Michigan State University

About: Michigan State University is a education organization based out in East Lansing, Michigan, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 60109 authors who have published 137074 publications receiving 5633022 citations. The organization is also known as: MSU & Michigan State.

Dimensionality reduction using genetic algorithms

Abstract: Pattern recognition generally requires that objects be described in terms of a set of measurable features. The selection and quality of the features representing each pattern affect the success of subsequent classification. Feature extraction is the process of deriving new features from original features to reduce the cost of feature measurement, increase classifier efficiency, and allow higher accuracy. Many feature extraction techniques involve linear transformations of the original pattern vectors to new vectors of lower dimensionality. While this is useful for data visualization and classification efficiency, it does not necessarily reduce the number of features to be measured since each new feature may be a linear combination of all of the features in the original pattern vector. Here, we present a new approach to feature extraction in which feature selection and extraction and classifier training are performed simultaneously using a genetic algorithm. The genetic algorithm optimizes a feature weight vector used to scale the individual features in the original pattern vectors. A masking vector is also employed for simultaneous selection of a feature subset. We employ this technique in combination with the k nearest neighbor classification rule, and compare the results with classical feature selection and extraction techniques, including sequential floating forward feature selection, and linear discriminant analysis. We also present results for the identification of favorable water-binding sites on protein surfaces.

Specific Oxidative Cleavage of Carotenoids by VP14 of Maize

Abstract: The plant growth regulator abscisic acid (ABA) is formed by the oxidative cleavage of an epoxy-carotenoid. The synthesis of other apocarotenoids, such as vitamin A in animals, may occur by a similar mechanism. In ABA biosynthesis, oxidative cleavage is the first committed reaction and is believed to be the key regulatory step. A new ABA-deficient mutant of maize has been identified and the corresponding gene, Vp14 , has been cloned. The recombinant VP14 protein catalyzes the cleavage of 9- cis -epoxy-carotenoids to form C 25 apo-aldehydes and xanthoxin, a precursor of ABA in higher plants.

Earnings Management to Avoid Earnings Declines across Publicly and Privately Held Banks

Abstract: This study compares samples of publicly and privately held bank holding companies to examine whether the high frequency of small earnings increases relative to small earnings decreases reported by public firms is attributable to earnings management. We expect public banks' shareholders to be more likely than private banks' shareholders to rely on simple earnings‐based heuristics in evaluating firm performance, so we expect public banks to have more incentives to report steadily increasing earnings. Consistent with this expectation, we find that relative to private banks, public banks: (1) report fewer small earnings declines, (2) are more likely to use the loan loss provision and security gain realizations to eliminate small earnings decreases, and (3) report longer strings of consecutive earnings increases. These results suggest that the asymmetric pattern of more small earnings increases than decreases, first documented by Burgstahler and Dichev (1997), is attributable to earnings management and is not ...

User Acceptance Enablers in Individual Decision Making About Technology: Toward an Integrated Model

Abstract: Building on recent unique, yet potentially complementary, approaches to understanding the formation of user perceptions about technology (Venkatesh, 1999; Venkatesh & Speier, 1999), the present work reanalyzes the data from both studies to develop an integrated model of technology acceptance. The integrated model specifically examines the influence of pre-training and training environment interventions (termed user acceptance enablers) to understand how user perceptions are formed prior to system implementation. The model is then further extended and tested using longitudinal data in a field setting. The results indicate that the integrated model emerged as a better predictor of user behavior when compared to the existing models.

The jina reaclib database: its recent updates and impact on type-i x-ray bursts

Abstract: We present results from the JINA REACLIB project, an ongoing effort to maintain a current and accurate library of thermonuclear reaction rates for astrophysical applications. Ongoing updates are transparently documented and version tracked, and any set of rates is publicly available and can be downloaded via a Web interface at http://groups.nscl.msu.edu/jina/reaclib/db/. We discuss here our library V1.0, a snapshot of recommended rates for stable and explosive hydrogen and helium burning. We show that the updated reaction rates lead to modest but significant changes in full network, one-dimensional X-ray burst model calculations, compared with calculations with previously used reaction rate sets. The late time behavior of X-ray burst light curves shows significant changes, suggesting that the previously found small discrepancies between model calculations and observations may be solved with a better understanding of the nuclear input. Our X-ray burst model calculations are intended to serve as a benchmark for future model comparisons and sensitivity studies, as the complete underlying nuclear physics is fully documented and publicly available.