Chain shuttling polymerization enables an efficient production of ethylene–octene block copolymers (OBCs) that combine different mechanical properties in a polymer chain. However, this method results in molecular weight polydispersity and multiblock chain structure. The melt-phase behavior and mesophase transition of the polydisperse OBCs with low octene content but different molecular weight and block composition were investigated by rheology, differential scanning calorimetry (DSC), atomic force microscopic (AFM), polarized optical microscopy (POM), and small-angle X-ray scattering (SAXS). Three rheological methods, namely the deviation of the scaling dependence of zero shear viscosity on molecular weight, the terminal behavior and the failure of time–temperature superposition (TTS), and two-dimensional rheological correlation spectrum, are used to reveal the mesophase separation with increasing sensitivity. The occurrence of mesophase separation transitions (MST) was observed in such low octene content and low molecular weight OBC systems, with much lower degree of segregation than the theoretical predictions in diblock copolymers. The extent of mesophase separation is further justified by its effect on subsequent crystallization behaviors.

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The appearances of children are inherited from their parents, which makes it feasible to predict them. Predicting realistic children's faces may help settle many social problems, such as age-invariant face recognition, kinship verification, and missing child identification. It can be regarded as an imageto-image translation task. Existing approaches usually assume domain information in the image-to-image translation can be interpreted by "style", i.e., the separation of image content and style. However, such separation is improper for the child face prediction, because the facial contours between children and parents are not the same. To address this issue, we propose a new disentangled learning strategy for children's face prediction. We assume that children's faces are determined by genetic factors (compact family features, e.g., face contour), external factors (facial attributes irrelevant to prediction, such as moustaches and glasses), and variety factors (individual properties for each child). On this basis, we formulate predictions as a mapping from parents' genetic factors to children's genetic factors, and disentangle them from external and variety factors. In order to obtain accurate genetic factors and perform the mapping, we propose a ChildPredictor framework. It transfers human faces to genetic factors by encoders and back by generators. Then, it learns the relationship between the genetic factors of parents and children through a mapping function. To ensure the generated faces are realistic, we collect a large Family Face Database to train ChildPredictor and evaluate it on the FF-Database validation set. Experimental results demonstrate that ChildPredictor is superior to other well-known image-to-image translation methods in predicting realistic and diverse child faces. Implementation codes can be found at https://github.com/zhaoyuzhi/ChildPredictor.

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