Productivity and the Decision to Export: Micro Evidence from Taiwan and South Korea

While there is widespread empirical evidence indicating exporting producers have higher productivity than nonexporters, the mechanisms that generate this pattern are less clear. One view is that exporters acquire knowledge of new production methods, inputs, and product designs from their international contacts, and this learning results in higher productivity for exporters relative to their more insulated domestic counterparts. Alternatively, the higher productivity of exporters may simply reflect the self-selection of more efficient producers into a highly competitive export market. In this paper we use micro data collected in the manufacturing censuses in South Korea and Taiwan to study the linkages between a producer's total factor productivity and choice to participate in the export market. We find differences between the countries in the importance of selection and learning forces. In Taiwan, transitions of firms in and out of the export market reflect systematic variations in productivity as predicted by self-selection models. Firms with higher productivity, ex ante, tend to enter the export market and exporters with low productivity tend to exit. Moreover, in several industries, entry into the export market is followed by relative productivity improvements, a result consistent with learning-by-exporting forces. In South Korea, the evidence of self-selection on the basis of productivity is much weaker. In addition, unlike Taiwan, we find no significant productivity changes following entry or exit from the export market that are consistent with learning from exporting. Comparison of the two countries suggests that in Korea factors other than production efficiency play a more prominent role as determinants determinants of the export decision.

Structural network efficiency is associated with cognitive impairment in small-vessel disease.

To characterize brain network connectivity impairment in cerebral small-vessel disease (SVD) and its relationship with MRI disease markers and cognitive impairment.METHODS: A cross-sectional design applied graph-based efficiency analysis to deterministic diffusion tensor tractography data from 115 patients with lacunar infarction and leukoaraiosis and 50 healthy individuals. Structural connectivity was estimated between 90 cortical and subcortical brain regions and efficiency measures of resulting graphs were analyzed. Networks were compared between SVD and control groups, and associations between efficiency measures, conventional MRI disease markers, and cognitive function were tested.RESULTS: Brain diffusion tensor tractography network connectivity was significantly reduced in SVD: networks were less dense, connection weights were lower, and measures of network efficiency were significantly disrupted. The degree of brain network disruption was associated with MRI measures of disease severity and cognitive function. In multiple regression models controlling for confounding variables, associations with cognition were stronger for network measures than other MRI measures including conventional diffusion tensor imaging measures. A total mediation effect was observed for the association between fractional anisotropy and mean diffusivity measures and executive function and processing speed.CONCLUSIONS: Brain network connectivity in SVD is disturbed, this disturbance is related to disease severity, and within a mediation framework fully or partly explains previously observed associations between MRI measures and SVD-related cognitive dysfunction. These cross-sectional results highlight the importance of network disruption in SVD and provide support for network measures as a disease marker in treatment studies

Productivity, output, and failure

노트 : The views expressed herein are those of the authors and not necessarily those of the National Bureau of Economic Research

Field theories of paramagnetic Mott insulators

This is a summary of a central argument in recent review articles by the author (cond-mat/0109419, cond-mat/0211005, and cond-mat/0211027). An effective field theory is derived for the low energy spin singlet excitations in a paramagnetic Mott insulator with collinear spin correlations.Comment: 12 pages, 4 figures, Proceedings of the International Conference on Theoretical Physics, Paris, UNESCO, July 200

Classifying HCP task-fMRI networks using heat kernels

Network theory provides a principled abstraction of the human brain: reducing a complex system into a simpler representation from which to investigate brain organisation. Recent advancement in the neuroimaging field are towards representing brain connectivity as a dynamic process in order to gain a deeper understanding of the interplay between functional modules for efficient information transport. In this work, we employ heat kernels to model the process of energy diffusion in functional networks. We extract node-based, multi-scale features which describe the propagation of heat over 'time' which not only inform the importance of a node in the graph, but also incorporate local and global information of the underlying geometry of the network. As a proof-of-concept, we test the efficacy of two heat kernel features for discriminating between motor and working memory functional networks from the Human Connectome Project. For comparison, we also classified task networks using traditional network metrics which similarly provide rankings of node importance. In addition, a variant of the Smooth Incremental Graphical Lasso Estimation algorithm was used to estimate non-sparse, precision matrices to account for non-stationarity in the time series. We illustrate differences in heat kernel features between tasks, and also between regions of the brain. Using a random forest classifier, we showed heat kernel metrics to capture intrinsic properties of functional networks that serve well as features for task classification

Structural subnetwork evolution across the life-span: rich-club, feeder, seeder

The impact of developmental and aging processes on brain connectivity and the connectome has been widely studied. Network theoretical measures and certain topological principles are computed from the entire brain, however there is a need to separate and understand the underlying subnetworks which contribute towards these observed holistic connectomic alterations. One organizational principle is the rich-club - a core subnetwork of brain regions that are strongly connected, forming a high-cost, high-capacity backbone that is critical for effective communication in the network. Investigations primarily focus on its alterations with disease and age. Here, we present a systematic analysis of not only the rich-club, but also other subnetworks derived from this backbone - namely feeder and seeder subnetworks. Our analysis is applied to structural connectomes in a normal cohort from a large, publicly available lifespan study. We demonstrate changes in rich-club membership with age alongside a shift in importance from 'peripheral' seeder to feeder subnetworks. Our results show a refinement within the rich-club structure (increase in transitivity and betweenness centrality), as well as increased efficiency in the feeder subnetwork and decreased measures of network integration and segregation in the seeder subnetwork. These results demonstrate the different developmental patterns when analyzing the connectome stratified according to its rich-club and the potential of utilizing this subnetwork analysis to reveal the evolution of brain architectural alterations across the life-span

Cosmic Ray Anomalies from the MSSM?

The recent positron excess in cosmic rays (CR) observed by the PAMELA satellite may be a signal for dark matter (DM) annihilation. When these measurements are combined with those from FERMI on the total ($e^++e^-$) flux and from PAMELA itself on the $\bar p/p$ ratio, these and other results are difficult to reconcile with traditional models of DM, including the conventional mSUGRA version of Supersymmetry even if boosts as large as $10^{3-4}$ are allowed. In this paper, we combine the results of a previously obtained scan over a more general 19-parameter subspace of the MSSM with a corresponding scan over astrophysical parameters that describe the propagation of CR. We then ascertain whether or not a good fit to this CR data can be obtained with relatively small boost factors while simultaneously satisfying the additional constraints arising from gamma ray data. We find that a specific subclass of MSSM models where the LSP is mostly pure bino and annihilates almost exclusively into $\tau$ pairs comes very close to satisfying these requirements. The lightest $\tilde \tau$ in this set of models is found to be relatively close in mass to the LSP and is in some cases the nLSP. These models lead to a significant improvement in the overall fit to the data by an amount $\Delta \chi^2 \sim 1/$dof in comparison to the best fit without Supersymmetry while employing boosts $\sim 100$. The implications of these models for future experiments are discussed.Comment: 57 pages, 31 figures, references adde

Spheroid arrays for high-throughput single-cell analysis of spatial patterns and biomarker expression in 3D

We describe and share a device, methodology and image analysis algorithms, which allow up to 66 spheroids to be arranged into a gel-based array directly from a culture plate for downstream processing and analysis. Compared to processing individual samples, the technique uses 11-fold less reagents, saves time and enables automated imaging. To illustrate the power of the technology, we showcase applications of the methodology for investigating 3D spheroid morphology and marker expression and for in vitro safety and efficacy screens. Firstly, spheroid arrays of 11 cell-lines were rapidly assessed for differences in spheroid morphology. Secondly, highly-positive (SOX-2), moderately-positive (Ki-67) and weakly-positive (βIII-tubulin) protein targets were detected and quantified. Third, the arrays enabled screening of ten media compositions for inducing differentiation in human neurospheres. Lastly, the application of spheroid microarrays for spheroid-based drug-screens was demonstrated by quantifying the dose-dependent drop in proliferation and increase in differentiation in etoposide-treated neurospheres