Science and Engineering Labor Force

[Excerpt] Like most developed economies, the United States increasingly depends on a technically skilled workforce, including scientists and engineers. Workers for whom knowledge and skill in S&E are central to their jobs have an effect on the economy and the wider society that is disproportionate to their numbers: they contribute to research and development, increased knowledge, technological innovation, and economic growth. Moreover, the knowledge and skills associated with science and engineering have diffused across occupations and become more important in jobs that are not traditionally associated with S&E

RAPID: A regional plan to rescue the orphaned University of Louisiana Monroe Fish Collection

Award to enable the rescue of the orphaned Fish Collection of the University of Louisiana Monroe by a consortium of other regional fish collectionsNational Science FoundationIntegrative Biolog

Women, Minorities, and Persons with Disabilities in Science and Engineering: 2015

Women, Minorities, and Persons with Disabilities in Science and Engineering provides statistical information about the participation of these three groups in science and engineering education and employment. Its primary purpose is to serve as an information source. It offers no endorsement of or recommendations about policies or programs. National Science Foundation reporting on this topic is mandated by the Science and Engineering Equal Opportunities Act (Public Law 96-516). This digest highlights key statistics drawn from a wide variety of data sources. Data and figures in this digest are organized into topical areas—enrollment, field of degree, occupation, employment status, and academic employment. Surveys conducted by the National Center for Science and Engineering Statistics (NCSES) at the National Science Foundation provided a large portion of the data used in this report. NCSES has a central role in the collection, interpretation, analysis, and dissemination of objective data on the science and engineering enterprise

Hybrid approximate message passing

Gaussian and quadratic approximations of message passing algorithms on graphs have attracted considerable recent attention due to their computational simplicity, analytic tractability, and wide applicability in optimization and statistical inference problems. This paper presents a systematic framework for incorporating such approximate message passing (AMP) methods in general graphical models. The key concept is a partition of dependencies of a general graphical model into strong and weak edges, with the weak edges representing interactions through aggregates of small, linearizable couplings of variables. AMP approximations based on the Central Limit Theorem can be readily applied to aggregates of many weak edges and integrated with standard message passing updates on the strong edges. The resulting algorithm, which we call hybrid generalized approximate message passing (HyGAMP), can yield significantly simpler implementations of sum-product and max-sum loopy belief propagation. By varying the partition of strong and weak edges, a performance--complexity trade-off can be achieved. Group sparsity and multinomial logistic regression problems are studied as examples of the proposed methodology.The work of S. Rangan was supported in part by the National Science Foundation under Grants 1116589, 1302336, and 1547332, and in part by the industrial affiliates of NYU WIRELESS. The work of A. K. Fletcher was supported in part by the National Science Foundation under Grants 1254204 and 1738286 and in part by the Office of Naval Research under Grant N00014-15-1-2677. The work of V. K. Goyal was supported in part by the National Science Foundation under Grant 1422034. The work of E. Byrne and P. Schniter was supported in part by the National Science Foundation under Grant CCF-1527162. (1116589 - National Science Foundation; 1302336 - National Science Foundation; 1547332 - National Science Foundation; 1254204 - National Science Foundation; 1738286 - National Science Foundation; 1422034 - National Science Foundation; CCF-1527162 - National Science Foundation; NYU WIRELESS; N00014-15-1-2677 - Office of Naval Research

National Science Foundation Fellow

University of Dayton first-year doctoral student Jesse Hughes \u2716 will mentor minority undergraduate students and introduce them to campus research opportunities after receiving a 2017 National Science Foundation graduate research fellowship

Designing forest biodiversity experiments : general considerations illustrated by a new large experiment in subtropical China

Funded by German Research Foundation. Grant Number: DFG FOR 891/1 and 2 National Natural Science Foundation of China. Grant Numbers: NSFC 30710103907, 30930005, 31170457 , 31210103910 Swiss National Science Foundation (SNSF) Sino-German Centre for Research Promotion in BeijingPeer reviewedPublisher PD

Author correction: Enabling controlling complex networks with local topological information

Correction to: Scientific Reports https://doi.org/10.1038/s41598-018-22655-5, published online 15 March 2018. The Acknowledgements section in this Article is incomplete.The work was partially supported by National Science Foundation of China (61603209, 61327902), and Beijing Natural Science Foundation (4164086), and the Study of Brain-Inspired Computing System of Tsinghua University program (20151080467), and SuZhou-Tsinghua innovation leading program 2016SZ0102, and Ministry of Education, Singapore, under contracts RG28/14, MOE2014-T2-1-028 and MOE2016-T2-1-119. Part of this work is an outcome of the Future Resilient Systems project at the Singapore-ETH Centre (SEC), which is funded by the National Research Foundation of Singapore (NRF) under its Campus for Research Excellence and Technological Enterprise (CREATE) program. (61603209 - National Science Foundation of China; 61327902 - National Science Foundation of China; 4164086 - Beijing Natural Science Foundation; 20151080467 - Study of Brain-Inspired Computing System of Tsinghua University program; 2016SZ0102 - SuZhou-Tsinghua innovation leading program; RG28/14 - Ministry of Education, Singapore; MOE2014-T2-1-028 - Ministry of Education, Singapore; MOE2016-T2-1-119 - Ministry of Education, Singapore; National Research Foundation of Singapore (NRF) under its Campus for Research Excellence and Technological Enterprise (CREATE) program)Published versio

Evapotranspiration estimation using Landsat-8 data with a two-layer framework

This work was partially supported by the National Natural Science Foundation of China (41401042), National Key Basic Research Program of China (973 Program) (Grant No. 2015CB452701) and National Natural Science Foundation of China (Grant Nos. 41571019 and 41371043).Peer reviewedproo