623 research outputs found

    A foundation for synthesising programming language semantics

    Get PDF
    Programming or scripting languages used in real-world systems are seldom designed with a formal semantics in mind from the outset. Therefore, the first step for developing well-founded analysis tools for these systems is to reverse-engineer a formal semantics. This can take months or years of effort. Could we automate this process, at least partially? Though desirable, automatically reverse-engineering semantics rules from an implementation is very challenging, as found by Krishnamurthi, Lerner and Elberty. They propose automatically learning desugaring translation rules, mapping the language whose semantics we seek to a simplified, core version, whose semantics are much easier to write. The present thesis contains an analysis of their challenge, as well as the first steps towards a solution. Scaling methods with the size of the language is very difficult due to state space explosion, so this thesis proposes an incremental approach to learning the translation rules. I present a formalisation that both clarifies the informal description of the challenge by Krishnamurthi et al, and re-formulates the problem, shifting the focus to the conditions for incremental learning. The central definition of the new formalisation is the desugaring extension problem, i.e. extending a set of established translation rules by synthesising new ones. In a synthesis algorithm, the choice of search space is important and non-trivial, as it needs to strike a good balance between expressiveness and efficiency. The rest of the thesis focuses on defining search spaces for translation rules via typing rules. Two prerequisites are required for comparing search spaces. The first is a series of benchmarks, a set of source and target languages equipped with intended translation rules between them. The second is an enumerative synthesis algorithm for efficiently enumerating typed programs. I show how algebraic enumeration techniques can be applied to enumerating well-typed translation rules, and discuss the properties expected from a type system for ensuring that typed programs be efficiently enumerable. The thesis presents and empirically evaluates two search spaces. A baseline search space yields the first practical solution to the challenge. The second search space is based on a natural heuristic for translation rules, limiting the usage of variables so that they are used exactly once. I present a linear type system designed to efficiently enumerate translation rules, where this heuristic is enforced. Through informal analysis and empirical comparison to the baseline, I then show that using linear types can speed up the synthesis of translation rules by an order of magnitude

    Supporting Academy Football Coaches to Develop Psychological Attributes in Male Academy Players

    Get PDF
    The overarching purpose of this thesis was to support academy football coaches to better facilitate the psychological development of young players. To achieve this purpose, three empirical studies were conducted, each with a respective aim. Study one (Chapter 3) aimed to first identify the key psychological attributes to develop within young academy players. Building on study one, study two (Chapter 4) aimed to explore the coaching strategies that can facilitate the development of each psychological attribute, and also to examine the observable behaviours that indicate that the attribute is developed. Finally, study three (Chapter 5) aimed to apply the knowledge constructed in study one and two by designing, delivering, and evaluating a sport psychology coach education and support programme at a case study football academy. Study one (Chapter 3) interviewed nine academy coaches who worked within a category one football academy, with eight psychological attributes constructed from thematic analysis of the interviews: commitment to develop, confidence, coping with the demands of high-level sport, drive to achieve goals, emotional control, resilience, self-aware and reflective, and strong work ethic. The findings provide greater direction for the attributes to develop in academy players but also indicate that current frameworks may not fully capture the key psychological attributes players need to develop in order to successfully progress out of the academy. Study two (Chapter 4) interviewed twelve, category one and two, academy football coaches to explore the coaching strategies used to develop each of the eight psychological attributes identified in study one. Fifty-two coaching strategies were constructed across the attributes providing academy coaches with a catalogue of user-friendly strategies to support their players’ psychological development with. Study two also identified behaviours that indicated the successful development of each attribute, knowledge that can help coaches to observe, intervene, monitor, and assess the psychological development of their players. Study three (Chapter 5) involved the design, delivery, and evaluation of a 14-month coach education and support programme with a case study academy: Dock FC. Summary coach interviews indicated that the programme was well received and helped increase awareness and to some degree their application of strategies to support the psychological development of their players. An indirect approach to the programme was taken to reflect the landscape of psychological support in academies present at the time of the study. Summary player focus groups, along with the coach interviews and researcher reflections indicated that this approach worked well, offering a tangible approach for sport psychology practitioners to adopt, more readily, in football academy environments. The research within this thesis offers empirically informed knowledge which extends the extant literature significantly. The thesis provides novel information on: (a) the key psychological attributes to prioritise the development of in the modern-day academy football player; (b) coaching strategies to support the development empirically informed psychological attributes; (c) behavioural indicators of successful psychological development; and (d) the real-world application of supporting academy coaches to develop targeted psychological attributes in the modern-day academy football academy. The insights provided in this thesis may help improve the holistic development that young football players receive throughout their football academy journey.<br/

    Sensing Collectives: Aesthetic and Political Practices Intertwined

    Get PDF
    Are aesthetics and politics really two different things? The book takes a new look at how they intertwine, by turning from theory to practice. Case studies trace how sensory experiences are created and how collective interests are shaped. They investigate how aesthetics and politics are entangled, both in building and disrupting collective orders, in governance and innovation. This ranges from populist rallies and artistic activism over alternative lifestyles and consumer culture to corporate PR and governmental policies. Authors are academics and artists. The result is a new mapping of the intermingling and co-constitution of aesthetics and politics in engagements with collective orders

    A survey of Bayesian Network structure learning

    Get PDF

    University of Windsor Graduate Calendar 2023 Spring

    Get PDF
    https://scholar.uwindsor.ca/universitywindsorgraduatecalendars/1027/thumbnail.jp

    Generalising weighted model counting

    Get PDF
    Given a formula in propositional or (finite-domain) first-order logic and some non-negative weights, weighted model counting (WMC) is a function problem that asks to compute the sum of the weights of the models of the formula. Originally used as a flexible way of performing probabilistic inference on graphical models, WMC has found many applications across artificial intelligence (AI), machine learning, and other domains. Areas of AI that rely on WMC include explainable AI, neural-symbolic AI, probabilistic programming, and statistical relational AI. WMC also has applications in bioinformatics, data mining, natural language processing, prognostics, and robotics. In this work, we are interested in revisiting the foundations of WMC and considering generalisations of some of the key definitions in the interest of conceptual clarity and practical efficiency. We begin by developing a measure-theoretic perspective on WMC, which suggests a new and more general way of defining the weights of an instance. This new representation can be as succinct as standard WMC but can also expand as needed to represent less-structured probability distributions. We demonstrate the performance benefits of the new format by developing a novel WMC encoding for Bayesian networks. We then show how existing WMC encodings for Bayesian networks can be transformed into this more general format and what conditions ensure that the transformation is correct (i.e., preserves the answer). Combining the strengths of the more flexible representation with the tricks used in existing encodings yields further efficiency improvements in Bayesian network probabilistic inference. Next, we turn our attention to the first-order setting. Here, we argue that the capabilities of practical model counting algorithms are severely limited by their inability to perform arbitrary recursive computations. To enable arbitrary recursion, we relax the restrictions that typically accompany domain recursion and generalise circuits (used to express a solution to a model counting problem) to graphs that are allowed to have cycles. These improvements enable us to find efficient solutions to counting fundamental structures such as injections and bijections that were previously unsolvable by any available algorithm. The second strand of this work is concerned with synthetic data generation. Testing algorithms across a wide range of problem instances is crucial to ensure the validity of any claim about one algorithm’s superiority over another. However, benchmarks are often limited and fail to reveal differences among the algorithms. First, we show how random instances of probabilistic logic programs (that typically use WMC algorithms for inference) can be generated using constraint programming. We also introduce a new constraint to control the independence structure of the underlying probability distribution and provide a combinatorial argument for the correctness of the constraint model. This model allows us to, for the first time, experimentally investigate inference algorithms on more than just a handful of instances. Second, we introduce a random model for WMC instances with a parameter that influences primal treewidth—the parameter most commonly used to characterise the difficulty of an instance. We show that the easy-hard-easy pattern with respect to clause density is different for algorithms based on dynamic programming and algebraic decision diagrams than for all other solvers. We also demonstrate that all WMC algorithms scale exponentially with respect to primal treewidth, although at differing rates

    University of Windsor Graduate Calendar 2023 Winter

    Get PDF
    https://scholar.uwindsor.ca/universitywindsorgraduatecalendars/1026/thumbnail.jp

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

    Get PDF
    LIPIcs, Volume 261, ICALP 2023, Complete Volum
    • …
    corecore