On the "Poisson Trick" and its Extensions for Fitting Multinomial Regression Models

This article is concerned with the fitting of multinomial regression models using the so-called "Poisson Trick". The work is motivated by Chen & Kuo (2001) and Malchow-M{\o}ller & Svarer (2003) which have been criticized for being computationally inefficient and sometimes producing nonsense results. We first discuss the case of independent data and offer a parsimonious fitting strategy when all covariates are categorical. We then propose a new approach for modelling correlated responses based on an extension of the Gamma-Poisson model, where the likelihood can be expressed in closed-form. The parameters are estimated via an Expectation/Conditional Maximization (ECM) algorithm, which can be implemented using functions for fitting generalized linear models readily available in standard statistical software packages. Compared to existing methods, our approach avoids the need to approximate the intractable integrals and thus the inference is exact with respect to the approximating Gamma-Poisson model. The proposed method is illustrated via a reanalysis of the yogurt data discussed by Chen & Kuo (2001)

Predicting On-axis Rotorcraft Dynamic Responses Using Machine Learning Techniques

Physical-law-based models are widely utilized in the aerospace industry. One such use is to provide flight dynamics models for use in flight simulators. For human-in-the-loop use, such simulators must run in real-time. Owing to the complex physics of rotorcraft flight, to meet this real-time requirement, simplifications to the underlying physics sometimes have to be applied to the model, leading to errors in the model's predictions of the real vehicle's response. This study investigated whether a machine-learning technique could be employed to provide rotorcraft dynamic response predictions. Machine learning was facilitated using a Gaussian process (GP) nonlinear autoregressive model, which predicted the on-axis pitch rate, roll rate, yaw rate, and heave responses of a Bo105 rotorcraft. A variational sparse GP model was then developed to reduce the computational cost of implementing the approach on large datasets. It was found that both of the GP models were able to provide accurate on-axis response predictions, particularly when the model input contained all four control inceptors and one lagged on-axis response term. The predictions made showed improvement compared to a corresponding physics-based model. The reduction of training data to one-third (rotational axes) or one-half (heave axis) resulted in only minor degradation of the sparse GP model predictions. response predictions. Machine learning was facilitated using a Gaussian process (GP) nonlinear autoregressive model, which predicted the on-axis pitch rate, roll rate, yaw rate, and heave responses of a Bo105 rotorcraft. A variational sparse GP model was then developed to reduce the computational cost of implementing the approach on large datasets. It was found that both of the GP models were able to provide accurate on-axis response predictions, particularly when the model input contained all four control inceptors and one lagged on-axis response term. The predictions made showed improvement compared to a corresponding physics-based model. The reduction of training data to one-third (rotational axes) or one-half (heave axis) resulted in only minor degradation of the sparse GP model predictions.</jats:p

Visitor Attitudes Toward and Support for Invasive Species Management at Cumberland Island National Seashore

Abstract Controlling invasive species has become a management priority on public lands across America. Although managers recognize the need for policies that minimize the impact of non-indigenous organisms, the extent to which the public supports these policies remains largely unknown. This study used an on-site intercept survey of visitors to Cumberland Island National Seashore, GA, (N = 1,166) to assess public attitudes toward invasive species. Based on responses, visitors were grouped into two main attitude categories: absolute ecocentric individuals (who believed all living things had a right to coexist) and adaptive ecocentric individuals (who acknowledged that some degree of control may be necessary to restore ecosystem integrity). Despite conflicting perspectives, members of both groups agreed that adaptive on-site management was the most acceptable and least controversial method of invasive species control. Future studies could build on this approach to identify stakeholder characteristics that help to predict invasive species management preferences

Structural and Financial Characteristics of U.S. Farms: 2001 Family Farm Report

Family farms vary widely in size and other characteristics, ranging from very small retirement and residential farms to establishments with sales in the millions of dollars. The farm typology developed by the Economic Research Service (ERS) categorizes farms into groups based primarily on occupation of the operator and sales class of the farm. The typology groups reflect operators' expectations from farming, position in the life cycle, and dependence on agriculture. The groups differ in their importance to the farm sector, product specialization, program participation, and dependence on farm income. These (and other) differences are discussed in this report.Agricultural Resource Management Study (ARMS), family farms, farm businesses, farm financial situation, farm operator household income, farm operators, farm structure, farm typology, female farm operators, government payments, spouses of farm operators, taxes, Agricultural Finance, Farm Management,

Characterization of radiotherapy component impact on MR imaging quality for an MRgRT system

Radiotherapy components of an magnetic resonnace-guided radiotherapy (MRgRT) system can alter the magnetic fields, causing spatial distortion and image deformation, altering imaging and radiation isocenter coincidence and the accuracy of dose calculations. This work presents a characterization of radiotherapy component impact on MR imaging quality in terms of imaging isocenter variation and spatial integrity changes on a 0.35T MRgRT system, pre- and postupgrade of the system. The impact of gantry position, MLC field size, and treatment table power state on imaging isocenter and spatial integrity were investigated. A spatial integrity phantom was used for all tests. Images were acquired for gantry angles 0-330° at 30° increments to assess the impact of gantry position. For MLC and table power state tests all images were acquired at the home gantry position (330°). MLC field sizes ranged from 1.66 to 27.4 cm edge length square fields. Imaging isocenter shift caused by gantry position was reduced from 1.7 mm at gantry 150° preupgrade to 0.9 mm at gantry 120° postupgrade. Maximum spatial integrity errors were 0.5 mm or less pre- and postupgrade for all gantry angles, MLC field sizes, and treatment table power states. However, when the treatment table was powered on, there was significant reduction in SNR. This study showed that gantry position can impact imaging isocenter, but spatial integrity errors were not dependent on gantry position, MLC field size, or treatment table power state. Significant isocenter variation, while reduced postupgrade, is cause for further investigation

The Gridded Retarding Ion Drift Sensor for the PetitSat CubeSat Mission

The Gridded Retarding Ion Drift Sensor (GRIDS) is a small sensor that will fly on the 6 U petitSat CubeSat. It is designed to measure the three-dimensional plasma drift velocity vector in the Earth’s ionosphere. The GRIDS also supplies information about the ion temperature, ion density, and the ratio of light to heavy ions present in the ionospheric plasma. It utilizes well-proven techniques that have been successfully validated by similar instruments on larger satellite missions while meeting CubeSat-compatible requirements for low mass, size, and power consumption. GRIDS performs the functions of a Retarding Potential Analyzer (RPA) and an Ion Drift Meter (IDM) by combining the features of both types of instruments in a single package. The sensor alternates RPA and IDM measurements to produce the full set of measurement parameters listed above. On the petitSat mission, GRIDS will help identify and characterize a phenomenon known as plasma blobs (or enhancements)