Regional Output Spillovers in China: Estimates from a VAR Model

Interregional spillover effects are central to China’s growth policy; yet relatively little is known about the strength and duration of these spillovers and whether their characteristics have changed over time. This paper examines the spillover of output between the three commonly-used regions of China: coastal, central and western regions. We find that there are strong spillovers from the coastal region to both other regions, from the central region to the western region but that shocks to the western region have no flow-on effect for the other two regions. Thus a policy of developing the coastal region is likely to indirectly benefit the other two regions.Regional Spillovers, China, regional growth

Inter-Regional Spillovers in China: The Importance of Common Shocks and the Definition of Regions

This paper examines the question of inter-regional spillovers in China. We argue that this is a central question in Chinese economic policy, given the marked regional disparities that exist and the concern of policy-makers to ameliorate them. We analyse this question within the framework of a six-region vectorautoregressive model which we subject to extensive sensitivity analysis, with particular attention paid to the effects on the results of strong common output movements. We find the results of dynamic simulations to be importantly dependent on model specification; in particular, they are sensitive to the order in which the variables enter the model. After an assessment of various alternatives, we are able to specify a model with tolerable robustness by using data which has been purged of the effects of national output fluctuations. We find some expected but also some unexpected results. In the first category, the Yellow River and Changjiang River regions are found to have spillover effects on other regions although they are more extensive for the former; the South Western region has no significant spillover effects on the rest of the country, consistently with the results of previous research. However, in contrast both to other research and to our expectations, shocks to the South Eastern region affect mainly the region itself with little spillover to the other regions. The same is true of the North East region while the North West region has extensive spillovers to other regions. We conclude that there is still much to be learned about the magnitude and timing of inter-regional spillovers before firm policy conclusions can be drawn.

Inter-Regional Output Spillovers in China: Disentangling National from Regional Shocks

This paper reports an investigation of the spillover effects of output shocks between regions in China. We use a six-region classification first suggested about two decades ago which still captures relatively homogeneous regions. The six regions are: South East, Changjiang River, Yellow River, North East, South West and North West. We start from a recent paper by Groenewold, Lee and Chen (2005b) which uses the same six regions and a vector autoregressive (VAR) framework. They find that the spillover effects are crucially dependent on the order of the variables in the model and argue that this is due to common national influences. They overcome the “ordering problem” by purging the regional outputs of their common national components using a preliminary regression of regional outputs on national output. We implement an alternative solution to the ordering problem which does not involve this two-step procedure. We proceed by including national output directly into our model. Moreover, we extend their analysis by investigating Granger causality between regional and national output measures as well as block exogeneity. Our results confirm important conclusions of the earlier paper but also raise some interesting differences.

Transcribing Content from Structural Images with Spotlight Mechanism

Transcribing content from structural images, e.g., writing notes from music scores, is a challenging task as not only the content objects should be recognized, but the internal structure should also be preserved. Existing image recognition methods mainly work on images with simple content (e.g., text lines with characters), but are not capable to identify ones with more complex content (e.g., structured symbols), which often follow a fine-grained grammar. To this end, in this paper, we propose a hierarchical Spotlight Transcribing Network (STN) framework followed by a two-stage "where-to-what" solution. Specifically, we first decide "where-to-look" through a novel spotlight mechanism to focus on different areas of the original image following its structure. Then, we decide "what-to-write" by developing a GRU based network with the spotlight areas for transcribing the content accordingly. Moreover, we propose two implementations on the basis of STN, i.e., STNM and STNR, where the spotlight movement follows the Markov property and Recurrent modeling, respectively. We also design a reinforcement method to refine the framework by self-improving the spotlight mechanism. We conduct extensive experiments on many structural image datasets, where the results clearly demonstrate the effectiveness of STN framework.Comment: Accepted by KDD2018 Research Track. In proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'18

A viral strategy for targeting and manipulating interneurons across vertebrate species

A fundamental impediment to understanding the brain is the availability of inexpensive and robust methods for targeting and manipulating specific neuronal populations. The need to overcome this barrier is pressing because there are considerable anatomical, physiological, cognitive and behavioral differences between mice and higher mammalian species in which it is difficult to specifically target and manipulate genetically defined functional cell types. In particular, it is unclear the degree to which insights from mouse models can shed light on the neural mechanisms that mediate cognitive functions in higher species, including humans. Here we describe a novel recombinant adeno-associated virus that restricts gene expression to GABAergic interneurons within the telencephalon. We demonstrate that the viral expression is specific and robust, allowing for morphological visualization, activity monitoring and functional manipulation of interneurons in both mice and non-genetically tractable species, thus opening the possibility to study GABAergic function in virtually any vertebrate species.National Institutes of Health (U.S.) (Grant MH071679)National Institutes of Health (U.S.) (Grant NS08297)National Institutes of Health (U.S.) (Grant NS074972)National Institutes of Health (U.S.) (Grant MH095147)National Institutes of Health (U.S.) (Grant MH066912)National Institutes of Health (U.S.) (Grant EY022577)National Institutes of Health (U.S.) (Grant MH063912