Semantic Image Segmentation via Deep Parsing Network

This paper addresses semantic image segmentation by incorporating rich information into Markov Random Field (MRF), including high-order relations and mixture of label contexts. Unlike previous works that optimized MRFs using iterative algorithm, we solve MRF by proposing a Convolutional Neural Network (CNN), namely Deep Parsing Network (DPN), which enables deterministic end-to-end computation in a single forward pass. Specifically, DPN extends a contemporary CNN architecture to model unary terms and additional layers are carefully devised to approximate the mean field algorithm (MF) for pairwise terms. It has several appealing properties. First, different from the recent works that combined CNN and MRF, where many iterations of MF were required for each training image during back-propagation, DPN is able to achieve high performance by approximating one iteration of MF. Second, DPN represents various types of pairwise terms, making many existing works as its special cases. Third, DPN makes MF easier to be parallelized and speeded up in Graphical Processing Unit (GPU). DPN is thoroughly evaluated on the PASCAL VOC 2012 dataset, where a single DPN model yields a new state-of-the-art segmentation accuracy.Comment: To appear in International Conference on Computer Vision (ICCV) 201

A Forward-Looking Factor Model for Volatility: Estimation and Implications for Predicting Disasters

We show that any factor structure for stock returns can be naturally translated into a factor structure for return volatility. We use this structure to propose a methodology for estimating forward-looking variances and covariances of both factors and individual assets from option prices at a high frequency. We implement the model empirically and show that our forward-looking volatility estimates provide useful predictions of rare disasters for both factors and individual stocks

Mechanical behaviours and mass transport properties of bone-mimicking scaffolds consisted of gyroid structures manufactured using selective laser melting

Bone scaffolds created in porous structures manufactured using selective laser melting (SLM) are widely used in tissue engineering, since the elastic moduli of the scaffolds are easily adjusted according to the moduli of the tissues, and the large surfaces the scaffolds provide are beneficial to cell growth. SLM-built gyroid structures composed of 316L stainless steel have demonstrated superior properties such as good corrosion resistance, strong biocompatibility, self-supported performance, and excellent mechanical properties. In this study, gyroid structures of different volume fraction were modelled and manufactured using SLM; the mechanical properties of the structures were then investigated under quasi-static compression loads. The elastic moduli and yield stresses of the structures were calculated from stress-strain diagrams, which were developed by conducting quasi-static compression tests. In order to estimate the discrepancies between the designed and as-produced gyroid structures, optical microscopy and micro-CT scanner were used to observe the structures’ micromorphology. Since good fluidness is conducive to the transport of nutrients, computational fluid dynamics (CFD) values were used to investigate the pressure and flow velocity of the channel of the three kinds of gyroid structures. The results show that the sizes of the as-produced structures were larger than their computer aided design (CAD) sizes, but the manufacturing errors are within a relatively stable range. The elastic moduli and yield stresses of the structures improved as their volume fractions increased. Gyroid structure can match the mechanical properties of human bone by changing the porosity of scaffold. The process of compression failure showed that 316L gyroid structures manufactured using SLM demonstrated high degrees of toughness. The results obtained from CFD simulation showed that gyroid structures have good fluidity, which has an accelerated effect on the fluid in the middle of the channel, and it is suitable for transport nutrients. Therefore, we could predict the scaffold's permeability by conducting CFD simulation to ensure an appropriate permeability before the scaffold being manufactured. SLM-built gyroid structures that composed of 316L stainless steel were suitable to be designed as bone scaffolds in terms of mechanical properties and mass-transport properties, and had significant promise