Beyond developable: computational design and fabrication with auxetic materials

We present a computational method for interactive 3D design and rationalization of surfaces via auxetic materials, i.e., flat flexible material that can stretch uniformly up to a certain extent. A key motivation for studying such material is that one can approximate doubly-curved surfaces (such as the sphere) using only flat pieces, making it attractive for fabrication. We physically realize surfaces by introducing cuts into approximately inextensible material such as sheet metal, plastic, or leather. The cutting pattern is modeled as a regular triangular linkage that yields hexagonal openings of spatially-varying radius when stretched. In the same way that isometry is fundamental to modeling developable surfaces, we leverage conformal geometry to understand auxetic design. In particular, we compute a global conformal map with bounded scale factor to initialize an otherwise intractable non-linear optimization. We demonstrate that this global approach can handle non-trivial topology and non-local dependencies inherent in auxetic material. Design studies and physical prototypes are used to illustrate a wide range of possible applications

Geodesics in Heat

We introduce the heat method for computing the shortest geodesic distance to a specified subset (e.g., point or curve) of a given domain. The heat method is robust, efficient, and simple to implement since it is based on solving a pair of standard linear elliptic problems. The method represents a significant breakthrough in the practical computation of distance on a wide variety of geometric domains, since the resulting linear systems can be prefactored once and subsequently solved in near-linear time. In practice, distance can be updated via the heat method an order of magnitude faster than with state-of-the-art methods while maintaining a comparable level of accuracy. We provide numerical evidence that the method converges to the exact geodesic distance in the limit of refinement; we also explore smoothed approximations of distance suitable for applications where more regularity is required

Diffusion is All You Need for Learning on Surfaces

We introduce a new approach to deep learning on 3D surfaces such as meshes or point clouds. Our key insight is that a simple learned diffusion layer can spatially share data in a principled manner, replacing operations like convolution and pooling which are complicated and expensive on surfaces. The only other ingredients in our network are a spatial gradient operation, which uses dot-products of derivatives to encode tangent-invariant filters, and a multi-layer perceptron applied independently at each point. The resulting architecture, which we call DiffusionNet, is remarkably simple, efficient, and scalable. Continuously optimizing for spatial support avoids the need to pick neighborhood sizes or filter widths a priori, or worry about their impact on network size/training time. Furthermore, the principled, geometric nature of these networks makes them agnostic to the underlying representation and insensitive to discretization. In practice, this means significant robustness to mesh sampling, and even the ability to train on a mesh and evaluate on a point cloud. Our experiments demonstrate that these networks achieve state-of-the-art results for a variety of tasks on both meshes and point clouds, including surface classification, segmentation, and non-rigid correspondence

A current landscape of provincial perinatal data collection in Canada.

BACKGROUND: The Canadian Perinatal Network (CPN) was launched in 2005 as a national perinatal database project designed to identify best practices in maternity care. The inaugural project of CPN is focused on interventions that optimize maternal and perinatal outcomes in women with threatened preterm birth at 22+0 to 28+6 weeks' gestation. OBJECTIVE: To examine existing data collection by perinatal health programs (PHPs) to inform decisions about shared data collection and CPN database construction. METHODS: We reviewed the database manuals and websites of all Canadian PHPs and compiled a list of data fields and their definitions. We compared these fields and definitions with those of CPN and the Canadian Minimal Dataset, proposed as a common dataset by the Canadian Perinatal Programs Coalition of Canadian PHPs. RESULTS: PHPs collect information on 2/3 of deliveries in Canada. PHPs consistently collect information on maternal demographics (including both maternal and neonatal personal identifiers), past obstetrical history, maternal lifestyle, aspects of labour and delivery, and basic neonatal outcomes. However, most PHPs collect insufficient data to enable identification of obstetric (and neonatal) practices associated with improved maternal and perinatal outcomes. In addition, there is between-PHP variability in defining many data fields. CONCLUSION: Construction of a separate CPN database was needed although harmonization of data field definitions with those of the proposed Canadian Minimal Dataset was done to plan for future shared data collection. This convergence should be the goal of researchers and clinicians alike as we construct a common language for electronic health records

Normative values for the Foot Posture Index

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Reading comprehension in autism spectrum disorders: The role of oral language and social functioning

Reading comprehension is an area of difficulty for many individuals with autism spectrum disorders (ASD). According to the Simple View of Reading, word recognition and oral language are both important determinants of reading comprehension ability. We provide a novel test of this model in 100 adolescents with ASD of varying intellectual ability. Further, we explore whether reading comprehension is additionally influenced by individual differences in social behaviour and social cognition in ASD. Adolescents with ASD aged 14-16 years completed assessments indexing word recognition, oral language, reading comprehension, social behaviour and social cognition. Regression analyses show that both word recognition and oral language explain unique variance in reading comprehension. Further, measures of social behaviour and social cognition predict reading comprehension after controlling for the variance explained by word recognition and oral language. This indicates that word recognition, oral language and social impairments may constrain reading comprehension in ASD