Childhood, children and family lives in China

In this chapter we bring into focus those aspects of family lives in China that are concerned with children’s family relationships, and the ways in which such issues are part and parcel of the broader institutionalisation of childhood. We draw on theoretical frameworks in the sociology of childhood and childhood studies (e.g., Prout, 2004; Qvortrup, 2000; Smith and Greene, 2014). Since these theoretical perspectives have developed predominantly in Anglophone literature, some researchers have considered their relevance to, and utility for, China and Chinese childhoods (Goh, 2011; Miao, 2013; Wang YY, 2011, 2014a, 2014b; Zheng, 2012a, 2012b; Ribbens McCarthy et al., 2017). In engaging with existing theories, and applying them to, Chinese children’s family lives, we seek to go beyond any tendency to just ‘add in the missing children’ to existing discussions (Kesby et al., 2006: 186), and give consideration to a variety of cultural and local contexts that characterise China and illuminate why it is necessary to decentre universalist thinking (Jullien, 2008/2014

SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection

Vision-based vehicle detection approaches achieve incredible success in recent years with the development of deep convolutional neural network (CNN). However, existing CNN based algorithms suffer from the problem that the convolutional features are scale-sensitive in object detection task but it is common that traffic images and videos contain vehicles with a large variance of scales. In this paper, we delve into the source of scale sensitivity, and reveal two key issues: 1) existing RoI pooling destroys the structure of small scale objects, 2) the large intra-class distance for a large variance of scales exceeds the representation capability of a single network. Based on these findings, we present a scale-insensitive convolutional neural network (SINet) for fast detecting vehicles with a large variance of scales. First, we present a context-aware RoI pooling to maintain the contextual information and original structure of small scale objects. Second, we present a multi-branch decision network to minimize the intra-class distance of features. These lightweight techniques bring zero extra time complexity but prominent detection accuracy improvement. The proposed techniques can be equipped with any deep network architectures and keep them trained end-to-end. Our SINet achieves state-of-the-art performance in terms of accuracy and speed (up to 37 FPS) on the KITTI benchmark and a new highway dataset, which contains a large variance of scales and extremely small objects.Comment: Accepted by IEEE Transactions on Intelligent Transportation Systems (T-ITS

The Yeast YPD1/SLN1 Complex Insights into Molecular Recognition in Two-Component Signaling Systems

AbstractIn Saccharomyces cerevisiae, a branched multistep phosphorelay signaling pathway regulates cellular adaptation to hyperosmotic stress. YPD1 functions as a histidine-phosphorylated protein intermediate required for phosphoryl group transfer from a membrane-bound sensor histidine kinase (SLN1) to two distinct response regulator proteins (SSK1 and SKN7). These four proteins are evolutionarily related to the well-characterized “two-component” regulatory proteins from bacteria. Although structural information is available for many two-component signaling proteins, there are very few examples of complexes between interacting phosphorelay partners. Here we report the first crystal structure of a prototypical monomeric histidine-containing phosphotransfer (HPt) protein YPD1 in complex with its upstream phosphodonor, the response regulator domain associated with SLN1

Explaining Africa\u27s (Dis)Advantage

Africa’s economic performance has been widely viewed with pessimism. In this paper, firm-level data for around 80 countries are used to examine formal firm performance. Without controls, manufacturing African firms perform significantly worse than firms in other regions. They have lower productivity levels and growth rates, export less, and have lower investment rates. Once geography, political competition, and the business environment are controlled for, formal African firms lead in productivity levels and growth. Africa’s conditional advantage is higher in low-tech than in high-tech manufacturing, and exists in manufacturing but not in services. The key factors explaining Africa’s disadvantage at the firm level are lack of infrastructure, access to finance, and political competition

Radiocarbon content of dissolved organic carbon in the South Indian Ocean

Author Posting. © American Geophysical Union, 2018. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 45 (2018): 872–879, doi:10.1002/2017GL076295.We report four profiles of the radiocarbon content of dissolved organic carbon (DOC) spanning the South Indian Ocean (SIO), ranging from the Polar Front (56°S) to the subtropics (29°S). Surface waters held mean DOC Δ14C values of −426 ± 6‰ (~4,400 14C years) at the Polar Front and DOC Δ14C values of −252 ± 22‰ (~2,000 14C years) in the subtropics. At depth, Circumpolar Deep Waters held DOC Δ14C values of −491 ± 13‰ (~5,400 years), while values in Indian Deep Water were more depleted, holding DOC Δ14C values of −503 ± 8‰ (~5,600 14C years). High-salinity North Atlantic Deep Water intruding into the deep SIO had a distinctly less depleted DOC Δ14C value of −481 ± 8‰ (~5,100 14C years). We use multiple linear regression to assess the dynamics of DOC Δ14C values in the deep Indian Ocean, finding that their distribution is characteristic of water masses in that region.National Science Foundation (NSF) Grant Numbers: OPP-1142117, OCE-14367482018-07-2