Satellite monitoring of vegetation and geology in semi-arid environments

The possibility of mapping various characteristics of the natural environment of Tanzania by various LANDSAT techniques was assessed. Interpretation and mapping were carried out using black and white as well as color infrared images on the scale of 1:250,000. The advantages of several computer techniques were also assessed, including contrast-stretched rationing, differential edge enhancement; supervised classification; multitemporal classification; and change detection. Results Show the most useful image for interpretation comes from band 5, with additional information being obtained from either band 6 or band 7. The advantages of using color infrared images for interpreting vegetation and geology are so great that black and white should be used only to supplement the colored images

Estimating individual treatment effect: generalization bounds and algorithms

There is intense interest in applying machine learning to problems of causal inference in fields such as healthcare, economics and education. In particular, individual-level causal inference has important applications such as precision medicine. We give a new theoretical analysis and family of algorithms for predicting individual treatment effect (ITE) from observational data, under the assumption known as strong ignorability. The algorithms learn a "balanced" representation such that the induced treated and control distributions look similar. We give a novel, simple and intuitive generalization-error bound showing that the expected ITE estimation error of a representation is bounded by a sum of the standard generalization-error of that representation and the distance between the treated and control distributions induced by the representation. We use Integral Probability Metrics to measure distances between distributions, deriving explicit bounds for the Wasserstein and Maximum Mean Discrepancy (MMD) distances. Experiments on real and simulated data show the new algorithms match or outperform the state-of-the-art.Comment: Added name "TARNet" to refer to version with alpha = 0. Removed sup

A Survey on Graph Kernels

Graph kernels have become an established and widely-used technique for solving classification tasks on graphs. This survey gives a comprehensive overview of techniques for kernel-based graph classification developed in the past 15 years. We describe and categorize graph kernels based on properties inherent to their design, such as the nature of their extracted graph features, their method of computation and their applicability to problems in practice. In an extensive experimental evaluation, we study the classification accuracy of a large suite of graph kernels on established benchmarks as well as new datasets. We compare the performance of popular kernels with several baseline methods and study the effect of applying a Gaussian RBF kernel to the metric induced by a graph kernel. In doing so, we find that simple baselines become competitive after this transformation on some datasets. Moreover, we study the extent to which existing graph kernels agree in their predictions (and prediction errors) and obtain a data-driven categorization of kernels as result. Finally, based on our experimental results, we derive a practitioner's guide to kernel-based graph classification

The Inflection Point of the Speed-Density Relation and the Social Force Model

It has been argued that the speed-density digram of pedestrian movement has an inflection point. This inflection point was found empirically in investigations of closed-loop single-file pedestrian movement. The reduced complexity of single-file movement does not only allow a higher precision for the evaluation of empirical data, but it occasionally also allows analytical considerations for micosimulation models. In this way it will be shown that certain (common) variants of the Social Force Model (SFM) do not produce an inflection point in the speed-density diagram if infinitely many pedestrians contribute to the force computed for one pedestrian. We propose a modified Social Force Model that produces the inflection point.Comment: accepted for presentation at conference Traffic and Granular Flow 201

A Carrot-and-Stick Approach to Environmental Improvement: Marrying Agri-Environmental Payments and Water Quality Regulations

Agri-environmental programs, such as the Environmental Quality Incentives Program, provide payments to livestock and crop producers to generate broadly defined environmental benefits and to help them comply with federal water quality regulations, such as those that require manure nutrients generated on large animal feeding operations to be spread on cropland at no greater than agronomic rates. We couch these policy options in terms of agri-environmental "carrots" and regulatory "sticks," respectively. The U.S. agricultural sector is likely to respond to these policies in a variety of ways. Simulation analysis suggests that meeting nutrient standards would result in decreased levels of animal production, increased prices for livestock and poultry products, increased levels of crop production, and water quality improvements. However, estimated impacts are not homogeneous across regions. In regions with relatively less cropland per ton of manure produced, the impacts of these policies are more pronounced.Environmental Economics and Policy,

WHEN THE !%$? HITS THE LAND: IMPLICATIONS FOR US AGRICULTURE AND ENVIRONMENT WHEN LAND APPLICATION OF MANURE IS CONSTRAINED

Confined animal production in the U.S. and its associated discharge of manure nutrients into area waters is considered a leading contributor to current water quality impairments. A common option to mitigate these impairments is to limit land application of manure. This paper evaluates the implications of alternative land application constraints for U.S. agriculture and the environment at the regional and sector level. The results suggest that when these constraints are particularly binding, due to minimal acceptance of manure as a substitute for commercial fertilizer, potentially large and unanticipated changes in returns to agricultural production and water quality may occur. Furthermore, we find that some of the cost of meeting the land application constraints will be passed on to consumers through higher prices and to a portion of rural economies through lower production rates and labor expenditures.Environmental Economics and Policy, Livestock Production/Industries,

The Method of Fundamental Solutions for Direct Cavity Problems in EIT

The Method of Fundamental Solutions (MFS) is an effective technique for solving linear elliptic partial differential equations, such as the Laplace and Helmholtz equation. It is a form of indirect boundary integral equation method and a technique that uses boundary collocation or boundary fitting. In this paper the MFS is implemented to solve A numerically an inverse problem which consists of finding an unknown cavity within a region of interest based on given boundary Cauchy data. A range of examples are used to demonstrate that the technique is very effective at locating cavities in two-dimensional geometries for exact input data. The technique is then developed to include a regularisation parameter that enables cavities to be located accurately and stably even for noisy input data