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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 childrenâs 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 childrenâs family lives, we seek to go beyond any tendency to just âadd in the missing childrenâ 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 âtwo-componentâ 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
Africaâs 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. Africaâs conditional advantage is higher in low-tech than in high-tech manufacturing, and exists in manufacturing but not in services. The key factors explaining Africaâs disadvantage at the firm level are lack of infrastructure, access to finance, and political competition
Country-of-origin labelling, food traceability drivers and food fraud:lessons from consumersâ preferences and perceptions
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): 872â879, 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 Î14C values of â426 ± 6â° (~4,400 14C years) at the Polar Front and DOC Î14C values of â252 ± 22â° (~2,000 14C years) in the subtropics. At depth, Circumpolar Deep Waters held DOC Î14C values of â491 ± 13â° (~5,400 years), while values in Indian Deep Water were more depleted, holding DOC Î14C values of â503 ± 8â° (~5,600 14C years). High-salinity North Atlantic Deep Water intruding into the deep SIO had a distinctly less depleted DOC Î14C value of â481 ± 8â° (~5,100 14C years). We use multiple linear regression to assess the dynamics of DOC Î14C 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
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