SAniHead: Sketching Animal-Like 3D Character Heads Using a View-Surface Collaborative Mesh Generative Network.

In the game and film industries, modeling 3D heads plays a very important role in designing characters. Although human head modeling has been researched for a long time, few works have focused on animal-like heads, which are of more diverse shapes and richer geometric details. In this article, we present SAniHead, an interactive system for creating animal-like heads with a mesh representation from dual-view sketches. Our core technical contribution is a view-surface collaborative mesh generative network. Initially, a graph convolutional neural network (GCNN) is trained to learn the deformation of a template mesh to fit the shape of sketches, giving rise to a coarse model. It is then projected into vertex maps where image-to-image translation networks are performed for detail inference. After back-projecting the inferred details onto the meshed surface, a new GCNN is trained for further detail refinement. The modules of view-based detail inference and surface-based detail refinement are conducted in an alternating cascaded fashion, collaboratively improving the model. A refinement sketching interface is also implemented to support direct mesh manipulation. Experimental results show the superiority of our approach and the usability of our interactive system. Our work also contributes a 3D animal head dataset with corresponding line drawings.

Increasing Quantum Correlations Based on Measurement-Induced Disturbance via a Swapping Procedure with Two-Qubit Mixed States.

In this paper, quantum correlation (QC) swapping for certain separable two-qubit mixed states is treated. A QC quantifier, measurement-induced disturbance (MID) (Luo in Phys Rev A 77:022301, 2008), is employed to characterize and quantify QCs in the relevant states. Properties of all QCs in the swapping process are revealed. Particularly, it is found that MID can be increased through QC swapping for certain separable two-qubit mixed states.

Lecithin alleviates protein flocculation and enhances fat digestion in a model of infant formula emulsion.

This study investigates whether lecithin could fasten lipolysis through the alleviation of protein aggregation in an infant formula emulsion model. Our previous study reported low intestinal digestion of infant formula could be due to the aggregation of proteins that slow lipid digestion. The emulsion contained lipids droplets simulating the fatty acid composition in breast milk, different levels of lecithin and milk protein. The interphase proteins were replaced with lecithin in a dose-dependent manner. The results showed the addition of 5% and 7% lecithin improves the physical stability, narrows the range of particle size, reduces the mean particle size and increases the zeta potential. The 5% lecithin emulsion showed the highest rate and extent of lipid and protein digestion. These positive effects were caused by lecithin through stabilizing the emulsion and suppressing droplet flocculation after digestion. Lecithin promotes lipid digestion and may improve the "insufficient fat supply" in infant formula.