Geometric bionics: Lotus effect helps polystyrene nanotube films get good blood compatibility

Various biomaterials have been widely used for manufacturing biomedical applications including artificial organs, medical devices and disposable clinical apparatus, such as vascular prostheses, blood pumps, artificial kidney, artificial hearts, dialyzers and plasma separators, which could be used in contact with blood^1^. However, the research tasks of improving hemocompatibility of biomaterials have been carrying out with the development of biomedical requirements^2^. Since the interactions that lead to surface-induced thrombosis occurring at the blood-biomaterial interface become a reason of familiar current complications with grafts therapy, improvement of the blood compatibility of artificial polymer surfaces is, therefore a major issue in biomaterials science^3^. After decades of focused research, various approaches of modifying biomaterial surfaces through chemical or biochemical methods to improve their hemocompatibility were obtained^1^. In this article, we report that polystyrene nanotube films with morphology similar to the papilla on lotus leaf can be used as blood-contacted biomaterials by virtue of Lotus effect^4^. Clearly, this idea, resulting from geometric bionics that mimicking the structure design of lotus leaf, is very novel technique for preparation of hemocompatible biomaterials

On the Universal Approximation Property and Equivalence of Stochastic Computing-based Neural Networks and Binary Neural Networks

Large-scale deep neural networks are both memory intensive and computation-intensive, thereby posing stringent requirements on the computing platforms. Hardware accelerations of deep neural networks have been extensively investigated in both industry and academia. Specific forms of binary neural networks (BNNs) and stochastic computing based neural networks (SCNNs) are particularly appealing to hardware implementations since they can be implemented almost entirely with binary operations. Despite the obvious advantages in hardware implementation, these approximate computing techniques are questioned by researchers in terms of accuracy and universal applicability. Also it is important to understand the relative pros and cons of SCNNs and BNNs in theory and in actual hardware implementations. In order to address these concerns, in this paper we prove that the "ideal" SCNNs and BNNs satisfy the universal approximation property with probability 1 (due to the stochastic behavior). The proof is conducted by first proving the property for SCNNs from the strong law of large numbers, and then using SCNNs as a "bridge" to prove for BNNs. Based on the universal approximation property, we further prove that SCNNs and BNNs exhibit the same energy complexity. In other words, they have the same asymptotic energy consumption with the growing of network size. We also provide a detailed analysis of the pros and cons of SCNNs and BNNs for hardware implementations and conclude that SCNNs are more suitable for hardware.Comment: 9 pages, 3 figure