Stock market uncertainty and the relation between stock and bond returns

The authors examine how the co-movement between daily stock and Treasury bond returns varies with stock market uncertainty. They use the lagged implied volatility from equity index options to provide an objective, observable, and dynamic measure of stock market uncertainty. The authors find that stock and bond returns tend to move substantially together during periods of lower stock market uncertainty. However, stock and bond returns tend to exhibit little relation or even a negative relation during periods of high stock market uncertainty. The authors’ findings have implications for understanding joint cross-market price formation. Further, their findings imply that diversification benefits increase for portfolios of stocks and bonds during periods of high stock market uncertainty.Stock market ; Stocks ; Bonds

Thickness effects on fibre-bridged fatigue delamination growth in composites

This paper provides an investigation on thickness effects on fibre-bridged fatigue delamination growth (FDG) in composite laminates. A modified Paris relation was employed to interpret experimental fatigue data. The results clearly demonstrated that both thickness and fibre bridging had negligible effects on FDG behaviors. Both energy principles and fractography analysis were subsequently performed to explore the physical reasons of this independence. It was found that the amount of energy release of a given crack growth was not only independent of fibre bridging, but also thickness. Fibre print was the dominant microscopic feature located on fracture surfaces, physically making the same energy dissipation during FDG. Furthermore, the present study provides extra evidence on the importance of using an appropriate similitude parameter in FDG studies. Particularly, the strain energy release rate (SERR) range applied around crack front was demonstrated as an appropriate similitude parameter for fibre-bridged FDG study

Octa­carbon­yl(5-meth­oxy-2,3-dihydro-1H-benzimidazol-2-yl)di-μ3-sulfido-diiron(I)iron(II)(2 Fe—Fe)

The title compound, [Fe3(C8H8N2O)S2(CO)8], was prepared by the direct reaction of Fe3(CO)12 and 5-meth­oxy-1H-benzoimidazole-2-thiol in tetra­hydro­furan. Desulfurization took place readily to form a sulfide carbonyl cluster. The mol­ecule contains a triangle consisting of three Fe atoms capped by two S atoms above and below. There are two Fe—Fe bonds [2.6322 (5) and 2.5582 (5) Å] in the triangle; the length of the third edge [3.3987 (5) Å] is too long to represent an Fe—Fe bond

MOEA/D with Adaptive Weight Adjustment

Recently, MOEA/D (multi-objective evolutionary algorithm based on decomposition) has achieved great success in the field of evolutionary multi-objective optimization and has attracted a lot of attention. It decomposes a multi-objective optimization problem (MOP) into a set of scalar subproblems using uniformly distributed aggregation weight vectors and provides an excellent general algorithmic framework of evolutionary multi-objective optimization. Generally, the uniformity of weight vectors in MOEA/D can ensure the diversity of the Pareto optimal solutions, however, it cannot work as well when the target MOP has a complex Pareto front (PF; i.e., discontinuous PF or PF with sharp peak or low tail). To remedy this, we propose an improved MOEA/D with adaptive weight vector adjustment (MOEA/D-AWA). According to the analysis of the geometric relationship between the weight vectors and the optimal solutions under the Chebyshev decomposition scheme, a new weight vector initialization method and an adaptive weight vector adjustment strategy are introduced in MOEA/D-AWA. The weights are adjusted periodically so that the weights of subproblems can be redistributed adaptively to obtain better uniformity of solutions. Meanwhile, computing efforts devoted to subproblems with duplicate optimal solution can be saved. Moreover, an external elite population is introduced to help adding new subproblems into real sparse regions rather than pseudo sparse regions of the complex PF, that is, discontinuous regions of the PF. MOEA/D-AWA has been compared with four state of the art MOEAs, namely the original MOEA/D, Adaptive-MOEA/D, [Formula: see text]-MOEA/D, and NSGA-II on 10 widely used test problems, two newly constructed complex problems, and two many-objective problems. Experimental results indicate that MOEA/D-AWA outperforms the benchmark algorithms in terms of the IGD metric, particularly when the PF of the MOP is complex.</jats:p

Change detection in SAR images based on the salient map guidance and an accelerated genetic algorithm

This paper proposes a change detection algorithm in synthetic aperture radar (SAR) images based on the salient image guidance and an accelerated genetic algorithm (S-aGA). The difference image is first generated by logarithm ratio operator based on the bi-temporal SAR images acquired in the same region. Then a saliency detection model is applied in the difference image to extract the salient regions containing the changed class pixels. The salient regions are further divided by fuzzy c-means (FCM) clustering algorithm into three categories: changed class (set of pixels with high gray values), unchanged class (set of pixels with low gray values) and undetermined class (set of pixels with middle gray value, which are difficult to classify). Finally, the proposed accelerated GA is applied to explore the reduced search space formed by the undetermined-class pixels according to an objective function considering neighborhood information. In S-aGA, an efficient mutation operator is designed by using the neighborhood information of undetermined-class pixels as the heuristic information to determine the mutation probability of each undetermined-class pixel adaptively, which accelerates the convergence of the GA significantly. The experimental results on two data sets demonstrate the efficiency of the proposed S-aGA. On the whole, S-aGA outperforms five other existing methods including the simple GA in terms of detection accuracy. In addition, S-aGA could obtain satisfying solution within limited generations, converging much faster than the simple GA

CP-consensus: a Blockchain Protocol Based on Synchronous Timestamps of Compass Satellite

Bitcoin, the first decentralized cryptocurrency, achieves great success but also encounters many challenges. In this paper, we mainly focus on Bitcoin\u27s five challenges: low network synchronization; poor throughput; high information propagation delay; vulnerabilities to fork-based attacks and consumption of a large amount of computational power to maintain the blockchain. To address these challenges, we present the CP-consensus, a blockchain protocol based on synchronous timestamps of the Compass satellite. Firstly, CP-consensus provides a quasi-synchronous network for nodes. Specifically, nodes synchronously begin or end in each phase. Secondly, the block propagation delay is significantly reduced by adopting cache-nodes. Moreover, the block verification delay is significantly reduced since it is limited only by the size of block-header. Thirdly, CP-consensus has a high throughput with a larger block size since that the block size does not influence the consistency of CP-consensus. Fourthly, CP-consensus resists fork-based attacks and consumes a small amount of computational power. Finally, parameters setting and the security of CP-consensus are discussed

Semi-supervised Complex-valued GAN for Polarimetric SAR Image Classification

Polarimetric synthetic aperture radar (PolSAR) images are widely used in disaster detection and military reconnaissance and so on. However, their interpretation faces some challenges, e.g., deficiency of labeled data, inadequate utilization of data information and so on. In this paper, a complex-valued generative adversarial network (GAN) is proposed for the first time to address these issues. The complex number form of model complies with the physical mechanism of PolSAR data and in favor of utilizing and retaining amplitude and phase information of PolSAR data. GAN architecture and semi-supervised learning are combined to handle deficiency of labeled data. GAN expands training data and semi-supervised learning is used to train network with generated, labeled and unlabeled data. Experimental results on two benchmark data sets show that our model outperforms existing state-of-the-art models, especially for conditions with fewer labeled data