking: Experiment and Analysis

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    This Special Issue of Entropy, titled “Recent Advances in Single-Particle Tracking: Experiment and Analysis”, contains a collection of 13 papers concerning different aspects of single-particle tracking, a popular experimental technique that has deeply penetrated molecular biology and statistical and chemical physics. Presenting original research, yet written in an accessible style, this collection will be useful for both newcomers to the field and more experienced researchers looking for some reference. Several papers are written by authorities in the field, and the topics cover aspects of experimental setups, analytical methods of tracking data analysis, a machine learning approach to data and, finally, some more general issues related to diffusion

    Contemporary Teacher Education: A Global Perspective

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    The research in this Special Issue is an international collection of studies focusing on the current challenges and possibilities in teacher education. The contributors examine teacher education with theoretical and empirical approaches including both qualitative and quantitative research methods. The studies demonstrate that future teachers need high-level ethical and pedagogical skills to cope with the new challenges in education. With a research-based and holistic approach, we can educate good teachers for tomorrow's schools. Contributors to this collection of eleven articles reflect global issues in teacher education originating from Australia, Estonia, Finland, England, Portugal, and Sweden

    “Still Blundering into Sense”. Maria Edgeworth, her context, her legacy

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    “Still Blundering into Sense”. Maria Edgeworth, her context, her legacy. This collection of international contributions, as well as celebrating Maria Edgeworth’s 250th anniversary, proposes some further investigation on two fundamental aspects of her thought and legacy, still little examined in depth: her interest in the education of the young (and of the adults supposed to educate them) in an empirical perspective, explicitly scientific, open to different religious confessions and addressed to all social classes; and the urge for a wider and shared tolerance for alterity. The various essays in the collection offer some insight on the multi-layered relationships between the universe of education and its relationship with the development of knowledge, literature – particularly children’s literature – and pedagogy, as well as between women’s emancipation and the development of both individual and social identity. Their common ground is a dialogic perspective aiming to connect areas of scholarship, which the academia generally classifies into separate research fields

    Foundations for programming and implementing effect handlers

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    First-class control operators provide programmers with an expressive and efficient means for manipulating control through reification of the current control state as a first-class object, enabling programmers to implement their own computational effects and control idioms as shareable libraries. Effect handlers provide a particularly structured approach to programming with first-class control by naming control reifying operations and separating from their handling. This thesis is composed of three strands of work in which I develop operational foundations for programming and implementing effect handlers as well as exploring the expressive power of effect handlers. The first strand develops a fine-grain call-by-value core calculus of a statically typed programming language with a structural notion of effect types, as opposed to the nominal notion of effect types that dominates the literature. With the structural approach, effects need not be declared before use. The usual safety properties of statically typed programming are retained by making crucial use of row polymorphism to build and track effect signatures. The calculus features three forms of handlers: deep, shallow, and parameterised. They each offer a different approach to manipulate the control state of programs. Traditional deep handlers are defined by folds over computation trees, and are the original con-struct proposed by Plotkin and Pretnar. Shallow handlers are defined by case splits (rather than folds) over computation trees. Parameterised handlers are deep handlers extended with a state value that is threaded through the folds over computation trees. To demonstrate the usefulness of effects and handlers as a practical programming abstraction I implement the essence of a small UNIX-style operating system complete with multi-user environment, time-sharing, and file I/O. The second strand studies continuation passing style (CPS) and abstract machine semantics, which are foundational techniques that admit a unified basis for implementing deep, shallow, and parameterised effect handlers in the same environment. The CPS translation is obtained through a series of refinements of a basic first-order CPS translation for a fine-grain call-by-value language into an untyped language. Each refinement moves toward a more intensional representation of continuations eventually arriving at the notion of generalised continuation, which admit simultaneous support for deep, shallow, and parameterised handlers. The initial refinement adds support for deep handlers by representing stacks of continuations and handlers as a curried sequence of arguments. The image of the resulting translation is not properly tail-recursive, meaning some function application terms do not appear in tail position. To rectify this the CPS translation is refined once more to obtain an uncurried representation of stacks of continuations and handlers. Finally, the translation is made higher-order in order to contract administrative redexes at translation time. The generalised continuation representation is used to construct an abstract machine that provide simultaneous support for deep, shallow, and parameterised effect handlers. kinds of effect handlers. The third strand explores the expressiveness of effect handlers. First, I show that deep, shallow, and parameterised notions of handlers are interdefinable by way of typed macro-expressiveness, which provides a syntactic notion of expressiveness that affirms the existence of encodings between handlers, but it provides no information about the computational content of the encodings. Second, using the semantic notion of expressiveness I show that for a class of programs a programming language with first-class control (e.g. effect handlers) admits asymptotically faster implementations than possible in a language without first-class control

    The political economy of unequal exchange : A critique of the theory of Arghiri Emmanuel

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    This thesis is an examination of the political economy of Arghiri Emmanuel's theory of unequal exchange. Emmanuel's theory is studied both as a theory of trade and as a theory of imperialism. Emmanuel's original aim was to develop a modified labour theory of value to explain why in the course of international trade some nations grow rich at the expense of poor ones. This thesis argues that Emmanuel's theory of international exchange value failed as an attempt to extend the labour theory of value to international trade; it rests instead on a Smithian 'adding up' theory of value, where value is defined by the sum of the rewards to the factors. Further, it is argued that Emmanuel's attempt to explain the determination of the rewards to the factors in terms of physical bundles of goods is inadequate as an explanation of value. Consequently, it is shown that he is unable to account for the origins of surplus value or profit. As a result Emmanuel's conclusions regarding the formation of international values do not move beyond sophisticated neo-Mercantilism - where one nation grows richer at the expense of another by adding on to its cost of production a 'surplus upon alienation'. Thus Emmanuel's neo-Mercantilist theory of international exchange value and trade is shownto be logically consistent with his theory of Mercantile imperialism. But it is argued this theory is inadequate as a theory of imperialism as it is merely descriptive and fails to identify the underlying determination of the transfer of surplus from one nation to another. Having established the main failures of Emmanuel's theory of unequal exchange the thesis concludes by examining its relevance to a theory of financial imperialism

    A Cross-cultural Comparative Study of Dark Triad and Five-Factor Personality Models in Relation to Prejudice and Aggression

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    When examining socially malevolent outcomes in the form of prejudice and aggression, previous research on the Dark Triad and five-factor personality models has failed to consider potential cross-cultural differences. A deeper understanding of cross-cultural variations is necessary because these factors represent important social problems and risks. Prior investigation has so far only established preliminary relationships between the Dark Triad and the Big Five model and these outlined associations influence prejudice and aggression. Accordingly, this thesis consisted of two phases. The first examined interrelationships between Dark Triad traits (psychopathy, narcissism, and Machiavellianism) and Big Five personality dimensions (extraversion, neuroticism, agreeableness, openness, conscientiousness) in UK and Russian samples. The second used the results from the initial phase to inform the baseline of a predictive model, which was extended. Both phases used cross-sectional designs, correlation-based methods of analysis (e.g., confirmatory factor analysis, structural equation modelling with mediation, path analysis and invariance analysis), and large samples, comprising a range of backgrounds and ages. The analysis identified the strongest and weakest relationships between personality traits and prejudice and aggression. This research made an original contribution to existing literature by identifying novel relationships
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