Tensors in Statistics

This article provides an overview of tensors, their properties, and their applications in statistics. Tensors, also known as multidimensional arrays, are generalizations of matrices to higher orders and are useful data representation architectures. We first review basic tensor concepts and decompositions, and then we elaborate traditional and recent applications of tensors in the fields of recommender systems and imaging analysis. We also illustrate tensors for network data and explore the relations among interacting units in a complex network system. Some canonical tensor computational algorithms and available software libraries are provided for various tensor decompositions. Future research directions, including tensors in deep learning, are also discussed. Expected final online publication date for the Annual Review of Statistics, Volume 8 is March 7, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Machine Learning for Sustainable Energy Systems

Annual Review of Environment and Resources ◽

10.1146/annurev-environ-020220-061831 ◽

2021 ◽

Vol 46 (1) ◽

Author(s):

Priya L. Donti ◽

J. Zico Kolter

Keyword(s):

Machine Learning ◽

Sustainable Energy ◽

Energy Systems ◽

Future Research ◽

Annual Review ◽

Publication Date ◽

Research Directions ◽

Sustainable Energy Systems ◽

Future Research Directions ◽

And Storage

In recent years, machine learning has proven to be a powerful tool for deriving insights from data. In this review, we describe ways in which machine learning has been leveraged to facilitate the development and operation of sustainable energy systems. We first provide a taxonomy of machine learning paradigms and techniques, along with a discussion of their strengths and limitations. We then provide an overview of existing research using machine learning for sustainable energy production, delivery, and storage. Finally, we identify gaps in this literature, propose future research directions, and discuss important considerations for deployment. Expected final online publication date for the Annual Review of Environment and Resources, Volume 46 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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The Lazy or Dishonest Respondent: Detection and Prevention

Annual Review of Organizational Psychology and Organizational Behavior ◽

10.1146/annurev-orgpsych-012420-055324 ◽

2020 ◽

Vol 8 (1) ◽

Author(s):

Winfred Arthur ◽

Ellen Hagen ◽

Felix George

Keyword(s):

Organizational Behavior ◽

Self Report ◽

Future Research ◽

Annual Review ◽

Publication Date ◽

Response Distortion ◽

High Stakes ◽

Careless Responding ◽

Practical Guidelines ◽

Future Research Directions

Self-report measures are characterized as being susceptible to threats associated with deliberate dissimulation or response distortion (i.e., social desirability responding) and careless responding. Careless responding typically arises in low-stakes settings (e.g., participating in a study for course credit) where some respondents are not motivated to respond in a conscientious manner to the items. In contrast, in high-stakes assessments (e.g., prehire assessments), because of the outcomes associated with their responses, respondents are motivated to present themselves in as favorable a light as possible and, thus, may respond dishonestly in an effort to accomplish this objective. In this article, we draw a distinction between the lazy respondent, which we associate with careless responding, and the dishonest respondent, which we associate with response distortion. We then seek to answer the following questions for both careless responding and response distortion: ( a) What is it? ( b) Why is it a problem or concern? ( c) Why do people engage in it? ( d) How pervasive is it? ( e) Can and how is it prevented or mitigated? ( f ) How is it detected? ( g) What does one do when one detects it? We conclude with a discussion of suggested future research directions and some practical guidelines for practitioners and researchers. Expected final online publication date for the Annual Review of Organizational Pscyhology and Organizational Behavior, Volume 8 is January 21, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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3D Human Motion Editing and Synthesis: A Survey

Computational and Mathematical Methods in Medicine ◽

10.1155/2014/104535 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 9

Author(s):

Xin Wang ◽

Qiudi Chen ◽

Wanliang Wang

Keyword(s):

Data Representation ◽

Human Motion ◽

Future Research ◽

Motion Capture Data ◽

Motion Synthesis ◽

Research Directions ◽

Motion Editing ◽

Comprehensive Survey ◽

Future Research Directions ◽

Human Motion Synthesis

The ways to compute the kinematics and dynamic quantities of human bodies in motion have been studied in many biomedical papers. This paper presents a comprehensive survey of 3D human motion editing and synthesis techniques. Firstly, four types of methods for 3D human motion synthesis are introduced and compared. Secondly, motion capture data representation, motion editing, and motion synthesis are reviewed successively. Finally, future research directions are suggested.

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