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.

Penalized Regressions: The Bridge versus the Lasso

Abstract: Bridge regression, a special family of penalized regressions of a penalty function Σ|βj|γ with γ ≤ 1, considered. A general approach to solve for the bridge estimator is developed. A new algorithm for the lasso (γ = 1) is obtained by studying the structure of the bridge estimators. The shrinkage parameter γ and the tuning parameter λ are selected via generalized cross-validation (GCV). Comparison between the bridge model (γ ≤ 1) and several other shrinkage models, namely the ordinary least squares regression (λ = 0), the lasso (γ = 1) and ridge regression (γ = 2), is made through a simulation study. It is shown that the bridge regression performs well compared to the lasso and ridge regression. These methods are demonstrated through an analysis of a prostate cancer data. Some computational advantages and limitations are discussed.

Graph Neural Networks for Social Recommendation

Abstract: In recent years, Graph Neural Networks (GNNs), which can naturally integrate node information and topological structure, have been demonstrated to be powerful in learning on graph data. These advantages of GNNs provide great potential to advance social recommendation since data in social recommender systems can be represented as user-user social graph and user-item graph; and learning latent factors of users and items is the key. However, building social recommender systems based on GNNs faces challenges. For example, the user-item graph encodes both interactions and their associated opinions; social relations have heterogeneous strengths; users involve in two graphs (e.g., the user-user social graph and the user-item graph). To address the three aforementioned challenges simultaneously, in this paper, we present a novel graph neural network framework (GraphRec) for social recommendations. In particular, we provide a principled approach to jointly capture interactions and opinions in the user-item graph and propose the framework GraphRec, which coherently models two graphs and heterogeneous strengths. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed framework GraphRec.

Authentic leadership and eudaemonic well-being: Understanding leader-follower outcomes

Abstract: We sought to examine the concept of authentic leadership and discuss the influences of authenticity and authentic leadership on leader and follower eudaemonic well-being, as well as examine the processes through which these influences are realized. This was accomplished in four ways. First, we provide an ontological definition of authentic leadership, rooted in two distinct yet related philosophical approaches to human well-being: hedonism and eudaemonia. Second, we develop a multi-component model of authentic leadership based on recent theoretical developments in the area of authenticity. The resulting model consists of self-awareness, unbiased processing, authentic behavior/acting and authentic relational orientation. Third, we discuss the personal antecedents (leader characteristics) of authentic leadership as well as the outcomes of authentic leadership for both leaders and followers and examine the processes linking authentic leadership to its antecedents and outcomes. Fourth, we discuss the implications of this work for authentic leadership theory and then provide some practical

Face Alignment Across Large Poses: A 3D Solution

Abstract: Face alignment, which fits a face model to an image and extracts the semantic meanings of facial pixels, has been an important topic in CV community. However, most algorithms are designed for faces in small to medium poses (below 45), lacking the ability to align faces in large poses up to 90. The challenges are three-fold: Firstly, the commonly used landmark-based face model assumes that all the landmarks are visible and is therefore not suitable for profile views. Secondly, the face appearance varies more dramatically across large poses, ranging from frontal view to profile view. Thirdly, labelling landmarks in large poses is extremely challenging since the invisible landmarks have to be guessed. In this paper, we propose a solution to the three problems in an new alignment framework, called 3D Dense Face Alignment (3DDFA), in which a dense 3D face model is fitted to the image via convolutional neutral network (CNN). We also propose a method to synthesize large-scale training samples in profile views to solve the third problem of data labelling. Experiments on the challenging AFLW database show that our approach achieves significant improvements over state-of-the-art methods.

The alternative oxidase lowers mitochondrial reactive oxygen production in plant cells

Abstract: Besides the cytochrome c pathway, plant mitochondria have an alternative respiratory pathway that is comprised of a single homodimeric protein, alternative oxidase (AOX). Transgenic cultured tobacco cells with altered levels of AOX were used to test the hypothesis that the alternative pathway in plant mitochondria functions as a mechanism to decrease the formation of reactive oxygen species (ROS) produced during respiratory electron transport. Using the ROS-sensitive probe 2′,7′-dichlorofluorescein diacetate, we found that antisense suppression of AOX resulted in cells with a significantly higher level of ROS compared with wild-type cells, whereas the overexpression of AOX resulted in cells with lower ROS abundance. Laser-scanning confocal microscopy showed that the difference in ROS abundance among wild-type and AOX transgenic cells was caused by changes in mitochondrial-specific ROS formation. Mitochondrial ROS production was exacerbated by the use of antimycin A, which inhibited normal cytochrome electron transport. In addition, cells overexpressing AOX were found to have consistently lower expression of genes encoding ROS-scavenging enzymes, including the superoxide dismutase genes SodA and SodB, as well as glutathione peroxidase. Also, the abundance of mRNAs encoding salicylic acid-binding catalase and a pathogenesis-related protein were significantly higher in cells deficient in AOX. These results are evidence that AOX plays a role in lowering mitochondrial ROS formation in plant cells.