Strain-hardening (the increase of flow stress with plastic strain) is the most important phenomenon in the mechanical behaviour of engineering alloys because it ensures that flow is delocalized, enhances tensile ductility and inhibits catastrophic mechanical failure 1,2. Metallic glasses (MGs) lack the crystallinity of conventional engineering alloys, and some of their properties-such as higher yield stress and elastic strain limit 3-are greatly improved relative to their crystalline counterparts. MGs can have high fracture toughness and have the highest known 'damage tolerance' (defined as the product of yield stress and fracture toughness) 4 among all structural materials. However, the promise of MGs for structural applications is largely thwarted by the fact that they show strain-softening, instead of strain-hardening; this leads to extreme localization of plastic flow in shear bands, and is associated with early catastrophic failure in tension. Although rejuvenation of an MG (raising its energy to values that are typical of glass formation at a higher cooling rate) lowers its yield stress, which might enable strain-hardening 5 , it is unclear whether sufficient rejuvenation can be achieved in bulk samples while retaining their glassy structure. Here we show that plastic deformation under triaxial compression at room temperature can rejuvenate bulk MG samples sufficiently to enable strain-hardening through a mechanism that has not been previously observed in the metallic state. This transformed behaviour suppresses shear-banding in bulk samples in normal uniaxial (tensile or compressive) tests,

Since 1979, China has made tremendous progress in its transformation to a socialist market economy. As part of this process, China's financial system has evolved to one characterised by a high degree of marketization. At the same time, China today faces new challenges to growth and development, particularly from the necessity of restructuring its economy to focus increasingly on innovation and away from government led investment and low wage labour. In the context of digital financial services, China has been a late mover but this has changed dramatically in the past five years, to the point today where China is one of the major centres for digital financial services and financial technology ("fintech"). Looking forward, China needs to provide an appropriate regulatory basis for the future development of digital financial services and fintech, balancing growth and innovation with financial stability. China today is exhibiting signs of a last mover advantage in this respect that may see it leaping regulatory developments elsewhere.

Fabric defect detection is very important in the textile quality process. Current deep learning algorithms are not effective in detecting tiny and extreme aspect ratio fabric defects. In this paper, we proposed a strong detection method, Priori Anchor Convolutional Neural Network (PRAN-Net), for fabric defect detection to improve the detection and location accuracy of fabric defects and decrease the inspection time. First, we used Feature Pyramid Network (FPN) by selected multi-scale feature maps to reserve more detailed information of tiny defects. Secondly, we proposed a trick to generate sparse priori anchors based on fabric defects ground truth boxes instead of fixed anchors to locate extreme defects more accurately and efficiently. Finally, a classification network is used to classify and refine the position of the fabric defects. The method was validated on two self-made fabric datasets. Experimental results indicate that our method significantly improved the accuracy and efficiency of detecting fabric defects and is more suitable to the automatic fabric defect detection.