Mitigating memory effects during undulatory locomotion on hysteretic materials dataset

Tracked snake data CoccipitalisMidlineCoords are .mat files with the 2D planar coordinates of the midline of Chionactis occipitalis snakes moving on the surface of 300 micron glass beads. CoccipitalisLiftingMidlineCoords are .mat files with the 3D coordinates of the midline of Chionactis occipitalis snakes moving on the surface of 300 micron glass beads.Undulatory swimming in flowing media like water is well studied, but little is known about locomotion in environments that are permanently deformed by body-substrate interactions like snakes in sand, eels in mud, and nematode worms in rotting fruit. We study the desert-specialist snake Chionactis occipitalis traversing granular matter and find body inertia is negligible despite rapid transit. New surface resistive force theory (RFT) calculation reveals this snake's waveform minimizes material memory effects and optimizes speed given anatomical limitations (power). RFT explains the morphology and waveform dependent performance of a diversity of non-sand-specialists, but over-predicts the capability of snakes with high slip. Robophysical experiments recapitulate aspects of these failure-prone snakes, elucidating how reencountering previously remodeled material hinders performance. This study reveals how memory effects stymied the locomotion of snakes in our previous study [Marvi et al, Science, 2014] and suggests the existence of a predictive model for history-dependent locomotion.This work was funded by NSF PoLS PHY-1205878, PHY-1150760, and CMMI-1361778. ARO W911NF-11-1-0514, U.S. DoD, NDSEG 32 CFR 168a (P.E.S.), and the Simons Southeast Center for Mathematics and Biolog

Albumin cartago: a "new" slow-moving alloalbumin

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Quantile Matrix Factorization for Collaborative Filtering

Matrix Factorization-based algorithms are among the state-of-the-art in Collaborative Filtering methods. In many of these models, a least squares loss functional is implicitly or explicitly minimized and thus the resulting estimates correspond to the conditional mean of the potential rating a user might give to an item. However they do not provide any information on the uncertainty and the confidence of the Recommendation. We introduce a novel Matrix Factorization algorithm that estimates the conditional quantiles of the ratings. Experimental results demonstrate that the introduced model performs well and can potentially be a very useful tool in Recommender Engines by providing a direct measure of the quality of the prediction

The spatial demography of new plants: urban creation and rural survival

Firm, Plant, Entry, Exit, Survival, Urban, Rural, D21, L1, L26, R30,