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Physically based simulation is often combined with geometric mesh animation to add realistic soft‐body dynamics to virtual characters. This is commonly done using constraint‐based simulation whereby a soft‐tissue simulation is constrained to geometric animation of a subpart (or otherwise proxy representation) of the character. We observe that standard constraint‐based simulation suffers from an important flaw that limits the expressiveness of soft‐body dynamics. Namely, under correct physics, the frequency and amplitude of soft‐tissue dynamics arising from constraints (“inertial amplitude”) are coupled, and cannot be adjusted independently merely by adjusting the material properties of the model. This means that the space of physically based simulations is inherently limited and cannot capture all effects typically expected by computer animators. For example, animators need the ability to adjust the frequency, inertial amplitude, gravity sag and damping properties of the virtual character, independently from each other, as these are the primary visual characteristics of the soft‐tissue dynamics. We demonstrate that independence can be achieved by transforming the equations of motion into a non‐inertial reference coordinate frame, then scaling the resulting inertial forces, and then converting the equations of motion back to the inertial frame. Such scaling of inertia makes it possible for the animator to set the character's inertial amplitude independently from frequency. We also provide exact controls for the amount of character's gravity sag, and the damping properties. In our examples, we use linear blend skinning and pose‐space deformation for geometric mesh animation, and the Finite Element Method for soft‐body constrained simulation; but our idea of scaling inertial forces is general and applicable to other animation and simulation methods. We demonstrate our technique on several character examples.

 

There is an increasing attention to cellulose nanofibrils (CNFs) for food packaging applications due to their abundance, biodegradability, and low gas permeability. In this work, oxygen and water barrier performance is studied for bio-nanocomposite films formed by incorporation of two types of bentonite (PGN and PGV) at different loads (15, 30 and 45 wt%) into continuous CNF matrix. The resulting hybrid films were analyzed for their morphology, surface energy, mechanical strengths as well as water/oxygen barrier qualities. Both types of bentonite lowered the CNF degradation temperature and strength to some degree for reasons not so clear but perhaps due to partial disruption of the CNF H-bond network. It was revealed from microscopic study that clay particles form a layer within cellulose chains, resulting in alteration of composite structure. The contact angle analysis by polar and nonpolar liquids, suggested the PGN-containing samples were more hydrophilic; clay induced polar functionalities to the composite. While 15% PGN load reduced the water vapor transmission rate from 425 to 375 g/ m2 day, higher proportions of bentonite negatively affected this trend. Also, analysis of oxygen transmission rate showed the PGN effectively restricted the oxygen passage in dry state and to a lower extent at higher relative humidity. In WVTR analysis, PGN showed a superior performance over PGV attributable to its crystalline structure as evident in XRD patterns. The proposed hybrid CNF-BNT films in this study can present an eco-friendly alternative in packaging materials, especially where penetration of water vapor and oxygen is to be avoided. 

Recent advances in convolutional neural network (CNN) model interpretability have led to impressive progress in vi- sualizing and understanding model predictions. In partic- ular, gradient-based visual attention methods have driven much recent effort in using visual attention maps as a means for visual explanations. A key problem, however, is these methods are designed for classification and categorization tasks, and their extension to explaining generative models, e.g., variational autoencoders (VAE) is not trivial. In this work, we take a step towards bridging this crucial gap, proposing the first technique to visually explain VAEs by means of gradient-based attention. We present methods to generate visual attention from the learned latent space, and also demonstrate such attention explanations serve more than just explaining VAE predictions. We show how these attention maps can be used to localize anomalies in images, demonstrating state-of-the-art performance on the MVTec- AD dataset. We also show how they can be infused into model training, helping bootstrap the VAE into learning im- proved latent space disentanglement, demonstrated on the Dsprites dataset. 

Variations in the water vapor that atmospheric rivers (ARs) carry toward North America within Pacific storms strongly modulates the spatiotemporal distribution of west‐coast precipitation. The “AR Recon” program was established to improve forecasts of landfalling Pacific‐coast ARs and their associated precipitation. Dropsondes are deployed from weather reconnaissance aircraft and pressure sensors have been added to drifting ocean buoys to fill a major gap in standard weather observations, while research is being conducted on the potential for airborne Global Navigation Satellite System (GNSS) radio occultation (ARO) to also contribute to forecast improvement. ARO further expands the spatial coverage of the data collected during AR Recon flights. This study provides the first description of these data, which provide water vapor and temperature information typically as far as 300 km to the side of the aircraft. The first refractivity profiles from European Galileo satellites are provided and their accuracy is evaluated using the dropsondes. It is shown that spatial variations in the refractivity anomaly (difference from the climatological background) are modulated by AR features, including the low‐level jet and tropopause fold, illustrating the potential for RO measurements to represent key AR characteristics. It is demonstrated that assimilation of ARO refractivity profiles can influence the moisture used as initial conditions in a high‐resolution model. While the dropsonde measurements provide precise, in situ wind, temperature and water vapor vertical profiles beneath the aircraft, and the buoys provide surface pressure, ARO provides complementary thermodynamic information aloft in broad areas not otherwise sampled at no additional expendable cost.