Multi-scale structure, pasting and digestibility of adlay (Coixlachryma-jobi L.) seed starch

peer-reviewedThe hierarchical structure, pasting and digestibility of adlay seed starch (ASS) were investigated compared with maize starch (MS) and potato starch (PS). ASS exhibited round or polyglonal morphology with apparent pores/channels on the surface. It had a lower amylose content, a looser and more heterogeneous C-type crystalline structure, a higher crystallinity, and a thinner crystalline lamellae. Accordingly, ASS showed a higher slowly digestible starch content combined with less resistant starch fractions, and a decreased pasting temperature, a weakened tendency to retrogradation and an increased pasting stability compared with those of MS and PS. The ASS structure-functionality relationship indicated that the amylose content, double helical orders, crystalline lamellar structure, and surface pinholes should be responsible for ASS specific functionalities including pasting behaviors and in vitro digestibility. ASS showed potential applications in health-promoting foods which required low rearrangement during storage and sustainable energy-providing starch fractions

FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors

Face Super-Resolution (SR) is a domain-specific super-resolution problem. The specific facial prior knowledge could be leveraged for better super-resolving face images. We present a novel deep end-to-end trainable Face Super-Resolution Network (FSRNet), which makes full use of the geometry prior, i.e., facial landmark heatmaps and parsing maps, to super-resolve very low-resolution (LR) face images without well-aligned requirement. Specifically, we first construct a coarse SR network to recover a coarse high-resolution (HR) image. Then, the coarse HR image is sent to two branches: a fine SR encoder and a prior information estimation network, which extracts the image features, and estimates landmark heatmaps/parsing maps respectively. Both image features and prior information are sent to a fine SR decoder to recover the HR image. To further generate realistic faces, we propose the Face Super-Resolution Generative Adversarial Network (FSRGAN) to incorporate the adversarial loss into FSRNet. Moreover, we introduce two related tasks, face alignment and parsing, as the new evaluation metrics for face SR, which address the inconsistency of classic metrics w.r.t. visual perception. Extensive benchmark experiments show that FSRNet and FSRGAN significantly outperforms state of the arts for very LR face SR, both quantitatively and qualitatively. Code will be made available upon publication.Comment: Chen and Tai contributed equally to this pape

Load Transfer Coefficient of Transverse Cracks in Continuously Reinforced Concrete Pavements Using FRP Bars

Continuously reinforced concrete pavement (CRCP) with fiber reinforced polymer bars (FRP) shows several advantages over the conventional CRCP, such as no erosion, lightweight, and low modulus. And the load transfer performance of the transverse cracks affects its service life. As the traditional evaluation system of crack load transfer failed to eliminate the influence of pavement deflection, the simple deflection value ratios for the cracks of the load transfer coefficient are not accurate enough to evaluate the load transfer performance of transverse crack in FRP-CRCP. In order to further understand the effects and improve the current load transfer coefficient of transverse cracks in FRP-CRCP, the relationship between the deflection value and load transfer coefficient in FRP-CRCP was analyzed. Using this approach, a more accurate prediction equation has been put forward to express the load transfer capacity

Simplifying Multiproject Scheduling Problem Based on Design Structure Matrix and Its Solution by an Improved aiNet Algorithm

Managing multiple project is a complex task involving the unrelenting pressures of time and cost. Many studies have proposed various tools and techniques for single-project scheduling; however, the literature further considering multimode or multiproject issues occurring in the real world is rather scarce. In this paper, design structure matrix (DSM) and an improved artificial immune network algorithm (aiNet) are developed to solve a multi-mode resource-constrained scheduling problem. Firstly, the DSM is used to simplify the mathematic model of multi-project scheduling problem. Subsequently, aiNet algorithm comprised of clonal selection, negative selection, and network suppression is adopted to realize the local searching and global searching, which will assure that it has a powerful searching ability and also avoids the possible combinatorial explosion. Finally, the approach is tested on a set of randomly cases generated from ProGen. The computational results validate the effectiveness of the proposed algorithm comparing with other famous metaheuristic algorithms such as genetic algorithm (GA), simulated annealing algorithm (SA), and ant colony optimization (ACO)

Adversarial PoseNet: A Structure-aware Convolutional Network for Human Pose Estimation

For human pose estimation in monocular images, joint occlusions and overlapping upon human bodies often result in deviated pose predictions. Under these circumstances, biologically implausible pose predictions may be produced. In contrast, human vision is able to predict poses by exploiting geometric constraints of joint inter-connectivity. To address the problem by incorporating priors about the structure of human bodies, we propose a novel structure-aware convolutional network to implicitly take such priors into account during training of the deep network. Explicit learning of such constraints is typically challenging. Instead, we design discriminators to distinguish the real poses from the fake ones (such as biologically implausible ones). If the pose generator (G) generates results that the discriminator fails to distinguish from real ones, the network successfully learns the priors.Comment: Fixed typos. 14 pages. Demonstration videos are http://v.qq.com/x/page/c039862eira.html, http://v.qq.com/x/page/f0398zcvkl5.html, http://v.qq.com/x/page/w0398ei9m1r.htm