Changing preferences: an experiment and estimation of market-incentive effects on altruism

This paper studies how altruistic preferences are changed by markets and incentives. We conduct a laboratory experiment in a within-subject design. Subjects are asked to choose health care qualities for hypothetical patients in monopoly, duopoly, and quadropoly. Prices, costs, and patient benefits are experimental incentive parameters. In monopoly, subjects choose quality to tradeoff between profits and altruistic patient benefits. In duopoly and quadropoly, we model subjects playing a simultaneous-move game. Each subject is uncertain about an opponent's altruism, and competes for patients by choosing qualities. Bayes-Nash equilibria describe subjects' quality decisions as functions of altruism. Using a nonparametric method, we estimate the population altruism distributions from Bayes-Nash equilibrium qualities in different markets and incentive configurations. Markets tend to reduce altruism, although duopoly and quadropoly equilibrium qualities are much higher than those in monopoly. Although markets crowd out altruism, the disciplinary powers of market competition are stronger. Counterfactuals confirm markets change preferences.Accepted manuscrip

Nanostructured materials with highly dispersed Au–Ce0.5Zr0.5O2 nanodomains: A route to temperature stable Au catalysts?

Our strategy to inhibit Au(0) growth with temperature involves the preparation of ultrafine Au clusters that are highly dispersed and strongly interacting with a thermally stable high-surface-area substrate. Temperature-stable Au-cluster-based catalysts were successfully prepared through the controlled synthesis of 3.5 nm Ce0.5Zr0.5O2 colloidal building blocks containing tailored strongly bound Au-cluster precursors. With the objective of stabilizing these Au clusters with temperature, grain growth of Ce0.5Zr0.5O2 nanodomains was inhibited by their dispersion through Al2O3 nanodomains. High surface area Au–Ce0.5Zr0.5O2–Al2O3 nanostructured composites were thus designed highlighting the drastic effect of Au cluster dispersion on Au(0) cluster growth. High thermal stability of our Au(0)-cluster-based catalysts was shown with the surprising catalytic activity for CO conversion observed on our nanostructured materials heated to temperatures as high as 800 C for 6 h

On One-dimensional Self-assembly of Surfactant-coated Nanoparticles

Nanometer-sized metal and semiconductor particles possess novel properties. To fully realize their potential, these nanoparticles need to be fabricated into ordered arrays or predesigned structures. A promising nanoparticle fabrication method is coupled surface passivation and self-assembly of surfactant-coated nanoparticles. Due to the empirical procedure and partially satisfactory results, this method still represents a major challenge to date and its refinement can benefit from fundamental understanding. Existing evidences suggest that the self-assembly of surfactant-coated nanoparticles is induced by surfactant-modified interparticle interactions and follows an intrinsic road map such that short one-dimensional (1D) chain arrays of nanoparticles occur first as a stable intermediate before further assembly takes place to form higher dimensional close-packed superlattices. Here we report a study employing fundamental analyses and Brownian dynamics simulations to elucidate the underlying pair interaction potential that drives the nanoparticle self-assembly via 1D arrays. We find that a pair potential which has a longer-ranged repulsion and reflects the effects of surfactant chain interdigitation on the dynamics is effective in producing and stabilizing nanoparticle chain arrays. The resultant potential energy surface is isotropic for dispersed nanoparticles but becomes anisotropic to favor the growth of linear chain arrays when self-assembly starts

Optimising Spatial and Tonal Data for PDE-based Inpainting

Some recent methods for lossy signal and image compression store only a few selected pixels and fill in the missing structures by inpainting with a partial differential equation (PDE). Suitable operators include the Laplacian, the biharmonic operator, and edge-enhancing anisotropic diffusion (EED). The quality of such approaches depends substantially on the selection of the data that is kept. Optimising this data in the domain and codomain gives rise to challenging mathematical problems that shall be addressed in our work. In the 1D case, we prove results that provide insights into the difficulty of this problem, and we give evidence that a splitting into spatial and tonal (i.e. function value) optimisation does hardly deteriorate the results. In the 2D setting, we present generic algorithms that achieve a high reconstruction quality even if the specified data is very sparse. To optimise the spatial data, we use a probabilistic sparsification, followed by a nonlocal pixel exchange that avoids getting trapped in bad local optima. After this spatial optimisation we perform a tonal optimisation that modifies the function values in order to reduce the global reconstruction error. For homogeneous diffusion inpainting, this comes down to a least squares problem for which we prove that it has a unique solution. We demonstrate that it can be found efficiently with a gradient descent approach that is accelerated with fast explicit diffusion (FED) cycles. Our framework allows to specify the desired density of the inpainting mask a priori. Moreover, is more generic than other data optimisation approaches for the sparse inpainting problem, since it can also be extended to nonlinear inpainting operators such as EED. This is exploited to achieve reconstructions with state-of-the-art quality. We also give an extensive literature survey on PDE-based image compression methods

A Boolean Model of the Pseudomonas syringae hrp Regulon Predicts a Tightly Regulated System

The Type III secretion system (TTSS) is a protein secretion machinery used by certain gram-negative bacterial pathogens of plants and animals to deliver effector molecules to the host and is at the core of the ability to cause disease. Extensive molecular and biochemical study has revealed the components and their interactions within this system but reductive approaches do not consider the dynamical properties of the system as a whole. In order to gain a better understanding of these dynamical behaviours and to create a basis for the refinement of the experimentally derived knowledge we created a Boolean model of the regulatory interactions within the hrp regulon of Pseudomonas syringae pathovar tomato strain DC3000 Pseudomonas syringae. We compared simulations of the model with experimental data and found them to be largely in accordance, though the hrpV node shows some differences in state changes to that expected. Our simulations also revealed interesting dynamical properties not previously predicted. The model predicts that the hrp regulon is a biologically stable two-state system, with each of the stable states being strongly attractive, a feature indicative of selection for a tightly regulated and responsive system. The model predicts that the state of the GacS/GacA node confers control, a prediction that is consistent with experimental observations that the protein has a role as master regulator. Simulated gene “knock out” experiments with the model predict that HrpL is a central information processing point within the network