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Self-expression is central to mental well-being and mental health therapy. Art therapy offers a wide range of expressive mechanisms, allowing individuals to process their emotions when traditional therapies prove unsuccessful. However, a lack of expertise or comfort with artistic expression, along with cost and waiting times, may hinder one's ability to receive needed mental health support. Creative machines can offer novel therapeutic approaches enabling the bereaved to engage in creative expression as and when needed. In this paper, we apply a co-creative songwriting system, ALYSIA, as a new form of therapy for those who had recently suffered the loss of a loved one. We evaluate the utility of this creative system in aiding bereaved individuals through user testing. The utility of collaborative creative systems for adaptation to bereavement is discussed and may have implications for other therapeutic applications. [ABSTRACT FROM AUTHOR]

The manuscript describes and visualizes datasets from the datasets package in the R statistical software, focusing on descriptive statistics and visualizations that provide insights into the clusterability of these datasets. These publicly available datasets are contained in the R software system, and can be downloaded at https://www.r-project.org/, with documentation provided at https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/00Index.html. Further information on clusterability is found in the companion to this article, To Cluster or Not to Cluster: An Analysis of Clusterability Methods ? (https://doi.org/10.1016/j.patcog.2018.10.026). Brief descriptions and graphs of the variables contained in each dataset are provided in the form of means, extrema, quartiles, standard deviation and standard error. Two-dimensional plots for each pair of variables are provided. Original references to the data sets are included when available. Further, each dataset is reduced to a single dimension by each of two different methods: pairwise distances and principal component analysis. For the latter, only the first component is used. Histograms of the reduced data are included for every dataset using both methods.

Users of phylogenetic methods require rooted trees, because the direction of time depends on the placement of the root. While phylogenetic trees are typically rooted by using an out-group, this mechanism is inappropriate when the addition of an out-group changes the in-group topology. We perform a formal analysis of phylogenetic algorithms under the inclusion of distant out-groups. It turns out that linkage-based algorithms (including UPGMA) and a class of bisecting methods do not modify the topology of the in-group when an out-group is included. By contrast, the popular neighbour joining algorithm fails this property in a strong sense: every data set can have its structure destroyed by some arbitrarily distant outlier. Furthermore, including multiple outliers can lead to an arbitrary topology on the in-group. The standard rooting approach that uses out-groups may be fundamentally unsuited for neighbour joining.