Notes

Notes by Robert J. Affeldt, Andrew V. Giorgi, Mark Harry Berens, Robert C. Enburg, James J. Haranzo, F. Richard Kramer, George J. Murphy, Jr., James F. O\u27Rieley, and Edward J. VanTassel

Frequency-Domain Measurement Method for the Analysis of ESD Generators and Coupling

A method for analyzing electrostatic discharge (ESD) generators and coupling to equipment under test in the frequency domain is proposed. In ESD generators, the pulses are excited by the voltage collapse across relay contacts. The voltage collapse is replaced by one port of a vector network analyer (VNA). All the discrete and structural elements that form the ESD current pulse and the transient fields are excited by the VNA as if they were excited by the voltage collapse. In such a way, the method allows analyzing the current and field-driven linear coupling without having to discharge an ESD generator, eliminating the risk to the circuit and allowing the use of the wider dynamic range of a network analyzer relative to a real-time oscilloscope. The method is applicable to other voltage-collapse-driven tests, such as electrical fast transient, ultrawideband susceptibility testing but requires a linear coupling path

Recent Decisions

Comments on recent decisions by Joseph C. Spalding, R. Emmett Fitzgerald, Howard G. Burke, Andrew V. Giorgi, Richard F. Welter, Edward L. Burke, Frank A. Howard, Robert C. Enburg, Carl F. Eiberger, William L. Kirchner, Jr., and William J. Hurley

Correcting Sociodemographic Selection Biases for Population Prediction from Social Media

Social media is increasingly used for large-scale population predictions, such as estimating community health statistics. However, social media users are not typically a representative sample of the intended population -- a "selection bias". Within the social sciences, such a bias is typically addressed with restratification techniques, where observations are reweighted according to how under- or over-sampled their socio-demographic groups are. Yet, restratifaction is rarely evaluated for improving prediction. Across four tasks of predicting U.S. county population health statistics from Twitter, we find standard restratification techniques provide no improvement and often degrade prediction accuracies. The core reasons for this seems to be both shrunken estimates (reduced variance of model predicted values) and sparse estimates of each population's socio-demographics. We thus develop and evaluate three methods to address these problems: estimator redistribution to account for shrinking, and adaptive binning and informed smoothing to handle sparse socio-demographic estimates. We show that each of these methods significantly outperforms the standard restratification approaches. Combining approaches, we find substantial improvements over non-restratified models, yielding a 53.0% increase in predictive accuracy (R^2) in the case of surveyed life satisfaction, and a 17.8% average increase across all tasks

Exchange-correlation functionals via local interpolation along the adiabatic connection

The construction of density-functional approximations is explored by modeling the adiabatic connection locally, using energy densities defined in terms of the electrostatic potential of the exchange−correlation hole. These local models are more amenable to the construction of size-consistent approximations than their global counterparts. In this work we use accurate input local ingredients to assess the accuracy of a range of local interpolation models against accurate exchange−correlation energy densities. The importance of the strictly correlated electrons (SCE) functional describing the strong coupling limit is emphasized, enabling the corresponding interpolated functionals to treat strong correlation effects. In addition to exploring the performance of such models numerically for the helium and beryllium isoelectronic series and the dissociation of the hydrogen molecule, an approximate analytic model is presented for the initial slope of the local adiabatic connection. Comparisons are made with approaches based on global models, and prospects for future approximations based on the local adiabatic connection are discussed

The coupling constant averaged exchange–correlation energy density

The exchange–correlation energy, central to density-functional theory, may be represented in terms of the coupling constant averaged (CCA) exchange–correlation energy density. We present an approach to calculate the CCA energy density using accurate ab initio methods and its application to simple atomic systems. This function provides a link between intrinsically non-local, many-body electronic structure methods and simple local and semi-local density-functional approximations (DFAs). The CCA energy density is resolved into separate exchange and correlation terms and the features of each compared with those of quantities commonly used to construct DFAs. In particular, the more complex structure of the correlation energy density is found to exhibit features that align well with those present in the Laplacian of the density, suggesting its role as a key variable to be used in the construction of improved semi-local correlation functionals. The accurate results presented in this work are also compared with those provided by the Laplacian-dependent Becke–Roussel model for the exchange energy