EXPRESS: Degree of Learning and Linear Forgetting

The aim of this study was to assess whether the degree of learning influences the observation of memory retention and forgetting that follows a linear pattern. According to our Retention Accuracy from Fragmented Traces (RAFT) model, one factor that should increase the likelihood of this is when there is greater learning of the material. Higher levels of learning should increase the number of trace components, making it more likely that reconstruction or partial retrieval can lead to an accurate response on a memory test. Here we report three new experiments, as well as re-analyses of existing data in the literature, to show that increasing the level of learning increases the likelihood of observing linear forgetting. For Experiment 1, people learned materials to different levels. This learning involved cued recall testing during memorization. Linear forgetting was observed with increased learning. For Experiment 2, learning did not involve cued recall testing. Linear forgetting was not observed. Although our aim was not to test theories of retrieval practice, for Experiment 3 we showed that when people engage in this, the pattern of retention and forgetting becomes more linear. Overall, these data are consistent with the RAFT theory and support mechanisms that it suggests can lead to the observation of linear forgetting.

Corrugated Diaphragm Actuator for Soft Robotic Applications

Soft robotics is projected to have a significant impact on healthcare, industry, and the military to deliver assistance in rehabilitation, daily living activities, repetitive motion tasks, and human performance augmentation. Many attempts have been made for application-specific robotic joints, robots, and exoskeletons using various actuator types, materials, and designs. The progress of creating soft robotic systems can be accelerated if a set of actuators with defined characteristics were developed, similar to conventional robotic actuators, which can be assembled to create desired systems including exoskeletons and end effectors. This work presents the design methodology of such a modular actuator, created with a novel corrugated diaphragm that can apply linear displacement, angular displacement, and force. This modular actuator approach allows for creating various robotic joints by arranging them into different configurations. The modular corrugated diaphragm actuator concept was validated through numerical simulation, fabrication, and testing. Linear displacement, angular displacement, and force characteristics were shown for a single module and in multi-module assemblies. Actuator assemblies that are configured in a serial and parallel manner were investigated to demonstrate the applicability and versatility of the concept of the modular corrugated diaphragm actuator for creating single and multi-DOF joints.

Why Are Species’ Traits Weak Predictors of Range Shifts?

We examine the evidence linking species’ traits to contemporary range shifts and find they are poor predictors of range shifts that have occurred over decades to a century. We then discuss reasons for the poor performance of traits for describing interspecific variation in range shifts from two perspectives: ( a) factors associated with species’ traits that degrade range-shift signals stemming from the measures used for species’ traits, traits that are typically not analyzed, and the influence of phylogeny on range-shift potential and ( b) issues in quantifying range shifts and relating them to species’ traits due to imperfect detection of species, differences in the responses of altitudinal and latitudinal ranges, and emphasis on testing linear relationships between traits and range shifts instead of nonlinear responses. Improving trait-based approaches requires a recognition that traits within individuals interact in unexpected ways and that different combinations of traits may be functionally equivalent. Expected final online publication date for the Annual Review of Ecology, Evolution, and Systematics, Volume 52 is November 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

ESTIMATION OF THE KRONECKER COVARIANCE MODEL BY QUADRATIC FORM

We propose a new estimator, the quadratic form estimator, of the Kronecker product model for covariance matrices. We show that this estimator has good properties in the large dimensional case (i.e., the cross-sectional dimension n is large relative to the sample size T). In particular, the quadratic form estimator is consistent in a relative Frobenius norm sense provided ${\log }^3n/T\to 0$ . We obtain the limiting distributions of the Lagrange multiplier and Wald tests under both the null and local alternatives concerning the mean vector $\mu $ . Testing linear restrictions of $\mu $ is also investigated. Finally, our methodology is shown to perform well in finite sample situations both when the Kronecker product model is true and when it is not true.

Fully automated image-based estimation of postural point-features in children with cerebral palsy using deep learning

The aim of this study was to provide automated identification of postural point-features required to estimate the location and orientation of the head, multi-segmented trunk and arms from videos of the clinical test ‘Segmental Assessment of Trunk Control’ (SATCo). Three expert operators manually annotated 13 point-features in every fourth image of 177 short (5–10 s) videos (25 Hz) of 12 children with cerebral palsy (aged: 4.52 ± 2.4 years), participating in SATCo testing. Linear interpolation for the remaining images resulted in 30 825 annotated images. Convolutional neural networks were trained with cross-validation, giving held-out test results for all children. The point-features were estimated with error 4.4 ± 3.8 pixels at approximately 100 images per second. Truncal segment angles (head, neck and six thoraco-lumbar–pelvic segments) were estimated with error 6.4 ± 2.8°, allowing accurate classification ( F 1 > 80%) of deviation from a reference posture at thresholds up to 3°, 3° and 2°, respectively. Contact between arm point-features (elbow and wrist) and supporting surface was classified at F 1 = 80.5%. This study demonstrates, for the first time, technical feasibility to automate the identification of (i) a sitting segmental posture including individual trunk segments, (ii) changes away from that posture, and (iii) support from the upper limb, required for the clinical SATCo.