What Development Regulatory Variables Say—or Don’t Say—About A Municipality

Little is known about how regulatory development variables reflect and define a community. This paper explores the correlation of development regulatory variables with broader community measures in 68 municipalities in the Twin Cities area of Minnesota. Coefficients of determination, correlation coefficients, principal component analysis, and factor analysis were used to compare development regulatory data with broader municipal measures. The hypothesis tested is overarching: that a municipality’s development regulations and processes correlate to general measures of community composition. The strongest and only significant correlations found were in the municipal use of tax increment financing and commercial/ industrial property values, non-residential construction activity, population, and multi-family building permit activity.

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning

We present a scheme to obtain an inexpensive and reliable estimate of the uncertainty associated with the predictions of a machine-learning model of atomic and molecular properties. The scheme is based on resampling, with multiple models being generated based on sub-sampling of the same training data. The accuracy of the uncertainty prediction can be benchmarked by maximum likelihood estimation, which can also be used to correct for correlations between resampled models, and to improve the performance of the uncertainty estimation by a cross-validation procedure. In the case of sparse Gaussian Process Regression models, this resampled estimator can be evaluated at negligible cost. We demonstrate the reliability of these estimates for the prediction of molecular energetics, and for the estimation of nuclear chemical shieldings in molecular crystals. Extension to estimate the uncertainty in energy differences, forces, or other correlated predictions is straightforward. This method can be easily applied to other machine learning schemes, and will be beneficial to make data-driven predictions more reliable, and to facilitate training-set optimization and active-learning strategies

Concept development of the U.S. Marine Corps personnel casualty reporting system

NANAhttp://archive.org/details/conceptdevelopme109452121

Theorems of Sylow Theory

Call number: LD2668 .R4 1965 M98

Discrete tomography and joint inversion for loosely connected or unconnected physical properties: application to crosshole seismic and georadar data sets

Tomographic inversions of geophysical data generally include an underdetermined component. To compensate for this shortcoming, assumptions or a priori knowledge need to be incorporated in the inversion process. A possible option for a broad class of problems is to restrict the range of values within which the unknown model parameters must lie. Typical examples of such problems include cavity detection or the delineation of isolated ore bodies in the subsurface. In cavity detection, the physical properties of the cavity can be narrowed down to those of air and/or water, and the physical properties of the host rock either are known to within a narrow band of values or can be established from simple experiments. Discrete tomography techniques allow such information to be included as constraints on the inversions. We have developed a discrete tomography method that is based on mixed-integer linear programming. An important feature of our method is the ability to invert jointly different types of data, for which the key physical properties are only loosely connected or unconnected. Joint inversions reduce the ambiguity in tomographic studies. The performance of our new algorithm is demonstrated on several synthetic data sets. In particular, we show how the complementary nature of seismic and georadar data can be exploited to locate air- or water-filled cavitie

Atomic-scale representation and statistical learning of tensorial properties

This chapter discusses the importance of incorporating three-dimensional symmetries in the context of statistical learning models geared towards the interpolation of the tensorial properties of atomic-scale structures. We focus on Gaussian process regression, and in particular on the construction of structural representations, and the associated kernel functions, that are endowed with the geometric covariance properties compatible with those of the learning targets. We summarize the general formulation of such a symmetry-adapted Gaussian process regression model, and how it can be implemented based on a scheme that generalizes the popular smooth overlap of atomic positions representation. We give examples of the performance of this framework when learning the polarizability and the ground-state electron density of a molecule

Focused Ion Beam Fabrication

Contains reports on five research projects.DARPA/Naval Electronic Systems Command (Contract MDA-903-85-C-0215)Charles Stark Draper Laboratory (Contract DL-H-261827)U.S. Navy - Office of Naval Research (Contract N00014-84-K-0073)Nippon Telephone and TelegraphHitachi Central Research Laborator

Structure-based optimization of potent, selective, and orally bioavailable CDK8 inhibitors discovered by high-throughput screening

The mediator complex-associated cyclin dependent kinase CDK8 regulates beta-catenin-dependent transcription following activation of WNT signaling. Multiple lines of evidence suggest CDK8 may act as an oncogene in the development of colorectal cancer. Here we describe the successful optimization of an imidazo-thiadiazole series of CDK8 inhibitors that was identified in a high-throughput screening campaign and further progressed by structure-based design. In several optimization cycles, we improved the microsomal stability, potency, and kinase selectivity. The initial imidazo-thiadiazole scaffold was replaced by a 3-methyl-1H-pyrazolo[3,4-b]-pyridine which resulted in compound 25 (MSC2530818) that displayed excellent kinase selectivity, biochemical and cellular potency, microsomal stability, and is orally bioavailable. Furthermore, we demonstrated modulation phospho-STAT1, a pharmacodynamic biomarker of CDK8 activity, and tumor growth inhibition in an APC mutant SW620 human colorectal carcinoma xenograft model after oral administration. Compound 25 demonstrated suitable potency and selectivity to progress into preclinical in vivo efficacy and safety studies