Bayesian Modeling of School Effects Using Hierarchical Models with Smoothing Priors

We describe a new and flexible framework for modeling school effects. Like previous work in this area, we introduce an empirical model that evaluates school performance on the basis of student level test-score gains. Unlike previous work, however, we introduce a flexible model that relates follow-up student test scores to baseline student test scores and explore for possible nonlinearities in these relationships. Using data from High School and Beyond (HSB) and adapting the methodology described in Koop and Poirier (2004a), we test and reject the use of specifications that have been frequently used in research and as a basis for policy. We find that nonlinearities are important in the relationship between intake and follow-up achievement, that rankings of schools are sensitive to the model employed, and importantly, that commonly used specifications can give different and potentially misleading assessments of school performance. When estimating our preferred semiparametric specification, we find small but ``significant'' impacts of some school quality proxies (such as district-level expenditure per pupil) in the production of student achievement.

Bayesian Analysis of Structural Effects in an Ordered Equation System

We describe a new simulation-based algorithm for Bayesian estimation of structural effects in models where the outcome of interest and an endogenous treatment variable are ordered. Our algorithm makes use of a reparameterization, suggested by Nandram and Chen (1996) in the context of a single equation ordered-probit model, which significantly improves the mixing of the standard Gibbs sampler. We illustrate the improvements afforded by this new algorithm in a generated data experiment and also make use of our methods in an empirical application. Specifically, we take data from the National Longitudinal Survey of Youth (NLSY) and investigate the impact of maternal alcohol consumption on early infant health. Our results show clear evidence that the health outcomes of infants whose mothers drink while pregnant are worse than the outcomes of infants whose mothers never consumed alcohol while pregnant. In addition, the estimated parameters clearly suggest the need to control for the endogeneity of maternal alcohol consumption.

Building Core Competencies in a Turbulent Environment: An Exploratory Study of Firm Resources and Capabilities in Chinese Transitional Economy

The impact of firm resources and capabilities on performance among Chinese enterprises has been a subject of anecdotal speculation due to its significant implications for organizational researchers as well as practitioners. Yet empirical evidence has been very limited. In this research, set in the People’s Republic of China, we examine firm resources and capabilities and their impact on firm performance among Chinese enterprises. Using large sample of 12,047 Chinese firms from 1991 to 1992, we found that most of the firm specific resources and capabilities examined in this study have a positive impact on performance. We discussed the implications for organizational researchers, policy makers, and managers, and proposed directions for future research.economic transition, firm strategy, resources and capabilities

Plasmon geometric phase and plasmon Hall shift

The collective plasmonic modes of a metal comprise a pattern of charge density and tightly-bound electric fields that oscillate in lock-step to yield enhanced light-matter interaction. Here we show that metals with non-zero Hall conductivity host plasmons with a fine internal structure: they are characterized by a current density configuration that sharply departs from that of ordinary zero Hall conductivity metals. This non-trivial internal structure dramatically enriches the dynamics of plasmon propagation, enabling plasmon wavepackets to acquire geometric phases as they scatter. Strikingly, at boundaries these phases accumulate allowing plasmon waves that reflect off to experience a non-reciprocal parallel shift along the boundary displacing the incident and reflected plasmon trajectories. This plasmon Hall shift, tunable by Hall conductivity as well as plasmon wavelength, displays the chirality of the plasmon's current distribution and can be probed by near-field photonics techniques. Anomalous plasmon dynamics provide a real-space window into the inner structure of plasmon bands, as well as new means for directing plasmonic beams

Control and Synchronization of Neuron Ensembles

Synchronization of oscillations is a phenomenon prevalent in natural, social, and engineering systems. Controlling synchronization of oscillating systems is motivated by a wide range of applications from neurological treatment of Parkinson's disease to the design of neurocomputers. In this article, we study the control of an ensemble of uncoupled neuron oscillators described by phase models. We examine controllability of such a neuron ensemble for various phase models and, furthermore, study the related optimal control problems. In particular, by employing Pontryagin's maximum principle, we analytically derive optimal controls for spiking single- and two-neuron systems, and analyze the applicability of the latter to an ensemble system. Finally, we present a robust computational method for optimal control of spiking neurons based on pseudospectral approximations. The methodology developed here is universal to the control of general nonlinear phase oscillators.Comment: 29 pages, 6 figure

Love Thy Neighbors: Image Annotation by Exploiting Image Metadata

Some images that are difficult to recognize on their own may become more clear in the context of a neighborhood of related images with similar social-network metadata. We build on this intuition to improve multilabel image annotation. Our model uses image metadata nonparametrically to generate neighborhoods of related images using Jaccard similarities, then uses a deep neural network to blend visual information from the image and its neighbors. Prior work typically models image metadata parametrically, in contrast, our nonparametric treatment allows our model to perform well even when the vocabulary of metadata changes between training and testing. We perform comprehensive experiments on the NUS-WIDE dataset, where we show that our model outperforms state-of-the-art methods for multilabel image annotation even when our model is forced to generalize to new types of metadata.Comment: Accepted to ICCV 201