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Sponges are animals that inhabit many aquatic environments while filtering small particles and ejecting metabolic wastes. They are composed of cells in a bulk extracellular matrix, often with an embedded scaffolding of stiff, siliceous spicules. We hypothesize that the mechanical response of this heterogeneous tissue to hydrodynamic flow influences cell proliferation in a manner that generates the body of a sponge. Towards a more complete picture of the emergence of sponge morphology, we dissected a set of species and subjected discs of living tissue to physiological shear and uniaxial deformations on a rheometer. Various species exhibited rheological properties such as anisotropic elasticity, shear softening and compression stiffening, negative normal stress, and non-monotonic dissipation as a function of both shear strain and frequency. Erect sponges possessed aligned, spicule-reinforced fibres which endowed three times greater stiffness axially compared with orthogonally. By contrast, tissue taken from shorter sponges was more isotropic but time-dependent, suggesting higher flow sensitivity in these compared with erect forms. We explore ecological and physiological implications of our results and speculate about flow-induced mechanical signalling in sponge cells. 

Cavitation has long been recognized as a crucial predictor, or precursor, to the ultimate failure of various materials, ranging from ductile metals to soft and biological materials. Traditionally, cavitation in solids is defined as an unstable expansion of a void or a defect within a material. The critical applied load needed to trigger this instability -- the critical pressure -- is a lengthscale independent material property and has been predicted by numerous theoretical studies for a breadth of constitutive models. While these studies usually assume that cavitation initiates from defects in the bulk of an otherwise homogeneous medium, an alternative and potentially more ubiquitous scenario can occur if the defects are found at interfaces between two distinct media within the body. Such interfaces are becoming increasingly common in modern materials with the use of multimaterial composites and layer-by-layer additive manufacturing methods. However, a criterion to determine the threshold for interfacial failure, in analogy to the bulk cavitation limit, has yet to be reported. In this work, we fill this gap. Our theoretical model captures a lengthscale independent limit for interfacial cavitation, and is shown to agree with our observations at two distinct lengthscales, via two different experimental systems. To further understand the competition between the two cavitation modes (bulk versus interface), we expand our investigation beyond the elastic response to understand the ensuing unstable propagation of delamination at the interface. A phase diagram summarizes these results, showing regimes in which interfacial failure becomes the dominant mechanism.

 

Researchers have investigated a number of strategies for capturing and analyzing data analyst event logs in order to design better tools, identify failure points, and guide users. However, this remains challenging because individual- and session-level behavioral differences lead to an explosion of complexity and there are few guarantees that log observations map to user cognition. In this paper we introduce a technique for segmenting sequential analyst event logs which combines data, interaction, and user features in order to create discrete blocks of goal-directed activity. Using measures of inter-dependency and comparisons between analysis states, these blocks identify patterns in interaction logs coupled with the current view that users are examining. Through an analysis of publicly available data and data from a lab study across a variety of analysis tasks, we validate that our segmentation approach aligns with users’ changing goals and tasks. Finally, we identify several downstream applications for our approach. 

A variety of systems have been proposed to assist users in detecting machine learning (ML) fairness issues. These systems approach bias reduction from a number of perspectives, including recommender systems, exploratory tools, and dashboards. In this paper, we seek to inform the design of these systems by examining how individuals make sense of fairness issues as they use different de-biasing affordances. In particular, we consider the tension between de-biasing recommendations which are quick but may lack nuance and ”what-if” style exploration which is time consuming but may lead to deeper understanding and transferable insights. Using logs, think-aloud data, and semi-structured interviews we find that exploratory systems promote a rich pattern of hypothesis generation and testing, while recommendations deliver quick answers which satisfy participants at the cost of reduced information exposure. We highlight design requirements and trade-offs in the design of ML fairness systems to promote accurate and explainable assessments. 

Exploratory Data Analysis (EDA) is a crucial step in any data science project. However, existing Python libraries fall short in supporting data scientists to complete common EDA tasks for statistical modeling. Their API design is either too low level, which is optimized for plotting rather than EDA, or too high level, which is hard to specify more fine-grained EDA tasks. In response, we propose DataPrep.EDA, a novel task-centric EDA system in Python. DataPrep.EDA allows data scientists to declaratively specify a wide range of EDA tasks in different granularity with a single function call. We identify a number of challenges to implement DataPrep.EDA, and propose effective solutions to improve the scalability, usability, customizability of the system. In particular, we discuss some lessons learned from using Dask to build the data processing pipelines for EDA tasks and describe our approaches to accelerate the pipelines. We conduct extensive experiments to compare DataPrep.EDA with Pandas-profiling, the state-of-the-art EDA system in Python. The experiments show that DataPrep.EDA significantly outperforms Pandas-profiling in terms of both speed and user experience. DataPrep.EDA is open-sourced as an EDA component of DataPrep: https://github.com/sfu-db/dataprep.