Thermal modeling of Anchorage driveway culvert with addition of insulation to prevent frost heaving

A predominate problem in cold regions, and specifically in Anchorage, Alaska, is frost heaving pavement above culverts in residential driveways. The culvert increases heat loss in the subgrade materials during winter months and allows the soils below the culvert to freeze, which is not an issue if the underlying soils are non-frost susceptible material. However, there are numerous locations in Anchorage and other parts of Alaska where the underlying soils are frost susceptible which result in frost heaving culverts under driveways that cause damaged pavement and culvert inverts that are too high. The seasonal heave and settlement of culverts under driveways accelerates pavement deterioration. A model of this scenario was developed and several insulation configurations were considered to determine a suitable alternative for preventing pavement damage from heaving culverts. The model used material properties for typical Anchorage area silty sand. The model showed that insulation could be used below culverts to prevent differential frost heave at the culvert. In addition, this technique uses typical construction materials and is reasonable for a typical residential dwelling contractor to complete during the construction of the home.Title Page / Abstract / Table of Contents / List of Figures / List of Tables / Introduction / Literature Review / Driveway Pavement Section / Driveway Pavement Section Design Method / Driveway Pavement Section / Thermal Analysis / TEMP/W (GeoStudio 2012) / Model Configuration / Model Materials and Boundary Conditions / Analysis Procedure / Results / Steady State Model and Temperature Gradient / Thermal Analysis with Pavement and Culvert without Insulation / Thermal Analysis with Pavement, Culvert, and Insulation / Discussion / Conclusions / Recommendations / References / Appendi

Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science

As the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning---pipeline design. We implement an open source Tree-based Pipeline Optimization Tool (TPOT) in Python and demonstrate its effectiveness on a series of simulated and real-world benchmark data sets. In particular, we show that TPOT can design machine learning pipelines that provide a significant improvement over a basic machine learning analysis while requiring little to no input nor prior knowledge from the user. We also address the tendency for TPOT to design overly complex pipelines by integrating Pareto optimization, which produces compact pipelines without sacrificing classification accuracy. As such, this work represents an important step toward fully automating machine learning pipeline design.Comment: 8 pages, 5 figures, preprint to appear in GECCO 2016, edits not yet made from reviewer comment

The Inverse Shapley Value Problem

For $f$ a weighted voting scheme used by $n$ voters to choose between two candidates, the $n$ \emph{Shapley-Shubik Indices} (or {\em Shapley values}) of $f$ provide a measure of how much control each voter can exert over the overall outcome of the vote. Shapley-Shubik indices were introduced by Lloyd Shapley and Martin Shubik in 1954 \cite{SS54} and are widely studied in social choice theory as a measure of the "influence" of voters. The \emph{Inverse Shapley Value Problem} is the problem of designing a weighted voting scheme which (approximately) achieves a desired input vector of values for the Shapley-Shubik indices. Despite much interest in this problem no provably correct and efficient algorithm was known prior to our work. We give the first efficient algorithm with provable performance guarantees for the Inverse Shapley Value Problem. For any constant \eps > 0 our algorithm runs in fixed poly$(n)$ time (the degree of the polynomial is independent of \eps) and has the following performance guarantee: given as input a vector of desired Shapley values, if any "reasonable" weighted voting scheme (roughly, one in which the threshold is not too skewed) approximately matches the desired vector of values to within some small error, then our algorithm explicitly outputs a weighted voting scheme that achieves this vector of Shapley values to within error \eps. If there is a "reasonable" voting scheme in which all voting weights are integers at most \poly(n) that approximately achieves the desired Shapley values, then our algorithm runs in time \poly(n) and outputs a weighted voting scheme that achieves the target vector of Shapley values to within error $\eps=n^{-1/8}.

Media outlets and their moguls: why concentrated individual or family ownership is bad for editorial independence

This article investigates the levels of owner influence in 211 different print and broadcast outlets in 32 different European media markets. Drawing on the literature from industrial organisation, it sets out reasons why we should expect greater levels of influence where ownership of individual outlets is concentrated; where it is concentrated in the hands of individuals or families; and where ownership groups own multiple outlets in the same media market. Conversely, we should expect lower levels of influence where ownership is dispersed between transnational companies. The articles uses original data on the ownership structures of these outlets, and combines it with reliable expert judgments as to the level of owner influence in each of the outlets. These hypotheses are tested and confirmed in a multilevel regression model of owner influence. The findings are relevant for policy on ownership limits in the media, and for the debate over transnational versus local control of media

A Dispersion Operator for Geometric Semantic Genetic Programming

Recent advances in geometric semantic genetic programming (GSGP) have shown that the results obtained by these methods can outperform those obtained by classical genetic programming algorithms, in particular in the context of symbolic regression. However, there are still many open issues on how to improve their search mechanism. One of these issues is how to get around the fact that the GSGP crossover operator cannot generate solutions that are placed outside the convex hull formed by the individuals of the current population. Although the mutation operator alleviates this problem, we cannot guarantee it will find promising regions of the search space within feasible computational time. In this direction, this paper proposes a new geometric dispersion operator that uses multiplicative factors to move individuals to less dense areas of the search space around the target solution before applying semantic genetic operators. Experiments in sixteen datasets show that the results obtained by the proposed operator are statistically significantly better than those produced by GSGP and that the operator does indeed spread the solutions around the target solution

Optimal transport on supply-demand networks

Previously, transport networks are usually treated as homogeneous networks, that is, every node has the same function, simultaneously providing and requiring resources. However, some real networks, such as power grid and supply chain networks, show a far different scenario in which the nodes are classified into two categories: the supply nodes provide some kinds of services, while the demand nodes require them. In this paper, we propose a general transport model for those supply-demand networks, associated with a criterion to quantify their transport capacities. In a supply-demand network with heterogenous degree distribution, its transport capacity strongly depends on the locations of supply nodes. We therefore design a simulated annealing algorithm to find the optimal configuration of supply nodes, which remarkably enhances the transport capacity, and outperforms the degree target algorithm, the betweenness target algorithm, and the greedy method. This work provides a start point for systematically analyzing and optimizing transport dynamics on supply-demand networks.Comment: 5 pages, 1 table and 4 figure