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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Statistical quality control and improvement of waste water treatment plant 20

Statistical quality control and improvement of waste water t...

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20th World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2016

作者： Akarapu, Vikranth Gunkala, Sairam Pattigadapa, Sahitya Pattigadappa, Bharani Krishna Ponnapalli, Srikar Segall, Richard S. Arkansas State University College of Engineering Engineering Management Program State University AR72467 United States Arkansas State University College of Business Department of Computer and Information Technology State University AR72467 United States

ISBN: (纸本)9781941763438

This research studies five characteristics of water quality using techniques of statistical quality control as applied to actual 2014 data collected for a water treatment plant located in United States. An overview of some of the results obtained using Minitab 17 are presented as well as conclusions and future directions of the research.

关键词： Water treatment plants

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Rainfall forecasting using PSPline and rice production with ocean-atmosphere interaction

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IOP Conference Series: Earth and Environmental Science 2018年第1期195卷

作者： Rezzy Eko Caraka Budi Darmawan Supatmanto Muhammad Tahmid Joko Soebagyo M Ali Mauludin Akbar Iskandar Bens Pardamean Bioinformatics and Data Science Research Center (BDSRC) Bina Nusantara University Jakarta Indonesia 11480 Weather Modification Technology Center Agency for the Assessment and Application of Technology (BPPT Indonesia Agency for Meteorology Climatology and Geophysics Indonesia College of Technology Wastukancana Purwakarta Laboratory of Sociology and Extension Padjadjaran University Informatics Engineering STMIK AKBA Makassar Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

The role of climate can be affected by plants. The weather can accelerate and multiply the existence of various plant pests and diseases, accelerate the growth and development of grass among plants, and encourage the emergence of infection and significant damage to plants. The elements of climate that affect the growth of plants are one of them is rainfall. In this paper, we performed the simulation using the non-parametric penalized spline (PSPLINE) method and studied the effect on rice production in Lampung. It can be concluded that the increasing fluctuation, frequency, and intensity of climate anomalies in the last decade caused by the ENSO phenomenon have an impact on changes in distribution patterns, intensity, and period of the wet season so that the start of the rainy season and the dry season becomes too late. As a result, there is a seasonal shift from normal average conditions that can ultimately have severe implications for food crops. In a nutshell penalized spline gives high accuracy with R 2 = 96.227% and MAPE = 1.62%.

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Optimal CMB lensing reconstruction and parameter estimation with SPTpol data

arXiv

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arXiv 2020年

作者： Millea, Marius Daley, C.M. Chou, T.-L. Anderes, E. Ade, P.A.R. Anderson, A.J. Austermann, J.E. Avva, J.S. Beall, J.A. Bender, A.N. Benson, B.A. Bianchini, F. Bleem, L.E. Carlstrom, J.E. Chang, C.L. Chaubal, P. Chiang, H.C. Citron, R. Corbett Moran, C. Crawford, T.M. Crites, A.T. de Haan, T. Dobbs, M.A. Everett, W. Gallicchio, J. George, E.M. Goeckner-Wald, N. Guns, S. Gupta, N. Halverson, N.W. Henning, J.W. Hilton, G.C. Holder, G.P. Holzapfel, W.L. Hrubes, J.D. Huang, N. Hubmayr, J. Irwin, K.D. Knox, L. Lee, A.T. Li, D. Lowitz, A. McMahon, J.J. Meyer, S.S. Mocanu, L.M. Montgomery, J. Natoli, T. Nibarger, J.P. Noble, G. Novosad, V. Omori, Y. Padin, S. Patil, S. Pryke, C. Reichardt, C.L. Ruhl, J.E. Saliwanchik, B.R. Schaffer, K.K. Sievers, C. Smecher, G. Stark, A.A. Thorne, B. Tucker, C. Veach, T. Vieira, J.D. Wang, G. Whitehorn, N. Wu, W.L.K. Yefremenko, V. Department of Physics University of California BerkeleyCA94720 United States Astronomy Department University of Illinois at Urbana-Champaign 1002 W. Green Street UrbanaIL61801 United States Kavli Institute for Cosmological Physics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Department of Physics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Department of Statistics University of California One Shields Avenue DavisCA95616 United States Cardiff University CardiffCF10 3XQ United Kingdom Fermi National Accelerator Laboratory MS209 P.O. Box 500 BataviaIL60510 United States NIST Quantum Devices Group 325 Broadway Mailcode 817.03 BoulderCO80305 United States Department of Physics University of Colorado BoulderCO80309 United States High Energy Physics Division Argonne National Laboratory 9700 S. Cass Avenue ArgonneIL60439 United States Department of Astronomy and Astrophysics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States School of Physics University of Melbourne ParkvilleVIC3010 Australia Enrico Fermi Institute University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Department of Physics McGill University 3600 Rue University MontrealQCH3A 2T8 Canada School of Mathematics Statistics & Computer Science University of KwaZulu-Natal Durban South Africa University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Jet Propulsion Laboratory PasadenaCA91109 United States Dunlap Institute for Astronomy & Astrophysics University of Toronto 50 St George St TorontoONM5S 3H4 Canada Department of Astronomy & Astrophysics University of Toronto 50 St George St TorontoONM5S 3H4 Canada Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Canadian Institute for Advanced Research CIFAR Program in Gravity Extreme Universe TorontoONM5G 1Z8 Canada Department of Astrophysical and Pla

We perform the first simultaneous Bayesian parameter inference and optimal reconstruction of the gravitational lensing of the cosmic microwave background (CMB), using 100 deg2 of polarization observations from the SPTpol receiver on the South Pole Telescope. These data reach noise levels as low as 5.8 µK-arcmin in polarization, which are low enough that the typically used quadratic estimator (QE) technique for analyzing CMB lensing is significantly sub-optimal. Conversely, the Bayesian procedure extracts all lensing information from the data and is optimal at any noise level. We infer the amplitude of the gravitational lensing potential to be Aφ = 0.949 ±0.122 using the Bayesian pipeline, consistent with our QE pipeline result, but with 17% smaller error bars. The Bayesian analysis also provides a simple way to account for systematic uncertainties, performing a similar job as frequentist "bias hardening," and reducing the systematic uncertainty on Aφ due to polarization calibration from almost half of the statistical error to effectively zero. Finally, we jointly constrain Aφ along with AL, the amplitude of lensing-like effects on the CMB power spectra, demonstrating that the Bayesian method can be used to easily infer parameters both from an optimal lensing reconstruction and from the delensed CMB, while exactly accounting for the correlation between the two. These results demonstrate the feasibility of the Bayesian approach on real data, and pave the way for future analysis of deep CMB polarization measurements with SPT-3G, Simons Observatory, and CMB-S4, where improvements relative to the QE can reach 1.5 times tighter constraints on Aφ and 7 times lower effective lensing reconstruction noise. Copyright © 2020, The Authors. All rights reserved.

关键词： Cosmology

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Genetic variation of somaclonal mutants from the 8th generation of Pekalongan accession rodent tuber (typhonium flagelliforme lodd.) based on RAPD-PCR analysis

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IOP Conference Series: Earth and Environmental Science 2018年第1期195卷

作者： Ragapadmi Purnamaningsih Nesti Fronika Sianipar Khoirunnisa Assidqi Harco Leslie Hendric Spits Warnars Indonesia Center for Agricultural Biotechnology and Genetic Resources Research and Development (BB-Biogen) Bogor 16111 Indonesia Food Technology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia 11480 Research Interest Group Food Biotechnology Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Rodent tuber is a potential plant which can be developed as an anticancer drug. The rodent tuber plant has a low anticancer compound which does not have an economic value to be utilized on an industrial scale, particularly to explore as an anticancer drug. The material plant was used 30 clones of Rodent Tuber Pekalongan to increase its bioactive compounds using a combination of in vitro culture with gamma-ray irradiation. The selection process has been done until the 8th generation and obtained 14 somaclonal mutants that have higher bioactive compounds than control plant (non-mutant). This study aimed to analyze the genetic variation of 14 somaclonal mutants of rodent tuber using RAPD with 14 primers. The results were produced 513 total bands with the size of 200-5000 bp and 380 polymorphic bands between somaclonal mutants. OPB-18 primer produced the most polymorphic bands and five specific polymorphic bands of 1336 bp, 1070 bp, 901 bp, 861 bp and 728 bp in the somaclonal mutant M23 (20-4-2-1-1-1). OPD-20 primer produced seven specific polymorphic bands of 1574 bp, 1557 bp, 1501 bp, 1496 bp, 1234 bp, 1229 bp and 1086 bp in the M23 somaclonal mutant. The phylogenetic analysis showed that there were two main groups with the coefficient similarity between 0.77 and 0.83. The highest genetic variation obtained was found in the M23 somaclonal mutant with a genetic difference of 23% compared with control. There are five somaclonal mutants (M24, M22, M21, M16, M14) that have a genetic variation of up to 22% over the control. This study shows that OPB-18 and OPD-20 primers were efficient in detecting somaclonal mutant variants.

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A demonstration of improved constraints on primordial gravitational waves with delensing

arXiv

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arXiv 2020年

作者： Ade, P.A.R. Ahmed, Z. Amiri, M. Anderson, A.J. Austermann, J.E. Avva, J.S. Barkats, D. Thakur, R. Basu Beall, J.A. Bender, A.N. Benson, B.A. Bianchini, F. Bischoff, C.A. Bleem, L.E. Bock, J.J. Boenish, H. Bullock, E. Buza, V. Carlstrom, J.E. Chang, C.L. Cheshire, J.R. Chiang, H.C. Chou, T.-L. Citron, R. Connors, J. Moran, C. Corbett Cornelison, J. Crawford, T.M. Crites, A.T. Crumrine, M. Cukierman, A. de Haan, T. Dierickx, M. Dobbs, M.A. Duband, L. Everett, W. Fatigoni, S. Filippini, J.P. Fliescher, S. Gallicchio, J. George, E.M. Germaine, T.St. Goeckner-Wald, N. Goldfinger, D.C. Grayson, J. Gupta, N. Hall, G. Halpern, M. Halverson, N.W. Harrison, S. Henderson, S. Henning, J.W. Hildebrandt, S.R. Hilton, G.C. Holder, G.P. Holzapfel, W.L. Hrubes, J.D. Huang, N. Hubmayr, J. Hui, H. Irwin, K.D. Kang, J. Karkare, K.S. Karpel, E. Kefeli, S. Kernasovskiy, S.A. Knox, L. Kovac, J.M. Kuo, C.L. Lau, K. Lee, A.T. Leitch, E.M. Li, D. Lowitz, A. Manzotti, A. McMahon, J.J. Megerian, K.G. Meyer, S.S. Millea, M. Mocanu, L.M. Moncelsi, L. Montgomery, J. Nadolski, A. Namikawa, T. Natoli, T. Netterfield, C.B. Nguyen, H.T. Nibarger, J.P. Noble, G. Novosad, V. O’Brient, R. Ogburn, R.W. Omori, Y. Padin, S. Palladino, S. Patil, S. Prouve, T. Pryke, C. Racine, B. Reichardt, C.L. Reintsema, C.D. Richter, S. Ruhl, J.E. Saliwanchik, B.R. Schaffer, K.K. Schillaci, A. Schmitt, B.L. Schwarz, R. Sheehy, C.D. Sievers, C. Smecher, G. Soliman, A. Stark, A.A. Steinbach, B. Sudiwala, R.V. Teply, G.P. Thompson, K.L. Tolan, J.E. Tucker, C. Turner, A.D. Umiltà, C. Veach, T. Vieira, J.D. Vieregg, A.G. Wandui, A. Wang, G. Weber, A.C. Whitehorn, N. Wiebe, D.V. Willmert, J. Wong, C.L. Wu, W.L. Kimmy Yang, H. Yefremenko, V. Yoon, K.W. Young, E. Yu, C. Zeng, L. Zhang, C. Cardiff University CardiffCF10 3XQ United Kingdom SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo ParkCA94025 United States Kavli Institute for Particle Astrophysics and Cosmology Stanford University 452 Lomita Mall StanfordCA94305 United States Department of Physics and Astronomy University of British Columbia VancouverBCV6T 1Z1 Canada Fermi National Accelerator Laboratory MS209 P.O. Box 500 BataviaIL60510 United States NIST Quantum Devices Group 325 Broadway Mailcode 817.03 BoulderCO80305 United States Department of Physics University of Colorado BoulderCO80309 United States Department of Physics University of California BerkeleyCA94720 United States Harvard-Smithsonian Center for Astrophysics 60 Garden Street CambridgeMA02138 United States California Institute of Technology MS 249-17 1216 E. California Blvd. PasadenaCA91125 United States High Energy Physics Division Argonne National Laboratory 9700 S. Cass Avenue ArgonneIL60439 United States Kavli Institute for Cosmological Physics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Department of Astronomy and Astrophysics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States School of Physics University of Melbourne ParkvilleVIC3010 Australia Department of Physics University of Cincinnati CincinnatiOH45221 United States Jet Propulsion Laboratory PasadenaCA91109 United States Department of Physics Harvard University CambridgeMA02138 United States Minnesota Institute for Astrophysics University of Minnesota MinneapolisMN55455 United States Department of Physics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Enrico Fermi Institute University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Department of Physics McGill University 3600 Rue University MontrealQCH3A 2T8 Canada School of Mathematics Statistics & Computer Science University of KwaZulu-Natal

We present a constraint on the tensor-to-scalar ratio, r, derived from measurements of cosmic microwave background (CMB) polarization B-modes with "delensing," whereby the uncertainty on r contributed by the sample variance of the gravitational lensing B-modes is reduced by cross-correlating against a lensing B-mode template. This template is constructed by combining an estimate of the polarized CMB with a tracer of the projected large-scale structure. The large-scale-structure tracer used is a map of the cosmic infrared background derived from Planck satellite data, while the polarized CMB map comes from a combination of South Pole Telescope, Bicep/Keck, and Planck data. We expand the Bicep/Keck likelihood analysis framework to accept a lensing template and apply it to the Bicep/Keck data set collected through 2014 using the same parametric foreground modelling as in the previous analysis. From simulations, we find that the uncertainty on r is reduced by ∼ 10%, from σ(r)= 0.024 to 0.022, which can be compared with a ∼ 26% reduction obtained when using a perfect lensing template or if there were zero lensing B-modes. Applying the technique to the real data, the constraint on r is improved from r0.05 Copyright © 2020, The Authors. All rights reserved.

关键词： Cosmology

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Exploring low internal reorganization energies for silicene nanoclusters

arXiv

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arXiv 2017年

作者： Pablo-Pedro, Ricardo Lopez-Rios, Hector Mendoza-Cortes, Jose-L Kong, Jing Fomine, Serguei van Voorhis, Troy Dresselhaus, Mildred S. Department of Chemistry Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Instituto de Investigaciones en Materiales Universidad Nacional Autónoma de México Apartado Postal 70-360 CU Coyoacán Ciudad de México04510 Mexico Department of Chemical & Biomedical Engineering Florida A&M University Florida State University Joint College of Engineering Tallahassee FL32310 United States Scientific Computing Department Materials Science and Engineering Program High Performance Material Institute Condensed Matter Theory National High Magnetic Field Laboratory Florida State University TallahasseeFL32310 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

High-performance materials rely on small reorganization energies to facilitate both charge separation and charge transport. Here, we performed DFT calculations to predict small reorganization energies of rectangular silicene nanoclusters with hydrogen-passivated edges denoted by H-SiNC. We observe that across all geometries, H-SiNCs feature large electron affinities and highly stabilized anionic states, indicating their potential as n-type materials. Our findings suggest that fine-tuning the size of H-SiNCs along the "zigzag" and "armchair" directions may permit the design of novel n-type electronic materials and spinctronics devices that incorporate both high electron affinities and very low internal reorganization energies. Copyright © 2017, The Authors. All rights reserved.

关键词： Silicene

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Supporting student attainment and management of competencies in a trans-disciplinary degree program 123

Supporting student attainment and management of competencies...

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123rd ASEE Annual Conference and Exposition

作者： Van Epps, Amy S. Ashby, Iryna Gray, Colin M. Exter, Marisa Purdue University Department of Library Science and Engineering Librarian West Lafayette United States Purdue University Learning Design and Technology Program West Lafayette United States Purdue University Department of Computer Graphics Technology West Lafayette United States Department of Learning Design and Technology College of Education Purdue University West Lafayette United States

In Fall 2014, a large Midwestern land-grant research university piloted a competency-based model as the foundation for an undergraduate transdisciplinary program focusing on connecting engineering and technology with humanities and social sciences. Students enrolled in this program progress through a set of competencies that require them to master cross-disciplinary and cross-functional skills needed to be successful in a 21stcentury workplace. Now in its second year, the competency-based program has undergone significant changes that include a more substantial definition of competencies at each of the three levels of competence (developing, emerging, and proficient), scaffolding needed to support students on their path towards gaining competencies, and significant mentoring by faculty, TAs, and professional advisers to support competency attainment. In this paper, we will share challenges and discoveries made by the faculty throughout the first two years of the novel Competency-Based Education (CBE) experience, including a reflection on how such experiences impacted modifications of the CBE model from Year 1 to Year 2, the ways in which the program supported individual attainment and management of competencies by students, and the value of the mentorship program in supporting student-driven learning paths. We will also share insights into students' perceptions of the benefits, challenges, and frustrations of being part of this pilot program based on interview and survey data provided by the 33 members of the initial cohort. This overview of the ways this program supported students in attaining competencies through coursework, individual mentoring, and scaffolding may be instructive as institutions seek to bring CBE to scale and increase holistic student learning for the 21stcentury. © American Society for engineering Education, 2016.

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Exploring Low Internal Reorganization Energies for Silicene Nanoclusters

arXiv

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arXiv 2017年

关键词： Silicene

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Distributed multi-robot coordination for dynamic perimeter surveillance in uncertain environments

Distributed multi-robot coordination for dynamic perimeter s...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Alexander Jahn Reza Javanmard Alitappeh David Saldaña Luciano C. A. Pimenta Andre G. Santos Mario F. M. Campos Graduate Program in Electrical Engineering (PPGEE) National Institute of Science and Technology for Cooperative Autonomous Systems Applied to Security and Environment Departamento de Informatica Universidade Federal de Vicosa Computer Science Department Universidade Federal de Minas Gerais Universidade Federal de Minas Gerais Belo Horizonte MG BR

In this work, multiple robots circulate around the boundary of a desired region in order to create a virtual fence. The aim of the this fence is to avoid internal or external agents crossing through the delimited area. In this paper, we propose a distributed technique that allows a team of robots to plan the deformation of the boundary shape in order to escort the safe region from one place to a goal. Our proposal is composed of two parts. First, we present a distributed planning method for the dynamic boundary. We model the resulting plan as a twice differentiable function. Second, we use the obtained function to guide the robot team, where every member uses only local information for the controller. The robots distribute themselves along the time-varying perimeter and patrol around it. We show in simulation how the robots behave in partially/totally unknown environments with static obstacles.

关键词： Shape Robot kinematics Trajectory Planning Robot sensing systems Dynamics

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