Reporting computing projects through structured abstracts: a quasi-experiment

Previous work has demonstrated that the use of structured abstracts can lead to greater completeness and clarity of information, making it easier for researchers to extract information about a study. In academic year 2007/08, Durham University’s Computer Science Department revised the format of the project report that final year students were required to write, from a ‘traditional dissertation’ format, using a conventional abstract, to that of a 20-page technical paper, together with a structured abstract. This study set out to determine whether inexperienced authors (students writing their final project reports for computing topics) find it easier to produce good abstracts, in terms of completeness and clarity, when using a structured form rather than a conventional form. We performed a controlled quasi-experiment in which a set of ‘judges’ each assessed one conventional and one structured abstract for its completeness and clarity. These abstracts were drawn from those produced by four cohorts of final year students: two preceding the change, and the two following. The assessments were performed using a form of checklist that is similar to those used for previous experimental studies. We used 40 abstracts (10 per cohort) and 20 student ‘judges’ to perform the evaluation. Scored on a scale of 0.1–1.0, the mean for completeness increased from 0.37 to 0.61 when using a structured form. For clarity, using a scale of 1–10, the mean score increased from 5.1 to 7.2. For a minimum goal of scoring 50% for both completeness and clarity, only 3 from 19 conventional abstracts achieved this level, while only 3 from 20 structured abstracts failed to reach it. We conclude that the use of a structured form for organising the material of an abstract can assist inexperienced authors with writing technical abstracts that are clearer and more complete than those produced without the framework provided by such a mechanism.     

Anti-windup controller design using linear parameter-varying control methods

In this paper, we seek to provide a systematic anti-windup control synthesis approach for systems with actuator saturation within a linear parameter-varying (LPV) design framework. The closed-loop induced L2 gain control problem is considered. Different from conventional two-step anti-windup design approaches, the proposed scheme directly utilizes saturation indicator parameters to schedule accordingly the parameter-varying controller. Hence, the synthesis conditions are formulated in terms of linear matrix inequalities (LMIs) that can be solved very efficiently. The resulting gain-scheduled controller is non-linear in general and would lead to graceful performance degradation in the presence of actuator saturation non-linearities and linear performance recovery. An aircraft longitudinal dynamics control problem with two input saturation non-linearities is used to demonstrate the effectiveness of the proposed LPV anti-windup scheme.     

Clustering of high throughput gene expression data

High throughput biological data need to be processed, analyzed, and interpreted to address problems in life sciences. Bioinformatics, computational biology, and systems biology deal with biological problems using computational methods. Clustering is one of the methods used to gain insight into biological processes, particularly at the genomics level. Clearly, clustering can be used in many areas of biological data analysis. However, this paper presents a review of the current clustering algorithms designed especially for analyzing gene expression data. It is also intended to introduce one of the main problems in bioinformatics – clustering gene expression data – to the operations research community.     

Types and associated type families for hardware simulation and synthesis

In this article we overview the design and implementation of the second generation of Kansas Lava. Driven by the needs and experiences of implementing telemetry decoders and other circuits, we have made a number of improvements to both the external API and the internal representations used. We have retained our dual shallow/deep representation of signals in general, but now have a number of externally visible abstractions for combinatorial and sequential circuits, and enabled signals. We introduce these abstractions, as well as our abstractions for reading and writing memory. Internally, we found the need to represent unknown values inside our circuits, so we made aggressive use of associated type families to lift our values to allow unknowns, in a principled and regular way. We discuss this design decision, how it unfortunately complicates the internals of Kansas Lava, and how we mitigate this complexity. Finally, when connecting Kansas Lava to the real world, the standardized idiom of using named input and output ports is provided by Kansas Lava using a new monad, called Fabric. We present the design of this Fabric monad, and illustrate its use in a small but complete example.     

Visualising Errors in Animal Pedigree Genotype Data

Genetic analysis of a breeding animal population involves determining the inheritance pattern of genotypes for multiple genetic markers across the individuals in the population pedigree structure. However, experimental pedigree genotype data invariably contains errors in both the pedigree structure and in the associated individual genotypes, introducing inconsistencies into the dataset, rendering them useless for further analysis. The resolution of these errors requires consideration of genotype inheritance patterns in the context of the pedigree structure. Existing pedigree visualisations are typically more suited to human pedigrees and are less suitable for large complex animal pedigrees which may exhibit cross generational inbreeding. Similarly, table‐based viewers of genotype marker data can highlight where errors become apparent but lack the functionality and interactive visual feedback to allow users to locate the origin of errors within the pedigree. In this paper, we detail a design study steered by biologists who work with pedigree data, and describe successive iterations through approaches and prototypes for viewing genotyping errors in the context of a displayed pedigree. We describe how each approach performs with real pedigree genotype data and why eventually we deemed them unsuitable. Finally, a novel prototype visualisation for pedigrees, which we term the ‘sandwich view’, is detailed and we demonstrate how the approach effectively communicates errors in the pedigree context, supporting the biologist in the error identification task.