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Student perceptions of faculty support: Do class size or institution type matter? 123

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Student perceptions of faculty support: Do class size or ins...

123rd ASEE Annual Conference and Exposition

作者： Allendoerfer, Cheryl Wilson, Denise Plett, Melani Bates, Rebecca A. Smith, Tamara Floyd Veilleux, Nanette M. University of Washington College of Engineering United States University of Washington Department of Electrical Engineering Seattle United States Seattle Pacific University United States Minnesota State University Department of Integrated Engineering Program Mankato United States Tuskegee University Department of Chemical Engineering United States Simmons College Computer Science and Informatics Program BostonMA United States

In the context of Tinto's Model of Institutional Departure, faculty support is critical to academic integration, which in turn is an essential element of persistence in college. Thus, understanding students' perceptions of faculty support in varied settings can provide insight into student satisfaction, performance, and persistence in engineering programs. In this study, focus groups were conducted with 175 students from five different institutions and a variety of engineering and computer science majors. The institutions in this study included a small women's college, a historically black university, a small private university, a medium size teaching university, and a large public research university. Focus group transcripts were coded and analyzed qualitatively. Findings from this analysis show that in some settings, despite a perceived lack of formal, in-class faculty support, students are still able to find faculty support outside the classroom, while in other settings this informal support is also perceived as lacking. In general, smaller environments with smaller student-to-faculty ratios appear to support stronger perceptions of support, although through different pathways. A small class size does not guarantee that students will perceive greater faculty support in that class. Large class sizes are not automatically detrimental for students, but they can be if students in those classes feel a lack of faculty support or availability both in and out of the classroom. With regard to institution type, the notion that undergraduates perceive lower levels of faculty support at research institutions holds true. This study has not verified whether the level of faculty support is actually less at research institutions, but only that students' self-reported perceptions of that support were lower at the research-focused institution when compared to teaching-focused institutions in this study. Regardless, given the importance of faculty support in persistence and other a

关键词： Class size engineering Faculty support Faculty-student interactions Institution type

Finding the needle in high-dimensional haystack: A tutorial on canonical correlation analysis

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arXiv 2018年

作者： Wang, Hao-Ting Smallwood, Jonathan Mourao-Miranda, Janaina Xia, Cedric Huchuan Satterthwaite, Theodore D. Bassett, Danielle S. Bzdok, Danilo Department of Psychology University of York Heslington YorkYO10 5DD United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Max Planck University College London Centre for Computational Psychiatry and Ageing Research University College London London United Kingdom Department of Psychiatry Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States Department of Bioengineering University of Pennsylvania PhiladelphiaPA19104 United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States Department of Neurology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States Department of Physics & Astronomy School of Arts & Sciences University of Pennsylvania PhiladelphiaPA19104 United States Department of Psychiatry Psychotherapy and Psychosomatics RWTH Aachen University Germany JARA-BRAIN Jülich-Aachen Research Alliance Germany Parietal Team INRIA Neurospin CEA Saclay Bat 145 Gif-sur-Yvette91191 France

Since the beginning of the 21st century, the size, breadth, and granularity of data in biology and medicine has grown rapidly. In the example of neuroscience, studies with thousands of subjects are becoming more common, which provide extensive phenotyping on the behavioral, neural, and genomic level with hundreds of variables. The complexity of such "big data" repositories offer new opportunities and pose new challenges to investigate brain, cognition, and disease. Canonical correlation analysis (CCA) is a prototypical family of methods for wrestling with and harvesting insight from such rich datasets. This doubly-multivariate tool can simultaneously consider two variable sets from different modalities to uncover essential hidden associations. Our primer discusses the rationale, promises, and pitfalls of CCA in biomedicine. Copyright © 2018, The Authors. All rights reserved.

关键词： Correlation methods

Applying the MVC EA-IRPM to reporting-guidelines in medicine: A strategy that is a web system

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Applying the MVC EA-IRPM to reporting-guidelines in medicine...

IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Patrick Letouze J. I. M. de Souza Valéria Martins Silva Glaubos Climaco F. B. Chagas Joao Y. Ishihara Faculty of Computer Science Universidade Federal do Tocantins Palmas Brazil Universidade Federal do Tocantins Palmas TO BR Graduate Program of Systems' Engineering and Computation Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil Department of Informatics Universidade Federal do Maranh/ ao - UFMA Faculty of Computer Science Faculdade Pitagoras Imperatriz Imperatriz Brazil Faculty of Electrical Engineering Universidade de Brasília Brasília Brazil

ISBN: (纸本)9781509041800

Reporting-Guidelines in Medicine play an important role in promoting the quality of reports in health-related research. For instance, a poorly reported research may induce misinterpretation and inappropriate clinical settings. Basically, three major issues regarding Reporting-Guidelines in Medicine has to be addressed: (1) the existence of lots of reporting guidelines covering a broad spectrum of medical research increases the difficulties when reporting a medical research because it may not be clear which guidelines are applicable, or if all related guidelines are considered; (2) most guideline developers have not described how their guidance was developed, consequently tracking guidelines and features for evaluation, or even for correcting, may be not possible; (3) few guideline developers have evaluated the effectiveness of their guidance, then the real value that the guideline brings to reporting the research is not assured. Hence, we propose a solution to these three issues based on the Model-View-Controller Evolutionary Acquisition Interdisciplinary Research Project Management (MVC EA-IRPM) that is simultaneously a strategy and a web system for the development and management of reporting guidelines.

关键词： Guidelines Project management Planning Adaptation models Phase measurement Systems architecture Systematics

Architecture Exploration for Energy-Efficient Embedded Vision Applications: From General Purpose Processor to Domain Specific Accelerator

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Architecture Exploration for Energy-Efficient Embedded Visio...

IEEE computer Society Annual Symposium on VLSI

作者： Maria Malik Farnoud Farahmand Paul Otto Nima Akhlaghi Tinoosh Mohsenin Siddhartha Sikdar Houman Homayoun Department of Electrical and Computer Engineering George Mason University Fairfax County Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Department of Bioengineering George Mason University Fairfax County

ISBN: (纸本)9781467390408

OpenCV applications are computationally intensive tasks among computer vision algorithms. The demand for low power yet high performance real-time processing of OpenCV embedded vision applications have led to developing their customized implementations on state-of-the-art embedded processing platforms. Given the industry move to heterogeneous platforms which integrates single core or multicore CPU with on-chip FPGA accelerators and GPU accelerators, the question of what platform and what implementation, whether hardware or software, is best suited for energy-efficient processing of this class of applications is becoming important. In this paper, we seek to answer this question through a detailed hardware and software implementation of OpenCV applications and methodically measurement and comprehensive analysis of their power and performance on state-of-the-art heterogeneous embedded processing platforms. The results show that in addition to application behavior, the size of image is an important factor in deciding the efficient platform in terms of highest energy-efficiency (EDP) among hardware accelerators on FPGA and software accelerators on GPU and multicore CPUs. While hardware implementation on ZYNQ shown to be the most performance and energy-efficient for image size of 500x500 or less, software GPU implementation found to be the most efficient and achieves highest speedup for larger image sizes. In addition, while for compute intensive vision applications the gap between FPGA, CPU and GPU reduces as the size of image increases, for non-intensive applications, a large performance and EDP gap is observed between the studied platforms, as the size of the image increases.

关键词： Graphics processing units Field programmable gate arrays Hardware Multicore processing Filtering algorithms

electrical tuning of polarizaion-state using graphene-integrated metasurfaces

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arXiv 2017年

作者： Dabidian, Nima Dutta-Gupta, Shourya Kholmanov, Iskandar Belkin, Mikhail Shvets, Gennady Department of Physics Center for Nano and Molecular Science and Technology University of Texas at Austin AustinTX78712 United States Department of Electrical and Computer Engineering Microelectronics Research Center University of Texas at Austin 10100 Burnet Road AustinTX78758 United States Department of Mechanical Engineering and Materials Science Program University of Texas at Austin AustinTX78712 United States CNR-INO Sensor Lab University of Brescia via Branze 45 Brescia25123 Italy

Plasmonic metasurfaces have been employed for tuning and controlling light enabling various novel applications. Their appeal is enhanced with the incorporation of an active element with the metasurfaces paving the way for dynamic control. In this letter, we realize a dynamic polarization state generator using graphene-integrated anisotropic metasurface (GIAM), where a linear incidence polarization is controllably converted into an elliptical one. The anisotropic metasurface leads to an intrinsic polarization conversion when illuminated with non-orthogonal incident polarization. Additionally, the single-layer graphene allows us to tune the phase and intensity of the reflected light on the application of a gate voltage, enabling dynamic polarization control. The stokes polarization parameters of the reflected light are measured using rotating polarizer method and it is demonstrated that a large change in the ellipticity as well as orientation angle can be induced by this device. We also provide experimental evidence that the titl angle can change independent of the ellipticity going from positive values to nearly zero to negative values while ellipticity is constant. Copyright © 2017, The Authors. All rights reserved.

关键词： Graphene

Building a concept inventory for numerical methods: A chronology 123

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Building a concept inventory for numerical methods: A chrono...

123rd ASEE Annual Conference and Exposition

作者： Kaw, Autar K. Lou, Yingyan Scott, Andrew Miller, Ronald L. University of South Florida United States Arizona State University Civil Environmental and Sustainable Engineering Program School of Sustainable Engineering and The Built Environment Engineering United States Alabama A and M University Department of Electrical Engineering and Computer Science Huntsville United States Colorado School of Mines Department of Chemical Engineering United States

Phase recovery and holographic image reconstruction using deep learning in neural networks

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arXiv 2017年

作者： Rivenson, Yair Zhang, Yibo Günaydın, Harun Teng, Da Ozcan, Aydogan Electrical Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States Computer Science Department University of California Los AngelesCA90095 United States Department of Surgery David Geffen School of Medicine University of California Los AngelesCA90095 United States

Phase recovery from intensity-only measurements forms the heart of coherent imaging techniques and holography. Here we demonstrate that a neural network can learn to perform phase recovery and holographic image reconstruction after appropriate training. This deep learning-based approach provides an entirely new framework to conduct holographic imaging by rapidly eliminating twin-image and self-interference related spatial artifacts. Compared to existing approaches, this neural network based method is significantly faster to compute, and reconstructs improved phase and amplitude images of the objects using only one hologram, i.e., requires less number of measurements in addition to being computationally faster. We validated this method by reconstructing phase and amplitude images of various samples, including blood and Pap smears, and tissue sections. These results are broadly applicable to any phase recovery problem, and highlight that through machine learning challenging problems in imaging science can be overcome, providing new avenues to design powerful computational imaging systems.68T01, 68T05, 68U10, 62M45, 78M32, 92C55, 94A08 Copyright © 2017, The Authors. All rights reserved.

关键词： Image reconstruction

A repeating fast radio burst source localised to a nearby spiral galaxy

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arXiv 2020年

作者： Marcote, B. Nimmo, K. Hessels, J.W.T. Tendulkar, S.P. Bassa, C.G. Paragi, Z. Keimpema, A. Bhardwaj, M. Karuppusamy, R. Kaspi, V.M. Law, C.J. Michilli, D. Aggarwal, K. Andersen, B. Archibald, A.M. Bandura, K. Bower, G.C. Boyle, P.J. Brar, C. Burke-Spolaor, S. Butler, B.J. Cassanelli, T. Chawla, P. Demorest, P. Dobbs, M. Fonseca, E. Giri, U. Good, D.C. Gourdji, K. Josephy, A. Kirichenko, A.Yu. Kirsten, F. Landecker, T.L. Lang, D. Lazio, T.J.W. Li, D.Z. Lin, H.-H. Linford, J.D. Masui, K. Mena-Parra, J. Naidu, A. Ng, C. Patel, C. Pen, U.-L. Pleunis, Z. Rafiei-Ravandi, M. Rahman, M. Renard, A. Scholz, P. Siegel, S.R. Smith, K.M. Stairs, I.H. Vanderlinde, K. Zwaniga, A.V. Oude Hoogeveensedijk 4 PD Dwingeloo7991 Netherlands ASTRON Netherlands Institute for Radio Astronomy Oude Hoogeveensedijk 4 PD Dwingeloo7991 Netherlands Anton Pannekoek Institute for Astronomy University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands Department of Physics McGill University 3600 University Street MontréalQCH3A 2T8 Canada McGill Space Institute McGill University 3550 University Street MontréalQCH3A 2A7 Canada Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany Department of Astronomy and Owens Valley Radio Observatory California Institute of Technology PasadenaCA91125 United States Department of Physics and Astronomy West Virginia University P.O. Box 6315 MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States School of Mathematics Physics and Statistics University of Newcastle Newcatle upon TyneNE1 7RU United Kingdom Lane Department of Computer Science and Electrical Engineering MorgantownWV26506 United States Academia Sinica Institute of Astronomy and Astrophysics 645 N. A’ohoku Place HiloHI96720 United States National Radio Astronomy Observatory SocorroNM87801 United States Dunlap Institute for Astronomy & Astrophysics University of Toronto 50 St. George Street TorontoONM5S 3H4 Canada Department of Astronomy & Astrophysics University of Toronto 50 St. George Street TorontoONM5S 3H4 Canada Perimeter Institute for Theoretical Physics WaterlooONN2L 2Y5 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of British Columbia 6224 Agricultural Road VancouverBCV6T 1Z1 Canada Instituto de Astronomía Universidad Nacional Autónoma de México Apdo. Postal 877 EnsenadaBaja California22800 Mexico Ioffe Institute 26 Politekhnicheskaya st. St. Petersburg194021 Russia Department o

Fast radio bursts (FRBs) are brief, bright, extragalactic radio flashes1, 2. Their physical origin remains unknown, but dozens of possible models have been postulated3. Some FRB sources exhibit repeat bursts4–7. Though over a hundred FRB sources have been discovered to date8, only four have been localised and associated with a host galaxy9–12, with just one of the four known to repeat9. The properties of the host galaxies, and the local environments of FRBs, provide important clues about their physical origins. However, the first known repeating FRB has been localised to a low-metallicity, irregular dwarf galaxy, and the apparently non-repeating sources to higher-metallicity, massive elliptical or star-forming galaxies, suggesting that perhaps the repeating and apparently non-repeating sources could have distinct physical origins. Here we report the precise localisation of a second repeating FRB source6, FRB 180916.J0158+65, to a star-forming region in a nearby (redshift z = 0.0337 ±0.0002) massive spiral galaxy, whose properties and proximity distinguish it from all known hosts. The lack of both a comparably luminous persistent radio counterpart and a high Faraday rotation measure6 further distinguish the local environment of FRB 180916.J0158+65 from that of the one previously localised repeating FRB source, FRB 121102. This demonstrates that repeating FRBs have a wide range of luminosities, and originate from diverse host galaxies and local environments. Copyright © 2020, The Authors. All rights reserved.

关键词： Galaxies

A Distributed and Privatized Framework for Drug-Target Interaction Prediction

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A Distributed and Privatized Framework for Drug-Target Inter...

IEEE International Conference on Bioinformatics and Biomedicine

作者： Chao Lan Sai Nivedita Chandrasekaran Jun Huan Department of Electrical Engineering and Computer Science University of Kansas Lawrence 66049 USA Department of Bioengineering University of Kansas Lawrence 66049 USA

ISBN: (纸本)9781509016129

Drug-Target interaction prediction has significantly sped up the process of drug discovery. However, existing studies focused on a centralized analytic framework, which may have many limitations when applying on the modern distributed data environments. In this paper, we present a distributed and privatized framework for drug-target interaction prediction, which assumes distributed repositories are stored on and analyzed by local computers which could exchange privatized information for improving their own prediction performance. Under this framework, we propose a 'conquer-and-divide' style prediction approach based on the recently introduced matrix factorization technique, and demonstrated its effectiveness on real-world cheminformatics data sets.

关键词： computers Drugs Distributed databases Privacy Data privacy Compounds Aggregates distributed databases Data privacy computers Privacy Pharmaceutical Preparations Drug Discovery compounds Distribute Frameworks matrix decomposition Set Theory aggregates Aggregates Aggregates