Heat conductivity from energy-density fluctuations.

We present a method, based on the classical Green-Kubo theory of linear response, to compute the heat conductivity of extended systems, leveraging energy-density, rather than energy-current, fluctuations, thus avoiding the need to devise an analytical expression for the macroscopic energy flux. The implementation of this method requires the evaluation of the long-wavelength and low-frequency limits of a suitably defined correlation function, which we perform using a combination of recently-introduced cepstral-analysis and Bayesian extrapolation techniques. Our methodology is demonstrated against standard current-based Green-Kubo results for liquid argon and water, and solid amorphous Silica, and compared with a recently proposed similar technique, which utilizes mass-density, instead of energy-density, fluctuations.

Reconstructing Nonparametric Productivity Networks.

Network models provide a general representation of inter-connected system dynamics. This ability to connect systems has led to a proliferation of network models for economic productivity analysis, primarily estimated non-parametrically using Data Envelopment Analysis (DEA). While network DEA models can be used to measure system performance, they lack a statistical framework for inference, due in part to the complex structure of network processes. We fill this gap by developing a general framework to infer the network structure in a Bayesian sense, in order to better understand the underlying relationships driving system performance. Our approach draws on recent advances in information science, machine learning and statistical inference from the physics of complex systems to estimate unobserved network linkages. To illustrate, we apply our framework to analyze the production of knowledge, via own and cross-disciplinary research, for a world-country panel of bibliometric data. We find significant interactions between related disciplinary research output, both in terms of quantity and quality. In the context of research productivity, our results on cross-disciplinary linkages could be used to better target research funding across disciplines and institutions. More generally, our framework for inferring the underlying network production technology could be applied to both public and private settings which entail spillovers, including intra- and inter-firm managerial decisions and public agency coordination. This framework also provides a systematic approach to model selection when the underlying network structure is unknown.

Bi ordered phases on Cu(100): periodic arrays of dislocations influence the electronic properties.

A single layer of bismuth deposited on the Cu(100) surface forms long range ordered structural phases at various Bi density. A highly ordered c(2 x 2) reconstruction is accomplished at 0.5 ML, further Bi deposition induces a c(9square root of 2 x square root of 2)R45 degrees structure and a subsequent p(10 x 10) phase related to the formation of regular dislocations arrays. The transition from a c(2 x 2) superstructure to the c(9square root of 2 x square root of 2)R45 degrees phase is accompanied by a sudden decrease in the work function. Photoemission measurements reveal that the Bi induced states close to the Fermi level, associated to the c(2 x 2) phase, are strongly quenched when the arrays of dislocations are formed, while at higher binding energies, they undergo an energy shift probably due to a confinement effect. The low-energy single particle excitations and the electron dispersion of the Bi induced states of the c(2 x 2) phase are compared to the electronic states deduced by theoretical band structure obtained by ab initio calculation performed within the embedding method applied to a realistic semi-infinite system.