Master plan : Greenport Shanghai Agropark

Greenport Shanghai is the innovative and ambitious exploration of how Chinese metropolitan agriculture will jump into the 21st century: circular, sustainable and profitable

Efficient Multi-Template Learning for Structured Prediction

Conditional random field (CRF) and Structural Support Vector Machine (Structural SVM) are two state-of-the-art methods for structured prediction which captures the interdependencies among output variables. The success of these methods is attributed to the fact that their discriminative models are able to account for overlapping features on the whole input observations. These features are usually generated by applying a given set of templates on labeled data, but improper templates may lead to degraded performance. To alleviate this issue, in this paper, we propose a novel multiple template learning paradigm to learn structured prediction and the importance of each template simultaneously, so that hundreds of arbitrary templates could be added into the learning model without caution. This paradigm can be formulated as a special multiple kernel learning problem with exponential number of constraints. Then we introduce an efficient cutting plane algorithm to solve this problem in the primal, and its convergence is presented. We also evaluate the proposed learning paradigm on two widely-studied structured prediction tasks, \emph{i.e.} sequence labeling and dependency parsing. Extensive experimental results show that the proposed method outperforms CRFs and Structural SVMs due to exploiting the importance of each template. Our complexity analysis and empirical results also show that our proposed method is more efficient than OnlineMKL on very sparse and high-dimensional data. We further extend this paradigm for structured prediction using generalized $p$-block norm regularization with $p>1$, and experiments show competitive performances when $p \in [1,2)$

Commodities and Linkages: Industrialisation in Sub-Saharan Africa

In a complementary Discussion Paper (MMCP DP 12 2011) we set out the reasons why we believe that there is extensive scope for linkage development into and out of SSA’s commodities sectors. In this Discussion Paper, we present the findings of our detailed empirical enquiry into the determinants of the breadth and depth of linkages in eight SSA countries (Angola, Botswana, Gabon, Ghana, Nigeria, South Africa Tanzania, and Zambia) and six sectors (copper, diamonds, gold, oil and gas, mining services and timber). We conclude from this detailed research that the extent of linkages varies as a consequence of four factors which intrinsically affect their progress – the passage of time, the complexity of the sector and the level of capabilities in the domestic economy. However, beyond this we identify three sets of related factors which determined the nature and pace of linkage development. The first is the structure of ownership, both in lead commodity producing firms and in their suppliers and domestic customers. The second is the nature and quality of both hard infrastructure (for example, roads and ports) and soft infrastructure (for example, the efficiency of customs clearance). The third is the availability of skills and the structure and orientation of the National System of Innovation in the domestic economy. The fourth, and overwhelmingly important contextual factor is policy. This reflects policy towards the commodity sector itself, and policy which affects the three contextual drivers, namely ownership, infrastructure and capabilities. As a result of this comparative analysis we provided an explanation of why linkage development was progressive in some economies (such as Botswana) and regressive in others (such as Tanzania). This cluster of factors also explains why the breadth and depth of linkages is relative advanced in some countries (such as South Africa), and at a very nascent stage in other countries (such as Angola)