Quantifying variability within technique and performance in elite fast bowlers : is technical variability dysfunctional or functional?

In fast bowling, cricketers are expected to produce a range of delivery lines and lengths while maximising ball speed. From a coaching perspective, technique consistency has been typically associated with superior performance in these areas. However, although bowlers are required to bowl consistently, at the elite level they must also be able to vary line, length and speed to adapt to opposition batters’ strengths and weaknesses. The relationship between technique and performance variability (and consistency) has not been investigated in previous fast bowling research. Consequently, the aim of this study was to quantify both technique (bowling action and coordination) and performance variability in elite fast bowlers from Australian Junior and National Pace Squads. Technique variability was analysed to investigate whether it could be classified as functional or dysfunctional in relation to speed and accuracy.

The role of cross-functional teams on the alignment between technology innovation effectiveness and operational effectiveness

Organisations are increasingly investing in complex technological innovations, such as enterprise information systems, with the aim of improving the operation of the business, and in this way gaining competitive advantage. However, the implementation of technological innovations tends to have an excessive focus on either technology innovation effectiveness, or the resulting operational effectiveness. Focusing on either one of them is detrimental to long-term performance. Cross-functional teams have been used by many organisations as a way of involving expertise from different functional areas in the implementation of technologies. The role of boundary spanning actors is discussed as they bring a common language to the cross-functional teams. Multiple regression analysis has been used to identify the structural relationships and provide an explanation for the influence of cross-functional teams, technology innovation effectiveness and operational effectiveness in the continuous improvement of operational performance. The findings indicate that cross functional teams have an indirect influence on continuous improvement of operational performance through the alignment between technology innovation effectiveness and operational effectiveness.

Learning the kernel matrix with semidefinite programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive semidefinite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space - classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -using the labeled part of the data one can learn an embedding also for the unlabeled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method for learning the 2-norm soft margin parameter in support vector machines, solving an important open problem.

Learning the Kernel Matrix with Semi-Definite Programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive definite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space -- classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semi-definite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -- using the labelled part of the data one can learn an embedding also for the unlabelled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method to learn the 2-norm soft margin parameter in support vector machines, solving another important open problem. Finally, the novel approach presented in the paper is supported by positive empirical results.

Functional heterogeneity as a two-dimensional concept : empirical evidence for new venture teams

Previous research on entrepreneurial teams has failed to settle the controversy over whether team heterogeneity helps or hinders new venture performance. Reconciling this inconsistency, this paper suggests a new conceptual approach to disentangle differential effects of team heterogeneity by modeling two separate heterogeneity dimensions, namely knowledge scope and knowledge disparity. Analyzing unique data on functional experiences of the members of 337 start-up teams, we find support for our contention of team heterogeneity as a two-dimensional concept. Results suggest that knowledge disparity negatively relates to both start-ups’ entrepreneurial and innovative performance. In contrast, we find knowledge scope to positively affect entrepreneurial performance, while it shows an inverse U-shaped relationship to innovative start-up performance.

Osteopontin is a downstream effector of the PI3-kinase pathway in melanomas that is inversely correlated with functional PTEN

The tumor suppressor PTEN antagonizes phosphatidylinositol 3-kinase (PI3K), which contributes to tumorigenesis in many cancer types. While PTEN mutations occur in some melanomas, their precise mechanistic consequences have yet to be elucidated. We sought to identify novel downstream effectors of PI3K using a combination of genomic and functional tests. Microarray analysis of 53 melanoma cell lines identified 610 genes differentially expressed (P<0.05) between wild-type lines and those with PTEN aberrations. Many of these genes are known to be involved in the PI3K pathway and other signaling pathways influenced by PTEN. Validation of differential gene expression by qRT-PCR was performed in the original 53 cell lines and an independent set of 18 melanoma lines with known PTEN status. Osteopontin (OPN), a secreted glycophosphoprotein that contributes to tumor progression, was more abundant at both the mRNA and protein level in PTEN mutants. The inverse correlation between OPN and PTEN expression was validated (P<0.02) by immunohistochemistry using melanoma tissue microarrays. Finally, treatment of cell lines with the PI3K inhibitor LY294002 caused a reduction in expression of OPN. These data indicate that OPN acts downstream of PI3K in melanoma and provides insight into how PTEN loss contributes to melanoma development.