Common cotton textiles are hydrophilic and oleophilic in nature. Superhydrophobic cotton textiles have the potential to be used as self-cleaning fabrics, but they typically are not super oil-repellent. Poor oil repellency may easily compromise the self-cleaning property of these fabrics. Here, we report on the preparation of superoleophobic cotton textiles based on a multilength-scale structure, as demonstrated by a high hexadecane contact angle (153 degrees for 5 microL droplets) and low roll-off angle (9 degrees for 20 microL droplets). The multilength-scale roughness was based on the woven structure, with additional two layers of silica particles (microparticles and nanoparticles, respectively) covalently bonded to the fiber. Superoleophobicity was successfully obtained by incorporating perfluoroalkyl groups onto the surface of the modified cotton. It proved to be essential to add the nanoparticle layer in achieving superoleophobicity, especially in terms of low roll-off angles for hexadecane.

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Spider dragline silk with its superlative tensile properties provides an ideal system to study the relationship between morphology and mechanical properties of a structural protein. Accordingly, we synthesized two hybrid multiblock copolymers by condensing poly(alanine) [(Ala)(5)] blocks of the structural proteins (spidroin MaSp1 and MaSp2) of spider dragline silk with different oligomers of isoprene (2200 and 5000 Da) having reactive end groups. The synthetic multiblock polymer displayed similar secondary structure to that of natural spidroin, the peptide segment forming a beta-sheet structure. These multiblock polymers showed a significant solubility in the component solvents. Moreover, the copolymer which contains the short polyisoprene segment would aggregate into a micellar-like structure, as observed by TEM.

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Polysaccharides were believed to play an important role in the mineralization process of many organisms. As the source of continuously and uniformly releasing alginate molecules and Ca(2+), alginate/Ca nanospherical gel was employed in the solution to induce the nucleation and growth of CaCO(3). Time-resolved transmission electron microscopy (TEM) was applied to study the crystallization at a very early stage. It was found that the initially formed lens-like vaterite particles gradually dissolved from the middle of the particle and released alginate molecules and Ca(2+) back into the system. As reaction time increased, the released substances were involved in the next stage of crystallization of CaCO(3), in the form of needle-like and shuttle-like aragonite particles sequentially depending on the concentration of alginate molecules and Ca(2+). "Egg-box" conformation of alginate and Ca(2+) was considered a skeleton for the growth of such aragonite particles. Notably, shuttle-like aragonite particles were composed of "bricks" of several hundred nanometers in size, which were very similar to biogenetic nacreous layers in shells.

The presence of additives has demonstrated strong effects on the crystallization and morphology of calcium carbonate (CaCO 3 ). To understand the mediating function of alginate on the growth of CaCO 3 , we design a novel method to add alginate molecules and inorganic ions mildly and continuously to the mineralization system: a Ca-alginate gel is used as a slow-releasing source of calcium ions and alginate molecules; the gel is gradually broken down by the diffusion of CO 2 to the solution, inducing the slow release of Ca 2+ and alginate molecules. The slowly released alginate is involved in the nucleation and growth of CaCO 3 , in the form of micro-sized lens-like particles with a vaterite polymorph and composed of fused nanoparticles. With the increasing reaction time, the lenslike CaCO 3 particles gradually develop into a hollow structure and finally turn into ring-shaped CaCO 3 , in which the polymorph of CaCO 3 remains vaterite. The formation of the lens-like particles is the result of the partially-oriented aggregation of primary nanoparticles mediated by alginate. The further evolution of the morphology to ring-shaped particles is due to a dissolution-recrystallization process as well as Ostward ripening.

Fully convolutional neural network (FCN) has been dominating the game of face detection task for a few years with its congenital capability of sliding-window-searching with shared kernels, which boiled down all the redundant calculation, and most recent state-of-the-art methods such as Faster-RCNN, SSD, YOLO and FPN use FCN as their backbone. So here comes one question: Can we find a universal strategy to further accelerate FCN with higher accuracy, so could accelerate all the recent FCN-based methods? To analyze this, we decompose the face searching space into two orthogonal directions, 'scale' and 'spatial'. Only a few coordinates in the space expanded by the two base vectors indicate foreground. So if FCN could ignore most of the other points, the searching space and false alarm should be significantly boiled down. Based on this philosophy, a novel method named scale estimation and spatial attention proposal (S 2 AP ) is proposed to pay attention to some specific scales and valid locations in image pyramid. Furthermore, we adopt a masked-convolution operation based on the attention result to accelerate FCN calculation. Experiments show that FCN-based method RPN can be accelerated by about 4× with the help of S 2 AP and masked-FCN and at the same time it can also achieve the state-of-the-art on FDDB, AFW and MALF face detection benchmarks as well.

Most existing methods for pedestrian attribute recognition in video surveillance can be formulated as a multi-label image classification methodology, while attribute localization is usually disregarded due to the low image qualities and large variations of camera viewpoints and human poses. In this paper, we propose a weakly-supervised learning based approaching to implementing multi-attribute classification and localization simultaneously, without the need of bounding box annotations of attributes. Firstly, a set of mid-level attribute features are discovered by a multi-scale attribute-aware module receiving the outputs of multiple inception layers in a deep Convolution Neural Network (CNN) e.g., GoogLeNet, where a Flexible Spatial Pyramid Pooling (FSPP) operation is performed to acquire the activation maps of attribute features. Subsequently, attribute labels are predicted through a fully-connected layer which performs the regression between the response magnitudes in activation maps and the image-level attribute annotations. Finally, the locations of pedestrian attributes can be inferred by fusing the multiple activation maps, where the fusion weights are estimated as the correlation strengths between attributes and relevant mid-level features. To validate the proposed approach, extensive experiments are performed on the two currently largest pedestrian attribute datasets, i.e.