Plethodon larselli

Number of Pages: 1Place of Publication: Kensington, MarylandIntegrative BiologyGeological Science

Tamagawa Numbers for Motives with (Non-Commutative) Coefficients

Let $M$ be a motive which is defined over a number field and admits an action of a finite dimensional semisimple \bq-algebra $A$. We formulate and study a conjecture for the leading coefficient of the Taylor expansion at $0$ of the $A$-equivariant $L$-function of $M$. This conjecture simultaneously generalizes and refines the Tamagawa number conjecture of Bloch, Kato, Fontaine, Perrin-Riou et al. and also the central conjectures of classical Galois module theory as developed by Fröhlich, Chinburg, M. Taylor et al. The precise formulation of our conjecture depends upon the choice of an order \A in $A$ for which there exists a `projective \A-structure' on $M$. The existence of such a structure is guaranteed if \A is a maximal order, and also occurs in many natural examples where \A is non-maximal. In each such case the conjecture with respect to a non-maximal order refines the conjecture with respect to a maximal order. We develop a theory of determinant functors for all orders in $A$ by making use of the category of virtual objects introduced by Deligne

Seglearn: A Python Package for Learning Sequences and Time Series

Seglearn is an open-source python package for machine learning time series or sequences using a sliding window segmentation approach. The implementation provides a flexible pipeline for tackling classification, regression, and forecasting problems with multivariate sequence and contextual data. This package is compatible with scikit-learn and is listed under scikit-learn Related Projects. The package depends on numpy, scipy, and scikit-learn. Seglearn is distributed under the BSD 3-Clause License. Documentation includes a detailed API description, user guide, and examples. Unit tests provide a high degree of code coverage

Robust particle outline extraction and its application to digital on-line holography

Peer reviewedPostprin

The importance of psychological well-being in organisational settings: moving beyond the pleasure principle

In contrast to the emphasis on affective states as components of Subjective Well-Being (SWB), the Psychological Well-Being (PWB) approach considers the role of personal resources, such as mastery and efficacy beliefs, a sense of autonomy, positive relatedness with others, and self acceptance. This study of 679 high-school teachers was based on the Organisational Health Research Framework and compared the contribution of PWB, personality and organisational climate to the prediction of SWB and organisational well-being. PWB was identified as a significant predictor of SWB even after controlling for demographic characteristics, organisational climate and personality variables with 46% of the variance in PA and 47% of the variance in NA explained. In addition, PWB contributed uniquely to the prediction of school morale and school distress with the overall set of predictors accounting for 69% of the variance in school morale and 66% of the variance in school distress. Individual interventions which promote PWB components would appear to be a most important avenue by which to improve employee SWB, while organisational interventions that focus on improving the organisational climate should have greater impact on organisational well-bein