On-ice measures of external load in relation to match outcome in elite female ice hockey

The aim of this study is to investigate the differences between select on-ice measures using inertial movement sensors based on match outcome, and to determine changes in player movements across three periods of play. Data were collected during one season of competition in elite female ice hockey players (N = 20). Two-factor mixed effects ANOVAs for each skating position were performed to investigate the differences in match outcome, as well as differences in external load measures during the course of a match. For match outcome, there was a small difference for forwards in explosive ratio (p = 0.02, ES = 0.26) and percentage high force strides (p = 0.04, ES = 0.50). When viewed across three periods of a match, moderate differences were found in skating load (p = 0.01, ES = 0.75), explosive efforts (p = 0.04, ES = 0.63), and explosive ratio (p = 0.002, ES = 0.87) for forwards, and in PlayerLoad (p = 0.01, ES = 0.70), explosive efforts (p = 0.04, ES = 0.63), and explosive ratio (p = 0.01, ES = 0.70) for defense. When examining the relevance to match outcome, external load measures associated with intensity appear to be an important factor among forwards. These results may be helpful for coaches and sport scientists when making decisions pertaining to training and competition strategies.York University Librarie

Nonlinear diffusion in two-dimensional ordered porous media based on a free volume theory.

A continuum nonlinear diffusion model is developed to describe molecular transport in ordered porous media. An existing generic van der Waals equation of state based free volume theory of binary diffusion coefficients is modified and introduced into the two-dimensional diffusion equation. The resulting diffusion equation is solved numerically with the alternating-direction fully implicit method under Neumann boundary conditions. Two types of pore structure symmetries are considered, hexagonal and cubic. The former is modeled as parallel channels while in case of the latter equal-sized channels are placed perpendicularly thus creating an interconnected network. First, general features of transport in both systems are explored, followed by the analysis of the impact of molecular properties on diffusion inside and out of the porous matrix. The influence of pore size on the diffusion-controlled release kinetics is assessed and the findings used to comment recent experimental studies of drug release profiles from ordered mesoporous silicates

Considerations in Representation Selection for Problem Solving: A Review

Choosing how to represent knowledge effectively is a long-standing open problem. Cognitive science has shed light on the taxonomisation of representational systems from the perspective of cognitive processes, but a similar analysis is absent from the perspective of problem solving, where the representations are employed. In this paper we review how representation choices are made for solving problems in the context of theorem proving from three perspectives: cognition, heterogeneity, and computational demands. We contrast the different factors that are most important for each perspective in the context of problem solving to produce a list of considerations for developers of problem solving tools regarding representations that are appropriate for particular users and effective for specific problem domains

Bayesian optimisation for premise selection in automated theorem proving (student abstract)

Modern theorem provers utilise a wide array of heuristics to control the search space explosion, thereby requiring optimisation of a large set of parameters. An exhaustive search in this multi-dimensional parameter space is intractable in most cases, yet the performance of the provers is highly dependent on the parameter assignment. In this work, we introduce a principled probabilistic framework for heuristic optimisation in theorem provers. We present results using a heuristic for premise selection and the Archive of Formal Proofs (AFP) as a case study.</jats:p

Now you see me (CME): Concept-based model extraction

Deep Neural Networks (DNNs) have achieved remarkable performance on a range of tasks. A key step to further empowering DNN-based approaches is improving their explainability. In this work we present CME: a concept-based model extraction framework, used for analysing DNN models via concept-based extracted models. Using two case studies (dSprites, and Caltech UCSD Birds), we demonstrate how CME can be used to (i) analyse the concept information learned by a DNN model (ii) analyse how a DNN uses this concept information when predicting output labels (iii) identify key concept information that can further improve DNN predictive performance (for one of the case studies, we showed how model accuracy can be improved by over 14%, using only 30% of the available concepts)

Correspondence-based analogies for choosing problem representations

Mathematics and computing students learn new concepts and fortify their expertise by solving problems. The representation of a problem, be it through algebra, diagrams, or code, is key to understanding and solving it. Multiple-representation interactive environments are a promising approach, but the task of choosing an appropriate representation is largely placed on the user. We propose a new method to recommend representations based on correspondences: conceptual links between domains. Correspondences can be used to analyse, identify, and construct analogies even when the analogical target is unknown. This paper explains how correspondences build on probability theory and Gentner's structure-mapping framework; proposes rules for semi-automated correspondence discovery; and describes how correspondences can explain and construct analogies

How to (Re)represent it?

Choosing an effective representation is fundamental to the ability of the representation’s user to exploit it for the intended purpose. The major contribution of this paper is to provide a novel, flexible framework, rep2rep, that can be used by AI systems to recommend effective representations. What makes an effective representation is determined by whether it expresses the necessary information, supports the execution of tasks, and reflects the user’s cognitive abilities. In general, there is no single ‘most effective’ representation for every problem and every user, which makes it difficult to choose one from the plethora of possible representations. To address this, rep2rep includes: a domain-independent language for describing representations, algorithms that compute measures of informational suitability and cognitive cost, and uses these measures to recommend representations. We demonstrate the application of rep2rep in the probability domain. Importantly, our framework provides the foundations for personalised interaction with AI systems in the context of representation choice

What Makes an Effective Representation of Information: A Formal Account of Observational Advantages

In order to effectively communicate information, the choice of representation is important. Ideally, a chosen representation will aid readers in making desired inferences. In this paper, we develop the theory of observation: what it means for one statement to be observable from another. Using observability, we give a formal characterization of the observational advantages of one representation of information over another. By considering observational advantages, people will be able to make better informed choices of representations of information. To demonstrate the benefit of observation and observational advantages, we apply these concepts to set theory and Euler diagrams. In particular, we can show that Euler diagrams have significant observational advantages over set theory. This formally justifies Larkin and Simon’s claim that “a diagram is (sometimes) worth ten thousand words”

Visual discovery and model-driven explanation of time series patterns

Gatherminer is an interactive visual tool for analysing time series data with two key strengths. First, it facilitates bottom-up analysis, i.e., the detection of trends and patterns whose shapes are not known beforehand. Second, it integrates data mining algorithms to explain such patterns in terms of the time series’ metadata attributes – an extremely difficult task if the space of attribute-value combinations is large. To accomplish these aims, Gatherminer automatically rearranges the data to visually expose patterns and clusters, whereupon users can select those groups they deem ‘interesting.’ To explain the selected patterns, the visualisation is tightly coupled with automated classification techniques, such as decision tree learning. We present a brief evaluation with telecommunications experts comparing our tool against their current commercial solution, and conclude that Gatherminer significantly improves both the completeness of analyses as well as analysts’ confidence therein.Advait is supported by an EPSRC+BT iCASE award and a Cambridge Computer Laboratory Robert Sansom scholarship.This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by IEEE