ANALYZING PEST CONTROL STRATEGIES FOR COTTON WITH AN ENVIRONMENTAL IMPACT MATRIX

Environmental Economics and Policy,

Orbital transfer vehicle concept definition and system analysis study, 1985. Volume 3: System and program trades

The key system and program trade studies performed to arrive at a preferred Orbital Transfer Vehicle (OTV) system concept and evolutionary approach to the acquisition of the requisite capabilites is documented. These efforts were expanded to encompass a Space Transportation Architecture Study (STAS) mission model and recommended unmanned cargo vehicle. The most important factors affecting the results presented are the mission model requirements and selection criteria. The reason for conducting the OTV concept definition and system analyses study is to select a concept and acquisition approach that meets a delivery requirement reflected by the mission model

Religious Identity and Economic Behavior

We randomly vary religious identity salience in laboratory subjects to test how identity salience contributes to six hypothesized links from prior literature between religious identity and economic behavior. We find that religious identity salience makes Protestants increase contributions to public goods. Catholics decrease contributions to public goods, expect others to contribute less to public goods, and become less risk averse. Jews more strongly reciprocate as an employee in a bilateral labor market gift-exchange game. We find no evidence of religious identity salience effects on disutility of work effort, discount rates, or generosity in a dictator game.

Guided Proofreading of Automatic Segmentations for Connectomics

Automatic cell image segmentation methods in connectomics produce merge and split errors, which require correction through proofreading. Previous research has identified the visual search for these errors as the bottleneck in interactive proofreading. To aid error correction, we develop two classifiers that automatically recommend candidate merges and splits to the user. These classifiers use a convolutional neural network (CNN) that has been trained with errors in automatic segmentations against expert-labeled ground truth. Our classifiers detect potentially-erroneous regions by considering a large context region around a segmentation boundary. Corrections can then be performed by a user with yes/no decisions, which reduces variation of information 7.5x faster than previous proofreading methods. We also present a fully-automatic mode that uses a probability threshold to make merge/split decisions. Extensive experiments using the automatic approach and comparing performance of novice and expert users demonstrate that our method performs favorably against state-of-the-art proofreading methods on different connectomics datasets.Comment: Supplemental material available at http://rhoana.org/guidedproofreading/supplemental.pd

Thyroid neoplasia

This issue of eMedRef provides information to clinicians on the pathophysiology, diagnosis, and therapeutics of thyroid neoplasia

FARM ADVISORY SERVICES AND PESTICIDE TOXICITY ON COTTON AND PEANUTS IN THE ALBEMARLE-PAMLICO WATERSHED

According to a Virginia-North Carolina watershed survey, farmers view advisory services as having the effect of decreasing pesticide use. However, analysis of pesticide use shows that hired staff, scouting personnel, and extension agents are associated with higher pesticide toxicity applied to cotton while chemical dealers and scouting personnel are associated with higher toxicity applied to peanuts.Crop Production/Industries, Environmental Economics and Policy,

A Parallel Solver for Graph Laplacians

Problems from graph drawing, spectral clustering, network flow and graph partitioning can all be expressed in terms of graph Laplacian matrices. There are a variety of practical approaches to solving these problems in serial. However, as problem sizes increase and single core speeds stagnate, parallelism is essential to solve such problems quickly. We present an unsmoothed aggregation multigrid method for solving graph Laplacians in a distributed memory setting. We introduce new parallel aggregation and low degree elimination algorithms targeted specifically at irregular degree graphs. These algorithms are expressed in terms of sparse matrix-vector products using generalized sum and product operations. This formulation is amenable to linear algebra using arbitrary distributions and allows us to operate on a 2D sparse matrix distribution, which is necessary for parallel scalability. Our solver outperforms the natural parallel extension of the current state of the art in an algorithmic comparison. We demonstrate scalability to 576 processes and graphs with up to 1.7 billion edges.Comment: PASC '18, Code: https://github.com/ligmg/ligm