Neural-signature methods for structured EHR prediction

Models that can effectively represent structured Electronic Healthcare Records (EHR) are central to an increasing range of applications in healthcare. Due to the sequential nature of health data, Recurrent Neural Networks have emerged as the dominant component within state-of-the-art architectures. The signature transform represents an alternative modelling paradigm for sequential data. This transform provides a non-learnt approach to creating a fixed vector representation of temporal features and has shown strong performances across an increasing number of domains, including medical data. However, the signature method has not yet been applied to structured EHR data. To this end, we follow recent work that enables the signature to be used as a differentiable layer within a neural architecture enabling application in high dimensional domains where calculation would have previously been intractable. Using a heart failure prediction task as an exemplar, we provide an empirical evaluation of different variations of the signature method and compare against state-of-the-art baselines. This first application of neural-signature methods in real-world healthcare data shows a competitive performance when compared to strong baselines and thus warrants further investigation within the health domain

An Algebraic Theory of Complexity for Discrete Optimization

International audienceDiscrete optimization problems arise in many different areas and are studied under many different names. In many such problems the quantity to be optimized can be expressed as a sum of functions of a restricted form. Here we present a unifying theory of complexity for problems of this kind. We show that the complexity of a finite-domain discrete optimization problem is determined by certain algebraic properties of the objective function, which we call weighted polymorphisms. We define a Galois connection between sets of rational-valued functions and sets of weighted polymorphisms and show how the closed sets of this Galois connection can be characterized. These results provide a new approach to studying the complexity of discrete optimization. We use this approach to identify certain maximal tractable subproblems of the general problem and hence derive a complete classification of complexity for the Boolean case

Game-based CSP

The search for a solution to a multi-criteria constraint optimisation problem can be shown to be analogous to game playing. By configuring agents to carry out game playing strategies within a constraint based search, gives a novel way of reaching solutions. In this paper, we describe how a constraint optimisation problem can be viewed as a game. For each formulation of a constraint problem as a game, the quality of solution depends on the gaming strategies employed by each player. We show that even when criteria are difficult to measure consistently, good balanced solutions can still be obtained using a heuristic approach

An Algebraic Theory of Complexity for Valued Constraints: Establishing a Galois Connection

The complexity of any optimisation problem depends critically on the form of the objective function. Valued constraint satisfaction problems are discrete optimisation problems where the function to be minimised is given as a sum of cost functions de ned on speci ed subsets of variables. These cost functions are chosen from some xed set ofavailable cost functions, known as a valued constraint language. We show in this paper that when the costs are non-negative rational numbers or in nite, then the complexity of a valued constraint problem is determined by certain algebraic properties of this valued constraint language, which we call weighted polymorphisms. We de ne a Galois connection between valued constraint languages and sets of weighted polymorphisms and show how the closed sets of this Galois connection can be characterised. These results provide a new approach in the search for tractable valued constraint languages

An Algebraic Theory of Complexity for Valued Constraints: Establishing a Galois Connection

The complexity of any optimisation problem depends critically on the form of the objective function. Valued constraint satisfaction problems are discrete optimisation problems where the function to be minimised is given as a sum of cost functions de ned on speci ed subsets of variables. These cost functions are chosen from some xed set ofavailable cost functions, known as a valued constraint language. We show in this paper that when the costs are non-negative rational numbers or in nite, then the complexity of a valued constraint problem is determined by certain algebraic properties of this valued constraint language, which we call weighted polymorphisms. We de ne a Galois connection between valued constraint languages and sets of weighted polymorphisms and show how the closed sets of this Galois connection can be characterised. These results provide a new approach in the search for tractable valued constraint languages

THERMAL TEST SCHEDULING USING CONSTRAINT PROGRAMMING

Abstract: Temperature cycling test is one of the key stages in the process of testing circuit packs in telecommunications. To obtain a good overall test schedule requires that the thermal test is carried out efficiently i.e. with the minimum number of runs and valid configurations of packs at each run. However, finding valid configurations and building them into a minimal thermal test schedule is a difficult combinatorial problem. Constraint Programming allows both a way of modelling the rules of configuration and formulating a model to derive an optimal number of runs. We describe this model and the results obtained from it for a large multi-national telecommunications manufacturer. Copyright c○2006 IFA

Adversarial constraint satisfaction by game-tree search

Abstract. Many decision problems can be modelled as adversarial constraint satisfaction, which allows us to integrate methods from AI game playing. In particular, by using the idea of opponents, we can model both collaborative problem solving, where intelligent participants with different agendas must work together to solve a problem, and multi-criteria optimisation, where one decision maker must balance different objectives. In this paper, we focus on the case where two opponents take turns to instantiate constrained variables, each trying to direct the solution towards their own objective. We represent the process as game-tree search. We develop variable and value ordering heuristics based on game playing strategies. We examine the performance of various algorithms on general-sum graph colouring games, for both multi-participant and multi-criteria optimisation.