Living creatures are continuous sources of inspiration for designing synthetic materials. However, living creatures are typically different from synthetic materials because the former consist of living cells to support their growth and regeneration. Although natural systems can grow materials with sophisticated microstructures, how to harness living cells to grow materials with predesigned microstructures in engineering systems remains largely elusive. Here, an attempt to exploit living bacteria and 3D‐printed materials to grow bionic mineralized composites with ordered microstructures is reported. The bionic composites exhibit outstanding specific strength and fracture toughness, which are comparable to natural composites, and exceptional energy absorption capability superior to both natural and artificial counterparts. This report opens the door for 3D‐architectured hybrid synthetic–living materials with living ordered microstructures and exceptional properties.
We propose a new framework for automatic image annotation (AIA) of regions through segmentation based semantic analysis and discriminative classification. Given a test image, it is first segmented by a proposed texture-enhanced JSEG algorithm. Then these regions are represented by an extended bag-of-words model in which a feature vector, based on a visual lexicon with its vocabulary consisting of a visual word or a co-occurrence of multiple visual words, is constructed to represent the region content. Finally a concept classifier learned by a maximal figure-of-merit algorithm is used to predict the region labels. These models are discriminatively trained from image regions with multiple associations between regions and concepts. Experiments on a subset of the Corel 5K data set illustrate that our proposed approach to region AIA achieves more accurate annotation results than some sate-of-the-art algorithms.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.