Particle-hole symmetric localization in optical lattices using time modulated random on-site potentials

The random hopping models exhibit many fascinating features, such as diverging localization length and density of states as energy approaches the bandcenter, due to its particle-hole symmetry. Nevertheless, such models are yet to be realized experimentally because the particle-hole symmetry is easily destroyed by diagonal disorder. Here we propose that a pure random hopping model can be effectively realized in ultracold atoms by modulating a disordered onsite potential in particular frequency ranges. This idea is motivated by the recent development of the phenomena called "dynamical localization" or "coherent destruction of tunneling". Investigating the application of this idea in one dimension, we find that if the oscillation frequency of the disorder potential is gradually increased from zero to infinity, one can tune a non-interacting system from an Anderson insulator to a random hopping model with diverging localization length at the band center, and eventually to a uniform-hopping tight-binding model.Comment: 7 pages, 5 figure

Bayesian inference in high-dimensional linear models using an empirical correlation-adaptive prior

In the context of a high-dimensional linear regression model, we propose the use of an empirical correlation-adaptive prior that makes use of information in the observed predictor variable matrix to adaptively address high collinearity, determining if parameters associated with correlated predictors should be shrunk together or kept apart. Under suitable conditions, we prove that this empirical Bayes posterior concentrates around the true sparse parameter at the optimal rate asymptotically. A simplified version of a shotgun stochastic search algorithm is employed to implement the variable selection procedure, and we show, via simulation experiments across different settings and a real-data application, the favorable performance of the proposed method compared to existing methods.Comment: 25 pages, 4 figures, 2 table

Meta-analysis of functional neuroimaging data using Bayesian nonparametric binary regression

In this work we perform a meta-analysis of neuroimaging data, consisting of locations of peak activations identified in 162 separate studies on emotion. Neuroimaging meta-analyses are typically performed using kernel-based methods. However, these methods require the width of the kernel to be set a priori and to be constant across the brain. To address these issues, we propose a fully Bayesian nonparametric binary regression method to perform neuroimaging meta-analyses. In our method, each location (or voxel) has a probability of being a peak activation, and the corresponding probability function is based on a spatially adaptive Gaussian Markov random field (GMRF). We also include parameters in the model to robustify the procedure against miscoding of the voxel response. Posterior inference is implemented using efficient MCMC algorithms extended from those introduced in Holmes and Held [Bayesian Anal. 1 (2006) 145--168]. Our method allows the probability function to be locally adaptive with respect to the covariates, that is, to be smooth in one region of the covariate space and wiggly or even discontinuous in another. Posterior miscoding probabilities for each of the identified voxels can also be obtained, identifying voxels that may have been falsely classified as being activated. Simulation studies and application to the emotion neuroimaging data indicate that our method is superior to standard kernel-based methods.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS523 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Can Contracts Replace Qualification in a Sourcing Process With Competitive Suppliers and Imperfect Information?

This paper considers a manufacturer who outsources the production of a product to multiple competing suppliers, who differ in their cost structures and in their capabilities for producing high-quality products. The manufacturer must design the sourcing process to ensure that the selected supplier has sufficient quality capability, while encouraging competition among the suppliers. We develop and analyze a mathematical model of performance-based contracting, a sourcing method that is appropriate when the manufacturer has imperfect information regarding the suppliers’ costs and capabilities. We compare the performance of performance-based contracting with that of a two-stage sourcing process, an alternative sourcing method that is more commonly used in practice. The theoretical results and managerial insights derived from this research can enable manufacturing firms to improve the management of their sourcing processes. In particular, we demonstrate that performance-based contracting with a symmetric linear penalty/reward function will always outperform the two-stage sourcing process from the perspective of the buyer and that the optimal penalty/reward rate is less than or equal to the unit warranty cost. In addition, performance-based contracting generally leads to a higher quality level provided by the winning supplier. However, the winning supplier is generally better off under the two-stage sourcing process

Dysregulation of visual motion inhibition in major depression

Individuals with depression show depleted concentrations of the inhibitory neurotransmitter GABA in occipital (visual) cortex, predicting weakened inhibition within their visual systems. Yet, visual inhibition in depression remains largely unexplored. To fill this gap, we examined the inhibitory process of centersurround suppression (CSS) of visual motion in depressed individuals. Perceptual performance in discriminating the direction of motion was measured as a function of stimulus presentation time and contrast in depressed individuals (n¼27) and controls (n¼22). CSS was operationalized as the accuracy difference between conditions using large (7.5°) and small (1.5°) grating stimuli. Both depressed and control participants displayed the expected advantage in accuracy for small stimuli at high contrast. A significant interaction emerged between subject group, contrast level and presentation time, indicating that alterations of CSS in depression were modulated by stimulus conditions. At high contrast, depressed individuals showed significantly greater CSS than controls at the 66 ms presentation time (where the effect peaked in both groups). The results' specificity and dependence on stimulus features such as contrast, size and presentation time suggest that they arise from changes in early visual processing, and are not the results of a generalized deficit or cognitive bias.Accepted versio

Effects of Domain-Specific Noise on Visual Motion Processing in Schizophrenia

Background: Visual perception impairments in schizophrenia stem from abnormal information processing. Information processing requires neural response to a stimulus (signal) against a backdrop of 1) random variation in baseline neural activity (internal noise) and sometimes irrelevant environmental stimulation (external noise). Filtering out noise is a critical aspect of information processing, and needs to be critically examined in schizophrenia. Methods: To understand how noise in the visual system constrains perceptual processing, we devised a novel paradigm to build in both signal and external noise on same visual stimulus. Here, instead of uniformed noise, domain-specific noise—variations in stimulus speed—was introduced to evaluate the performance of schizophrenia patients in speed discrimination. Each motion stimulus—a random dot pattern (RDP) comprising 200 moving dots—included a range of speeds, drawn individually from a Gaussian distribution for each dot. The task for patients (n = 26) and controls (n = 28) was to identify which of two stimuli moved faster based on their mean speeds. Findings: Patients exhibited deficient speed discrimination at baseline, in the absence of speed noise. Their speed discrimination was further degraded in the presence of low and medium levels of external noise. In the presence of a high levels of noise, degradation of patients' speed discrimination leveled-off, resulting in similar performance to controls. Conclusion: These domain-specific noise effects on speed discrimination provide direct evidence for the existence of heightened internal noise within a specific visual motion processing domain in schizophrenia